The effect of IL-6 on the level of N-myc downstream ... · Silencing of tumor suppressor genes is...
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The effect of IL-6 on the level of N-myc downstream regulated gene 2 and the growth
and survival of colon cancer cells Master thesis in molecular biology & medical biology
Louise Dannemann Jørgensen
Department of Science, Systems, and Models
Roskilde University
Supervisor Anders Blomkild Lorentzen
Title: The effect of IL-6 on the level of N-myc downstream regulated gene 2 and the growth and survival of colon cancer cells
Author: Louise Dannemann Jørgensen
Institution: Roskilde University, Denmark
Supervisor:
Anders Blomkild Lorentzen, Substitute Assistant Professor, Ph.D.
Department of Science, Systems and Models, Roskilde University, Denmark
Date of submission: May 31, 2014
Preface
This master thesis has been conducted over a time period from December 2012 to June 2014 at
the Department of Science, Nature, Systems and Models, Roskilde University.
I wish to express my deep and sincere gratitude to my supervisor, Anders Blomkild Lorentzen, for
his endless support, patience, and guidance throughout this master thesis.
I would like to thank Kirsten Olesen for her valuable help, support, and advice in the laboratory.
Further, I would like to address a special greeting to my friend, Annette Weber, for always
supporting me and believing in me throughout this project.
Abstract
Cancer is a leading cause of death, and tumorigenesis is dependent on different mechanisms,
including genomic changes and the immune system. Silencing of tumor suppressor genes is an
important mechanism. The recently suggested tumor suppressor gene, NDRG2, has been
correlated with down-regulation and decrease proliferation in cancers. Additionally, chronic
inflammation and cancer development have been connected. Oncogenic transformation occurs in
tumor cells exposed to repeated chronic inflammation, which may lead to epigenetic alterations
and changed expression of tumor suppressor genes. The immune system supplies the tumor
environment with cytokines including IL-6, which has been connected with chronic inflammation
through its ability to activate STAT3 leading to inhibition of anti-tumor immunity. NDRG2
expression has been reported to modulate SOCS3 and STAT3 activity and further induce SOCS1
expression, which leads to down-regulation of STAT3 in breast cancer. It would therefore be
interesting to study a possible connection between NDRG2 expression and IL-6 levels in colon
cancer cells.
The objective of this thesis was to study the expression of NDRG2 and examine a possible
correlation between IL-6 and NDRG2 expression in colon cancer cell lines, SW480 and HCT116, by
use of RT-qPCR and western blotting. Furthermore, distinct growth assays were performed to
evaluate the effect on the growth rate in colon cancer cells of presence of IL-6 and NDRG2,
separately and together.
The expression of NDRG2 in colon cancer cells was found to be down-regulated both on mRNA
and protein level, and the same was observed after treatment with IL-6 on mRNA level. The
growth assays provided results indicating that SW480 cells transfected with the plasmid pcDNA6-
NDRG2L-V5 had an increased growth rate, when compared with normal SW480 cells, and the
same tendency was seen after treatment with IL-6, both in normal SW480 cells and transfected
SW480 cells.
Altogether, these results suggest that NDRG2 is down-regulated in both colon cancer cell lines,
and that transfected cells treated with IL-6 show increased growth rate. This may indicate a
potential correlation between NDRG2 and IL-6 in relation to growth in colon cancer cells.
Resume
Cancer er en af de hyppigste dødsårsager, og udviklingen af tumorer er afhængig af forskellige
mekanismer, inklusiv genom-ændringer og immunsystemet. Inhibition af tumor suppressor gener
er en vigtig mekanisme, og den nyligt foreslåede tumor suppressor kandidat, NDRG2, er blevet set
nedreguleret i mange forskellige cancer typer og forbundet med nedsat vækst hos cancer celler.
Endvidere er kronisk inflammation og cancer udvikling blevet forbundet gennem transformation af
tumor celler, der har været udsat for gentagen kronisk inflammation, hvilket har medført
epigenetiske ændringer og ændring i udtrykket af tumor suppressor gener. Immunsystemet
supplerer tumormiljøet med cytokiner, herunder IL-6, som er blevet forbundet med kronisk
inflammation gennem dets evne til at aktivere STAT3 og medføre inhibition af anti-tumor respons.
NDRG2 ekspression har vist sig at medføre modulation af SOCS3 og STAT3s aktivitet, og derudover
induceres ekspressionen af SOCS1, hvilket medfører en nedregulering af STAT3 i bryst cancer.
Derfor vil det være yderst interessant at undersøge, om der er en mulig forbindelse mellem
NDRG2 ekspression og IL-6 niveau i colon cancer celler.
Formålet med dette speciale var at undersøge ekspressionen af NDRG2 på mRNA og protein
niveau og undersøge om en mulig sammenhæng mellem IL-6 og NDRG2 ekspression er til stede i
cancer cellelinierne SW480 og HCT116 ved at bruge metoder som RT-qPCR og western blot.
Derudover blev vækstforsøg udført for at undersøge om IL-6 og NDRG2, adskilt og sammen,
påvirker væksten af SW480 celler.
En nedregulering af NDRG2 ekspression på både mRNA og protein niveau blev fundet i cancer
celler, og det samme blev observeret på mRNA niveau efter behandling med IL-6. Resultaterne af
de udførte vækstforsøg viste, at SW480 cellers, transfekteret med plasmid pcDNA6-NDRG2L-V5,
vækst er øget i forhold til normale SW480 celler og den samme tendens kunne ses efter
behandling med IL-6 i både normale og transfekteret SW480 celler. Disse resultater indikerer, at
NDRG2 er nedreguleret i begge cancer cellelinier, og at transfekterede SW480 celler behandlet
med IL-6 viser en øget vækst, hvilket kunne indikere en potentiel forbindelse mellem NDRG2 og IL-
6 i forhold til væksten af colon cancer celler.
Table of Contents
1. Introduction 1
1.1 Cancer 1
1.2 The hallmarks of cancer 1 1.2.1 Sustaining proliferative signaling 2 1.2.2 Evading growth suppressors 2 1.2.3 Resisting cell death 3 1.2.4 Enabling replicative immortality 4 1.2.5 Inducing angiogenesis 5 1.2.6 Activating invasion and metastasis 6
1.3 Enabling characteristics and emerging hallmarks in cancer 9 1.3.1 Genome instability and mutations 9 1.3.2 Genetic modifications in the genome of cancer cells 10 1.3.3 Epigenetics 11 1.3.4 Epigenetic changes in normal cells 12 1.3.5 Epigenetic modifications and cancer 14 1.3.6 N-Myc downstream-regulated family of genes 16 1.3.7 NDRG2 17 1.3.8 NDRG2 & Cancer 19
1.4 Avoiding of immune destruction 21 1.4.1 Immune system 21 1.4.2 Innate immunity 22 1.4.3 Recognition of pathogens by the innate immune system 23 1.4.4 The interplay between the innate immune system and the adaptive immune system 23 1.4.5 Adaptive immunity 24 1.4.6 B cell and T cell function and activation 24 1.4.7 The Adaptive immunity is able to respond in two different ways 26 1.4.8 Cancer immunology 27 1.4.9 Immunoediting: from immune surveillance to immune escape 28 1.4.10 From elimination to escape 28 1.4.11 Cancer cells’ most common strategies to avoid the immunity 29
1.5 Tumor-promoting inflammation 33 1.5.1 Inflammation and cancer 33 1.5.2 Function of IL-6 and its role in cancer 36 1.5.3 Interleukin 6 and NDRG2 37
1.6 Short summary 38
2. The aim of the Master’s thesis 39
3. Materials & Methods 40
3.1 Experimental Design 40
3.2 Cells Culture - growth, harvesting, and treatment 41 3.2.1 Treatment of Cells 41
3.3 Quantification of NDRG2 mRNA level 43 3.3.1 RNA extraction 43 3.3.2 cDNA synthesis 44 3.3.3 Quantitative real time polymerase chain reaction (qRT-PCR) 45 3.3.4 Standard curve 46
3.4 Detection of NDRG2 protein expression 47 3.4.1 Transfection 47 3.4.2 Whole cell protein extraction 48 3.4.3 Western Blot 48 3.4.4 Protein measurement 49 3.4.5 Separation of proteins by gel electrophoresis 49 3.4.6 Transfer of proteins by electro-blotting 50 3.4.7 Antigen binding 51 3.4.8 Protein detection 51
3.5 Quantification of cell proliferation under varied conditions 52 3.5.1 Growth assay for transfected cancer cell line SW480 52 3.5.2 Growth assay for transfected cancer cell line SW480 treated with IL-6 52
3.6 Statistic 53
4. Results 54
4.1 NDRG2 expression in cancer cell lines SW480 and HCT116 at the mRNA level 54
4.2 NDRG2 expression in cancer cell lines SW480 and HCT116 at the protein level 55
4.3 Analysis of the most efficient concentration and time for the treatment with IL-6 57
4.4 NDRG2 expression in treated cancer cell lines SW480 and HCT116 at mRNA level 58
4.5 Analysis of transfection efficiency 60
4.6 Analysis of NDRG2 protein level after transfection 60
4.7 Growth assays comparing cancer cell lines SW480 growth rate +/- NDRG2 62
4.8 Growth assays comparing growth rates for SW480 cells untreated or treated with IL-6 63
4.9 Growth assay comparing transfected cancer cell line SW480 treated and untreated with IL-6 64
4.10 Short summary 66
5. Discussion 67
5.1 The demonstration of the observed down-regulation of NDRG2 expression 67
5.2 Possible impact of IL-6 on the expression of NDRG2 68
5.3 The growth rate of cancer cell lines and the influence of NDRG2 expression 69
5.4 The growth rate and the influence of IL-6 treatment 70
6. Conclusion 71
7. Perspectives 72
8. References 73
9. Appendices 87
9.1 Appendix I 87
9.2 Appendix II 89
List of abbreviations
Abbreviation Clarification
RB Retinoblastoma-associated protein
Bcl-2 B-Cell lymphoma 2
Bax Bcl-2 associated X protein
Bak Bcl-2 antagonist/Killer protein
VEGF-A Vascular endothelial growth factor-A
TSP-1 Thrombospondin-1
ECM Extracellular matrix
EMT Epithelial-mesenchymal transition
TAMs Tumor-associated macrophages
EGF Epidermal growth factor
CIN Chromosomal instability
miRNA microRNA
NDRG N-MYC downstream-regulated gene
BMP Bone morphogenetic protein
MMP Matrix metallopeptidase
TGF-β Transforming growth factor beta
CCRCC Clear cell renal cell carcinoma
MHC Major histocompatibility complex
APCs Antigen-presenting cells
B cells B lymphocytes
T cells T lymphocytes
DCs Dendritic cells
NK cells Natural killer cells
PAMPs Pathogen-associated molecular patterns
PRRs Pattern recognition receptors
TLRs Toll-like receptors
Tc cells Cytotoxic T cells
Th cells Helper T cells
Treg cells Regulatory T cells
TCRs T cell receptors
IFN-γ Interferon gamma
TNF-α Tumor necrosis factor alpha
KIR Killer inhibitory receptor
ER Endoplasm reticulum
B2M Beta-2 microglobulin
FasL Fas ligand
IDO Indolamine-2-3-dioxygenase
FasR Fas receptor
TILs Tumor-infiltrating lymphocytes
IL Interleukin
sIL-6R Soluble IL-6 receptor
RT-qPCR Real time quantitative polymerase chain reaction
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1. Introduction
1.1 Cancer
According to the WHO, cancer affected 54.9 million people worldwide in year 2012. Cancer is the
term for a large group of diseases with different origins in the body, and they are also called
neoplasms [WHO, 2014]. Neoplasia refers to new tissue composed by cells with heritable capacity
to do uncontrolled and abnormal growth beyond normal growth patterns [Halazonetis et al.,
2008]. Neoplasia exists in two types; malign and benign, where the malign neoplasia is also named
cancer [Halazonetis et al., 2008]. The process of neoplasia in humans is a multistep process, and
genetic and epigenetic modifications are highly involved in the transformation of normal cells into
cancer cells [Hanahan & Weinberg, 2000; Iacobuzio-Donahue, 2009; Baylin & Ohm, 2006]. Cancer
is caused by alterations in three types of genes, including oncogenes, tumor suppressor genes, and
stability genes, and unlike most other genetic-dependent diseases, cancer does not arise because
of a single gene defect [Vogelstein & Kinzler, 2004]. Cancer can be defined as a hyper-proliferative
disorder and involves morphological cellular transformation, uncontrolled cellular proliferation,
dysregulation of apoptosis, invasion, angiogenesis, and metastasis [Lin & Karin, 2007; Zitvogel et
al., 2006]. Hanahan & Weinberg’s six hallmarks constitute and provide understanding of the
diversity in the processes behind cancer [Hanahan & Weinberg, 2011].
1.2 The hallmarks of cancer
Six hallmarks (see Figure 1) that
describe cancer were proposed in
2000 by Hanahan & Weinberg to
support the description of the
processes behind the development
of cancer cells and the complexity of
the tumor tissue [Hanahan &
Weinberg, 2011]. The hallmarks will
be described more thoroughly in the
next sections.
Figure 1 shows the six hallmarks of cancer [Hanahan & Weinberg, 2011]
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1.2.1 Sustaining proliferative signaling
Under normal conditions the production and release of growth-promoting signals are strictly
controlled by several mechanisms. These control mechanisms ensure homeostasis of cell number
and maintenance of normal tissue structure and function [Clarke & Fuller, 2006; Hanahan &
Weinberg, 2011]. Cancer cells are characterized by their ability to sustain proliferative signaling
and avoid normal growth patterns. They obtain the capability to sustain proliferative signaling in
several ways including; production of growth factor ligands, stimulation of normal cells to produce
and release growth factors, de-regulation of receptor signaling, and structural alteration of
receptor molecules [Cheng et al., 2008; Bhowmick et al., 2004; Hanahan & Weinberg, 2011]. DNA
sequencing of cancer cells genomes has shown somatic mutations in almost all human tumors,
and they may affect the constitutive activation of signaling pathways trigged by activated growth
factor receptors and thus support sustained proliferative signaling [Stratton et al., 2009; Hanahan
& Weinberg, 2011]. Furthermore, the importance of negative-feedback loops has been shown,
and under normal conditions they function by suppressing various types of signaling to ensure
homeostasis in the regulation of signaling through the intracellular circuitry. However, in cancer
cells defects in these negative-feedback loops enable them to enhance proliferative signaling
[Hanahan & Weinberg, 2011; Wertz & Dixit, 2010; Cabrita & christofori, 2008; Amit et al., 2007;
Mosesson et al., 2008].
1.2.2 Evading growth suppressors
Just like the controlled production and release of growth factor signals, cell proliferation is
negatively regulated by specific programs, and many of them depend on the actions of tumor
suppressor genes [Hanahan & Weinberg, 2011]. Tumor suppressor genes are characterized by
their ability to prevent proliferation and growth of tumor cells, and studies have shown that they
are inactivated in many human cancer types [Park & Vogelstein, 2003]. One of the most well-
documented tumor suppressor genes is TP53, which receives signals from stress and abnormality
sensors and then suppress further cell cycle progression until the right conditions are present.
Furthermore, TP53 can trigger the cell to undergo apoptosis, whenever irreparable or
overwhelming damage is observed [Macleod, 2000; Vogelstein et al., 2000; Oren, 2003]. Another
important tumor suppressor gene is the RB gene encoding the retinoblastoma-associated (RB)
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protein, which also functions as a central part of the control mechanisms in growth and cell
division. The RB protein determines if the cell may proceed through the cell cycle, and defects
found in this pathway are connected with permitted persistent cell proliferation in cancer cells
[Burkhart & Sage, 2008; Deshpande et al., 2005; Sherr & McCormick, 2002]. Several studies have
indicated that both TP53 and RB operate through larger networks. Experiments with TP53 null
mice confirm this redundancy in that they show normal development, tissue homeostasis, and
proper cell development [Ghebranious & Donehower, 1998].
Contact inhibition is observed in normal cells and is a consequence of cell-to-cell contact in cell
populations, where two-dimensional cultures are formed. In various types of cancer this
mechanism disappears during the course of tumorigenesis, which suggests that contact inhibition
ensures normal tissue homeostasis in normal cells [Hanahan & Weinberg, 2011]. One of the
mechanisms involved in contact inhibition is through the protein product of NF2. When the
expression of the NF2 gene is lost, the formation of neurofibromatosis is trigged. By coupling of
cell-surface adhesion molecules to the transmembrane receptor tyrosine kinases, the adhesivity of
cell-to-cell attachments is strengthened and the ability to release mitogenic signals is limited by
isolation of growth factor receptors [Curto et al. 2007; Okada et al., 2005].
1.2.3 Resisting cell death
Normal programmed cell death serves as a natural barrier to cancer development, but elevated
levels of oncogene signaling can result in signaling imbalances and lead to hyperproliferation
[Adams & Cory, 2007; Lowe et al., 2004; Evan & Littlewood, 1998]. The apoptotic machinery is
regulated by both extracellular death-inducing signals (the extrinsic apoptotic program) and
signals of intracellular origin (the intrinsic apoptotic program). Both signaling programs induce
activation of proteases, which leads to initiation of a cascade of proteolysis, and the cell is
progressively disassembled and consumed by neighboring cells [Adams & Cory, 2007; Hannahan &
Weinberg, 2011]. Regulatory proteins of the B-Cell lymphoma 2 (Bcl-2) family function as
controllers of apoptotic triggers, and they are inhibitors of apoptosis by binding to the
proapoptotic triggering proteins, Bcl-2 associated X protein (Bax) and Bcl-2 antagonist/Killer
protein (Bak). When no proapoptotic proteins are present, Bax and Bak disrupt the integrity of the
outer mitochondrial membrane, which leads to release of proapoptotic signaling proteins
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including cytochrome c. A cascade of caspases is activated by cytochrome c and induces the
cellular changes leading to apoptosis [Adams & Cory, 2007; Willis & Adams, 2005]. Furthermore,
several abnormality sensors involved in tumor development have been identified and associated
with apoptosis [Adams & Cory, 2007; Lowe et al. 2004]. The most well-known DNA-damage
sensors function through the TP53 tumor suppressor and induce apoptosis by up-regulation of
Noxa and Puma BH3-only proteins, when DNA breaks and chromosomal abnormalities are
detected [Junttila & Evan, 2009]. Tumor cells have developed several strategies to avoid apoptosis
and the most common one is the loss of TP53 tumor suppressor function [Hanahan & Weinberg,
2011].
Autography is another cell degenerating mechanism, which is induced by cellular stress especially
at low levels of nutrient, where it enables cells to break down cellular organelles leading to
recycling for use in biosynthesis and energy metabolism [Levine & Kromer, 2008; Mizushima,
2007]. Cancer cells are able to generate metabolites that support survival in stressed nutrient-
limited environments and ensure further proliferation of cancer cells [Hanahan & Weinberg,
2011].
Necrosis of cells is described as the system-wide exhaustion and breakdown, which leads to
release of proinflammatory signals to the surrounding microenvironment. As a consequence of the
proinflammatory signals, inflammatory cells of the immune system are recruited, and this process
has recently been associated with tumor promotion by inducing angiogenesis, proliferation, and
invasiveness [Grivennikov et al., 2010; White et al., 2010; Galluzzi & Kroemer, 2008].
1.2.4 Enabling replicative immortality
Under normal conditions, cells are only able to pass through a limited number of cell cycles to
prevent uncontrolled cell proliferation and tumorigenesis [Wai, 2004]. At the end of every
chromosome are protective structures called telomeres, which are composed of long repetitive
sequences of TTAGGG [Wai, 2004]. The protection of the chromosomes by telomeres indicates
that telomeres may be involved in unlimited proliferation and thus connected with cancer
development [Blasco, 2005; Shay & Wright, 2000]. Senescence and crisis are the two telomere-
dependent pathways of cell mortality, which prevent proliferation and involve irreversible
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entrance into the cell cycle and cell death [Hanley, 2008]. The first step to prevent further
proliferation is induction of senescence, and for those cells that succeed in circumventing this
barrier, the crisis phase will be induced and the cells will undergo cell death. Unlimited
proliferation has been associated with telomerase activity and expression in immortalized cells
[Wai, 2004]. Both senescence and crisis are suppressed by telomerase activity, and suppression of
telomerase activity thus leads to shortening of telomeres and activation of senescence or crisis
[Hanahan & Weinberg, 2011]. Up-regulation of telomerase expression is the most common
procedure in immortalized cells to maintain telomeric DNA at lengths sufficient to prevent
senescence or apoptosis. Excessive or unbalanced oncogene signaling has been shown to induce
another form of cell senescence and function as a protective mechanism against development of
neoplasia. Thereby cell senescence is a part of the protective barrier against neoplastic expansion
and is triggered by various proliferation-associated abnormalities and shortening of telomeres
[Hanahan & Weinberg, 2011].
1.2.5 Inducing angiogenesis
Angiogenesis is the process whereby new blood vessels are developed from existing ones. This
process is especially used by tumors to acquire nutrients and oxygen, and to evacuate waste
products and carbon dioxide. Under normal conditions angiogenesis is activated in processes like
wound healing, but only temporary [Hanahan & Weinberg, 2011]. In tumor progression,
angiogenesis is almost always activated and new vessels are developed to support the neoplastic
growth [Hanahan & Folkman, 1996]. This change in the activation of angiogenesis is called the
angiogenic switch and is controlled by countervailing factors that either induce or oppose
angiogenesis [Baeriswyl & Christofori, 2009; Bergers & Benjamin, 2003]. The regulation is
performed by signaling proteins, which bind to cell-surface receptors on vascular endothelial cells.
These regulatory proteins are classified as either inhibitor or stimulator proteins, and the most
well-known are vascular endothelial growth factor-A (VEGF-A) and thrombospondin-1 (TSP-1)
[Hanahan & Weinberg, 2011]. VEGF-A is a stimulator of angiogenesis and has been found to be up-
regulated by oncogene signaling and hypoxia, which indicate an important role in tumor
progression [Ferara, 2009; Mac Gabhann & Popel, 2008; Carmeliet, 2005]. Additional signaling
proteins have been associated with tumor angiogenesis based on their proangiogenic properties
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and chronic up-regulation [Baeriswyl & Christofori, 2009]. TSP-1 and several other proteins have
been connected with inhibition of angiogenesis and are called endogenous inhibitors of
angiogenesis [Ribatti, 2009; Kazerounian et al., 2008; Folkman, 2006; Folkman, 2002; Nyberg et al.,
2005]. Many of these proteins can be detected in the circulation in both mice and humans and if
the levels are increased by overexpression, tumor growth is impaired; this indicates a role as an
intrinsic barrier to induction of angiogenesis [Ribatti, 2009; Nyberg et al., 2005].
Cells originating from the bone marrow have been shown to be important in the process of
angiogenesis [Qian & Pollard, 2010; Zumsteg & Christofobi, 2009; Murdoch et al., 2008; De Palma
et al., 2007]. Many cells from the innate immune system originate from the bone marrow
including macrophages, neutrophils, mast cells, and myeloid progenitors, and all of them are able
to infiltrate the tumor environment. These cells are also involved in the angiogenic switch, which
occurs in the early progression of the tumor and ensures facilitation of local invasion [Hanahan &
Weinberg, 2011]. After the migration to the neoplastic environment most of the bone marrow-
derived progenitor cells become pericytes or endothelial cells. Studies have shown an association
between pericytes and the neovasculature found in most tumors, where pericytes are important
for the maintenance of functional tumor neovasculature [Patenuade et al., 2010; Kovacic &
Boehm, 2009; Lamagna & Bergers, 2006; Raza et al., 2010; Bergers & Song, 2005].
1.2.6 Activating invasion and metastasis
Invasion and metastasis is a multistep process and is termed the invasion-metastasis cascade
[Talmadge & Fidler, 2010; Fidler, 2003]. Local invasion of the specific tissue is the first step,
followed by intravasion into blood and lymphatic vessels, which makes the cancer cells able to
escape through the lymphatic and hematogenic systems and invade distant tissues. When the
cancer cells have reached the distant tissue, the formations of small nodules of cancer cells
(micrometastases) takes place and by further growth they develop into macroscopic tumors
[Hanahan & Weinberg, 2011].
The ability of cancer cells to invade and develop metastases involves alterations in shape and
attachment to other cells and the extracellular matrix (ECM). A well-known key cell-to-cell
adhesion molecule is E-cadherin, and the expression of this molecule has been found to be missing
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in carcinoma cells. E-cadherin works by forming junctions with adjacent epithelial cells to
assemble and maintain epithelial cell sheets, and the down-regulation of E-cadherin in carcinomas
support its role as a key suppressor of invasion and metastasis [Berx & van Roy, 2009; Cavallaro &
Christofori, 2004]. The gene expression of many cell-to-ECM adhesion molecules has been found
altered in aggressive forms of carcinomas and especially adhesion molecules involved in cell
migration are up-regulated [Cavallaro & Christofori, 2004].
Epithelial cells become transformed by epithelial-mesenchymal transition (EMT) to acquire the
ability to invade, resist apoptosis, and disseminate [Klymkowsky & Savagner, 2009; Polyak &
Weinberg, 2009; Thiery et al., 2009; Yilmaz & Christofori, 2009; Barrallo-Gimeno & Nieto, 2005].
Carcinoma cells are able to co-opt multiple attributes from the EMT program, which enable the
invasion and metastasis, and further carcinoma cells can activate the EMT program transiently or
stably throughout the invasion and metastasis. The activation of EMT is organized by several
transcriptional factors, which are all expressed in different combinations in different malignant
tumor types [Micalizzi et al., 2010; Taube et al., 2010; Schmalhofer et al., 2009; Yang & Weinberg,
2008]. These transcription factors affect the cells by loss of adherence junctions, expression of
matrix-degrading enzymes, increased motility, and resistance to apoptosis, all processes involved
in invasion and metastasis [Hanahan & Weinberg, 2011]. The gene expression of E-cadherin has
been found repressed by several of these transcription factors, which lead to increased
invasiveness in the neoplastic epithelial cells [Peinado et al., 2004].
The collaboration between cancer cells and cells of the neoplastic stroma has been shown to be
strongly associated with the ability of cancer cells to invade and develop metastases [Egeblad et
al., 2010; Qian & Pollard, 2010; Joyce & Pollard, 2009; Kalluri & Zeisberg, 2006]. Experiments with
metastatic breast cancer have shown collaboration between tumor-associated macrophages
(TAMs) and breast cancer cells, where TAMs supply the cancer cells with epidermal growth factor
(EGF), and the cancer cells supply TAMs with CSF-1. Intravasion into the circulatory system and
development of metastases happen through this communication between normal cells and cancer
cells [Qian & Pollard, 2010; Wyckoff et al., 2007].
The invasion process can be split up in to different modes; collective invasion and amoeboid
invasion [Friedl & Wolf, 2008; Friedl & Wolf, 2010]. Collective invasion is when a small mass of
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cancer cells invades an adjacent tissue, and this type of invasion is characteristic for squamous cell
carcinomas. Individual cancer cells can also perform invasion typically in already existing
interstices in the extracellular matrix, but the knowledge about this amoeboid invasion is limited
[Madsen & Sahai, 2010; Sabeh et al., 2009]. Inflammatory cells have been shown to produce
extracellular matrix-degrading enzymes and other important factors and thereby facilitate cancer
cell invasion and growth [Kessenbrock et al., 2010; Qian & Pollard, 2010; Joyce & Pollard, 2009].
The cancer cells recruit the inflammatory cells by release of chemoattractants, and thus they avoid
producing the matrix-degrading enzymes themselves [Hanahan & Weinberg, 2011].
After the invasion the next step is the development of metastases, which can be described by two
phases; the dissemination of cancer cells from the primary tumor to distant tissues, and the
successful colonization leading to development of micrometastases into macroscopic tumors
[Hanahan & Weinberg, 2011]. The development from micrometastases to macroscopic tumors can
be controlled by systemic suppressor factors, which are released by the primary tumor in some
cancer types [Demicheli et al., 2008; Folkman, 2002]. In those cases, when the primary tumor is
removed the metastatic growth will explode and the macroscopic tumor develop as a
consequence of the missing systemic suppressor factors [Demicheli et al., 2008; Folkman, 2002].
Other reasons for delayed development of micrometastases are inability to activate tumor
angiogenesis, antigrowth signals, tumor suppression by the immune system, nutrient starvation
inducing autophagy, and poor adaption to new microenvironments in the tissue [Naumov et al.,
2008; Aguirre-Ghiso, 2007; Kenific et al., 2010; Lu et al., 2008; Barkan et al., 2010; Teng et al.,
2008; Gupta et al., 2005].
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1.3 Enabling characteristics and emerging hallmarks in cancer
Hanahan & Weinberg have proposed some emerging hallmarks and enabling characteristics of the
development of cancer [Hanahan & Weinberg, 2011]. Enabling characteristics make the process of
tumorigenesis possible, and these
characteristics describe how the
development of cancer cells occurs.
Emerging hallmarks ensure the
progression of the cancer cells by
deregulation of cellular energetics and
the ability to avoid immune
destruction, see Figure 2 [Hanahan &
Weinberg, 2011]. Throughout this
master thesis, the main focus will be on
genome instability and mutations,
avoiding immune destruction, and
tumor-promoting inflammation.
1.3.1 Genome instability and mutations
The first enabling characteristic for cancer development discovered was genome instability and
mutations, which trigger tumor progression [Hanahan & Weinberg, 2011]. Clonal expansion can be
achieved through changes in the genome and are caused by genetic and epigenetic mechanisms
including inactivation of different cancer related genes [Berdasco & Esteller, 2010; Esteller, 2007;
Jones & Baylin, 2007; De Visser et al., 2006; Abbas et al., 2007; Jones, 2007; Shama et al., 2010].
Normally, the genome’s maintenance system is able to detect and resolve defects in the DNA and
thus ensure low rate of spontaneous mutations during each cell generation, but cancer cells are
capable of modifying the maintenance system and increase the rate of mutations in order to
ensure the development of tumorigenesis [Negrini et al., 2010; Salk et al., 2010]. By breakdown in
one or several components of the genomic maintenance machinery, the genome becomes more
sensitive to mutagenic agents and as a result the mutation rate is increased. The surveillance
Figure 2 shows the enabling and emerging hallmarks of cancer [Hanahan & Weinberg, 2011]
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systems of the genome normally monitor genomic integrity and force damaged cells into either
senescence or apoptosis, but the altered mutation rate can compromise these systems and
accumulation of mutations is accelerated [Jackson & Bartek, 2009; Kastan, 2008; Sigal and Rotter,
2000]. A special group of genes of the DNA-maintenance machinery are named caretakers of the
genome and defects in these genes and theirs products are involved in detection of DNA damage,
activation of the repair system and direct repair of damaged DNA, and inactivation of mutagenic
molecules before damaged DNA [Kinzler & Vogelstein, 1997; Negrini et al., 2010; Ciccia & Elledge,
2010; Jackson & Bartek, 2009; Kastan, 2008; Harper & Elledge, 2007; Friedberg et al., 2006].
Introduction of mutant copies of these caretaker genes into mice results in increased cancer
incidence and thus supports their involvement in human cancer development [Barnes & Lindalh,
2004]. Both genetic and epigenetic modifications drive the tumor progression, and the large
numbers of defects in genes of the maintenance and repair systems together with findings of
silencing of tumor suppressor genes are all supporting the enabling characteristic of genome
instability in cancer development [Hanahan & Weinberg, 2011].
1.3.2 Genetic modifications in the genome of cancer cells
Genomic instability is characteristic for almost all human cancer types and is caused by genetic
modifications [Negrini et al., 2010; Stratton et al., 2009]. Different forms of genomic instability are
found, and the most common form in human cancer is called chromosomal instability (CIN), which
refers to a high rate of changes found in chromosome structure and number, when cancer cells
are compared with normal cells [Negrini et al., 2010; McGranahan et al., 2012]. Other forms of
genomic instability have been found, including forms that are characterized by expansion or
contraction of the number of oligonucleotide repeats present in microsatellite sequences, and
increased frequencies of base-pair mutations [Fisher et al., 1993; Leach et al., 1993; Al-Tassan et
al., 2002].
Cancer types can be classified as hereditary or sporadic cancers depending on their origin and
development. Both CIN and non-CIN forms of genomic instability have been associated with
mutations in DNA repair genes and are characteristic for hereditary cancers [Fishel et al., 1993;
Negrini et al., 2010]. The study of mutations in DNA repair genes in hereditary cancers has
provided evidence for the mutation hypothesis concerning the presence of genomic instability in
11
precancerous lesions and increased mutation rate leading to tumor development [Nowell, 1976;
Loeb, 1991]. As mentioned earlier, genomic instability has been linked to mutations in caretaker
genes, and they include DNA repair genes and mitotic checkpoint genes. Furthermore, the tumor
suppressor gene TP53 and ataxia telangiectasia mutated gene have been considered as caretaker
genes because of their function in DNA damage responses [Negrini et al., 2010]. The presence of
genomic instability caused by inactivation of caretaker genes in sporadic cancers has not been
confirmed successfully, and the molecular basis of genomic instability in sporadic cancers is still
unclear [Negrini et al., 2010]. Several studies have investigated target sequences hoping to find
mutations in DNA repair and mitotic checkpoint genes with higher mutation frequency, and
thereby explain the genomic instability and development of sporadic cancers [Rajagopalan &
Lengauer, 2004; Cahill et al., 1999; Wang et al., 2004; Cahill et al., 1998]. The low frequency of
mutations in caretaker genes observed in sporadic cancers may be underestimated because of the
repression of gene function caused by epigenetic mechanisms [Esteller, 2008]. These epigenetic
abnormalities together with genetic alterations are important processes in the transformation of
normal cells to cancer cells [Sharma et al., 2010].
1.3.3 Epigenetics
All DNA in cells is packaged into chromatin forms, and these structures define the state of
organization of genetic information within the cell [Sharma et al., 2010]. The chromatin structure
is made by nucleosomes, where every unit contains 146 base pairs of DNA. The DNA is wrapped
around a histone octamer
consisting of four histone
proteins, named H3, H4,
H2A, H2B, see Figure 3
[Luger et al., 1997;
Iacobuzio-Donahue, 2009;
Momparler, 2003].
Epigenetics was defined by
C.H. Waddington in the 1940s based on epigenetics in embryonic development. Later the
definition was modified, so that epigenetic changes mean heritable changes in gene expression
Figure 3 shows the chromatin structure, nucleosome and histone composition [Füllgrabe et al., 2010]
12
which are not accomplished by changes in the primary DNA sequence, and the definition of
epigenetics is modifications of DNA [Jones & Baylin, 2007; Momparler, 2003; Baylin & Ohm, 2006;
Iacobuzio-Donahue, 2009; Sharma et al., 2010; Rodriquez-Paredes & Esteller, 2011]. The
epigenetic processes are essential for key biological processes, such as proper development,
cellular differentiation, imprinting, and silencing of large chromosomal domains, which include
histone modifications, genome imprinting, and DNA methylation [Sharma et al., 2010; Rodriquez-
Paredes & Esteller, 2011; Jones & Baylin, 2007; Iacobuzio-Donahue, 2009; Jaenisch & Bird, 2003;
Momparler, 2003]. Studies have found that genetic and epigenetic alterations interact at all stages
of cancer development and promote cancer progression [Sharma et al., 2010; Jones & Laird,
1999]. Epigenetic modifications have further been connected with suppression of tumor
suppressor genes in several cancer types. Thereby, epigenetic modifications are of highest interest
for this project because of the connection between cancer development and repression of tumor
suppressor genes such as the potential tumor suppressor gene, NDRG2.
1.3.4 Epigenetic changes in normal cells
The function of the genome is regulated through different epigenetic mechanisms such as DNA
methylation, histone modification, and miRNAs, which all modify the chromatin structure [Sharma
et al., 2010]. All these modification mechanisms work together and regulate the genome by
altering the local structure of chromatin, thereby creating an “epigenome” that ensures the
cellular identity by the way the genome manifests itself in different cell types [Jones & Baylin,
2007; Bernstein et al., 2007; Suzuki & Bird, 2008; Kouzarides, 2007; Zhang et al., 2007; Jiang et al.,
2009].
Normally, the primary sequence of DNA consists of
four bases named adenine, guanine, cytosine and
thymine, but a fifth base called 5-methylcytosine can
be produced by covalent modification of post-
replicative DNA. This process is also called DNA
methylation, in which S-adenosyl-methionine
function as the methyl donor and the process is
catalyzed by the enzyme DNA methyltransferase (DNMTs). The methylation is finished when the
Figure 4 shows the DNA methylation process [Meehan, 2013]
13
methyl group is added to the cytosine ring, see Figure 4, and the cytosine becomes methylated
[Herman & Baylin, 2003]. The process of DNA methylation takes place after DNA replication and
cell division, and the process is performed by a maintenance DNA methylase, also called DNMT1
[Momparler, 2003]. DNA methylation provides stable gene silencing and plays an important role
in regulation of gene expression [Sharma et al., 2010]. This epigenetic modification primarily
occurs in cytosines of the dinucleotid sequence CpG, and CpG-rich regions are called CpG islands
[Herman & Baylin, 2003; Weber et al., 2007; Jaenisch & Bird, 2003]. The distinction between CpG
sites and CpG islands is based on size, with CpG islands defined as a 1 kb stretch of DNA containing
the sequence more frequently than the rest of the genome [Momparler, 2003]. CpG islands are
primarily located in the 5’end of the gene and in 60 % of all human gene promoters, and they are
normally not methylated in normal cells, whereas CpG sites have been found to be methylated to
prevent chromosome instability [Herman & Baylin, 2003; Weber et al., 2007; Suzuki & Bird, 2008;
Wang et al., 2004].
Another important epigenetic mechanism is histone modification. The histone is composed of four
core histone proteins with DNA wrapped around them. The nucleosome has an N-terminal tail and
a C-terminal domain, and the N-terminal tail can undergo many post-translational modifications
such as methylation, acetylation, ubiquitylation and phosphorylation [Kouzarides, 2007]. All these
modifications are added or removed by different enzymes, including histone acetyltransferases
(HATs), deacetylases (HDACs), methyltransferases (HMTs), demethylases (HDMs), and so on
[Kouzarides, 2007; Allis et al., 2007]. Different combinations of modifications in specific genomic
regions may lead to a more open or closed state of the chromatin structure, and unlike DNA
methylation this leads to either activation or repression of the genes [Li et al., 2007; Sharma et al.,
2010]. Furthermore, these specific patterns of histone modifications may play a potential role in
determining cellular identity by affecting gene expression [Mikkelsen et al., 2007; Ringrose et al.,
2007].
Interactions between DNA methylation and histone modifications are necessary for each of them
to perform their individual roles in gene regulation, and the complexity of epigenetic regulation is
further enhanced by these interactions [Cedar & Bergman 2009].
14
MicroRNAs (miRNA) are small noncoding RNAs that are able to regulate gene expression through
posttranscriptional silencing by binding to the 3’ untranslated region of mRNAs [Baer et al., 2013;
Sharma et al., 2010]. This binding leads to inhibition of protein synthesis or RNA degradation, both
affecting the expression of the target gene [Baer et al., 2013; He & Hannon, 2004]. Like other
epigenetic mechanisms, miRNAs are also part of the control of different biological processes,
including cell proliferation, apoptosis, and differentiation, and the miRNAs can be regulated like
normal genes by epigenetic modifications [Saito & Jones, 2006]. Furthermore, miRNAs are able to
modulate epigenetic regulatory mechanisms by targeting enzymes involved in DNA methylation
and histone modifications [Sharma et al., 2010].
1.3.5 Epigenetic modifications and cancer
Several studies have stated the fact that epigenetic changes play an important role in
tumorigenesis [Momparlet 2003; Sharma et al., 2010; Rodriguez-Paredes & Esteller, 2011]. The
first connection between epigenetic abnormality and cancer was found by Feinberg and
Vogelstein in 1983, who observed a reduction in methylation in colon cancer cells when compared
with normal tissue [Feinberg & Vogelstein, 1983]. Additionally, Gama-Sosa et al. demonstrated a
reduction of 5-methylcytosine content, and both reductions where observed in pre-invasive and
invasive cancer tissues [Gama-Sosa et al., 1983]. DNA methylation is a normal event in gene
regulation, but aberrant DNA methylation can lead to silencing of tumor suppressor genes and
also to activation of oncogenes, which are two important groups of genes in cancer development
[Momparlet, 2003; Sharma et al., 2010; Esteller, 2007]. The activation of oncogenes happens by a
mechanism called DNA hypomethylation, which means loss of methylation, and which can lead to
demethylation of specific coding regions [Feinberg & Tycko, 2004]. Furthermore, DNA
hypomethylation differ from the more site-specific DNA hypermethylation by affecting many
genomic sequences in the genome and thus leading to genomic instability [Esteller, 2007]. The
degree of hypomethylation increases through the progression of cancer [Fraga et al., 2004]. In
both gastric cancer and colon cancer, growth-promoting genes such as R-Ras, MAPSIN and S-100
have been found activated by hypomethylation [Sharma et al., 2010]. Also, promoter regions have
been found to be demethylated allowing normally repressed genes to become expressed, for
15
example the gene PAX2 known to encode a transcription factor involved in proliferation of cells
[Wu et al., 2005; Brueckner et al., 2007].
Opposite to DNA hypomethylation, DNA hypermethylation works by silencing especially tumor
suppressor genes and thereby inducing cancer progression [Costello et al., 2000; Esteller et al.,
2001]. The first DNA hypermethylations were found in the CpG Island of the promoter region of
the Rb tumor suppressor gene [Greger et al., 1989; Sakai et al., 1991]. This finding was further
supported by hypermethylation in other tumor suppressor genes, such as P16, MLH1, VHL (Hippel-
Lindau disease) and BRCA1 (Breast cancer) [Herman & Baylin, 2003; Esteller et al., 2000; Baylin,
2005; Jones & Baylin., 2002; Jones & Baylin, 2007]. The mentioned tumor suppressor genes are all
involve in DNA repair, cell cycle control, cell adhesion, apoptosis, and angiogenesis, which are all
important steps in the cancer development and progression [Sharma et al., 2010; Feinberg, 2005;
Howard et al., 2008]. DNA hypermethylation of the CpG islands in tumor suppressor genes are
specific for each cancer type [Esteller et al., 2001; Grady et al., 2000]. How these specific
hypermethylation “patterns” occur for each cancer type is still unclear. Besides affecting tumor
suppressor genes, hypermethylation can also indirectly silence genes encoding transcription
factors and DNA repair genes [Sharma et al., 2010].
A new potential tumor suppressor gene is NDRG2, which has been found to be involved in cell
growth, initiation and progression of cancer, cell differentiation, and apoptosis [Shi et al., 2009;
Yao et al., 2008; Choi et al., 2003]. Down-regulation or inactivation of NDRG2 expression has been
linked to transcriptional repression by MYC, post-translational inactivation by microRNA, and
epigenetic silencing through promoter methylation [Oh et al., 2012; Zhang et al., 2006; Shi et al.,
2009; Tepel et al., 2008; Lusis et al., 2005; Piepoli et al., 2009; Furuta et al., 2010; Zhao et al.,
2008; Shi et al., 2009]. Hypermethylation is one of the most important reasons for down-
regulation and loss of NDRG2 expression, and hypermethylation of the NDRG2 promoter is
significantly associated with meningioma, breast cancer, and colorectal cancer [Yao et al., 2008;
Feng et al., 2011; Piepoli et al., 2009; Tepel et al., 2008; Liu et al., 2007; Lusis et al., 2005].
16
1.3.6 N-Myc downstream-regulated family of genes
One of the earliest identified oncogenes was MYC, which has been associated especially with
regulation of cell proliferation and differentiation. A small fraction of Myc-repressed genes have
shown the ability to affect the interaction and communication between the cells and their external
environment, and several of these have been associated with tumor suppressor and metastatic
properties [Vervoorts et al., 2006; Grandori et al., 2000; Dang, 1999; O’Connell et al., 2003]. One
of the families of Myc-repressed genes are the N-Myc downstream-regulated family of genes
(NDRG), which consists of four genes called NDRG1-4 and are all found in humans [Qu et al., 2002;
Liu, 2012; Yao et al., 2008]. Because of the potential as tumor suppressors, the NDRG gene family
has been given special attention in cancer research [Yao et al., 2008].
The NDRG genes are localized on four different chromosomes, and they code for proteins of
varying sizes ranging from 339 – 394 amino acids, see Table 1 [Lorentzen & Mitchelmore, 2012;
Zhao et al., 2001]. The sequences of the four genes have sequence homologies between 57 and 65
% [Lorentzen & Mitchelmore, 2012; Chu et al., 2011; Melotte et al., 2010]. The different isoforms
of the NDRG genes are uniquely expressed in tissues between species, which is especially apparent
for NDRG4 in human and mice [Melotte et al., 2010].
Name Chromosomal location Isoform Number of exon Protein length (aa) NDRG1 8q24 0 16 394 NDRG2 14q11.1-11.2 1
2 14 13
371 357
NDRG3 20q11.21-q11.23 1 2
16 15
375 363
NDRG4 16q21-q22.1 1 2 3
17 16 15
371 352 339
Table 1 shows the comparison of human N-myc downstream-regulated gene (NDRG) gene family [Lorentzen & Mitchelmore, 2012; Melotte et al., 2010; Yao et al., 2008]
Common for NDRG proteins is the α/β hydrolase fold domain and NDR-domain, where the α/β
hydrolase fold domain has showed no catalytic function in any of the genes [Zheng et al., 2010;
Lorentzen & Mitchelmore, 2012; Bhaduri et al., 2003; Shaw et al., 2002]. The sequence
differences are primarily located in the N-and C-terminal regions of the NDRG genes, and NDRG1
differs from the rest of the genes by having three 10-aa tandem repeats in the C-terminal region,
17
see Figure 5 [Melotte et al., 2010]. All family
members have a CpG island in their
promoter, which is an important feature in
DNA methylation of the genes [Melotte et
al., 2010].
All the proteins of the NDRG gene family
have been correlated with the regulation of
cell proliferation, differentiation,
development, and stress responses [Kim et
al., 2009; Melotte et al., 2010]. The
function of NDRG1 is the most well-known,
and alterations in the protein have been observed, when specific mutations are present in the
gene. Especially two mutations have been linked with Charcot-Marie-Tooth disease, which is also
known as a demyelinating disorder [Hunter et al., 2003; Kalaydjieva et al., 2000]. NDRG1
expression is induced by cellular stresses, and it is involved in inflammatory processes, regulation
of cell growth, metastasis suppression, and nerve myelination [Taketomi et al., 2003; Piquemal et
al., 1999; Kalaydjieva et al., 2000; Kim et al., 2009]. Important roles in neurodegenerative diseases,
cell differentiation, and cancer have been found for all genes of the NDRG family, and especially in
studies on NDRG2 expression, significantly low levels of NDRG2 were found in tumors and cancer
cell lines, when compared with normal benign tissues. This may indicate a potential role for
NDRG2 as a tumor suppressor and as a prognostic marker in some cancer types [Lorentzen &
Mitchelmore, 2012; Wang et al., 2012; Yang et al., 2011; Shi et al., 2009; Lusis et al., 2005; Park et
al. 2008].
1.3.7 NDRG2
Two isoforms of NDRG2 are found located on chromosome 14q 11.1-11.2, and they differ in
numbers of exons and amino acids, see Table 1 [Dake, 2011 & Libo, 2008; Feng et al., 2011]. In the
C-terminal region of NDRG2, several potential phosphorylation sites are found, and they may have
influence on regulatory mechanisms by a phosphorylation-dephosphorylation cycle [Lorentzen et
al., 2011; Kim et al., 2009; Yao et al., 2008].
Figure 5 shows the variation between the genes in the NDRG family [Melotte et al., 2010]
18
The expression of the NDRG2 gene has been analyzed on mRNA level, and high expression of
NDRG2 mRNA was found in brain, heart, skeletal muscle, liver, and kidney, and low expression was
found in colon, spleen, placenta, and lung tissue [Yao et al., 2008; Wang et al., 2012; Feng et al.,
2011; Lorentzen et al., 2011]. The observed expression pattern of NDRG2 suggests a correlation
between the level of NDRG2 and the rate of cell proliferation, and during development the
expression level of NDRG2 is increased [Hu et al., 2006]. The protein product of NDRG2 is found in
cytoplasm, cell membranes, adherence junctions, and the nucleus [Deng et al., 2003; Qu et al.,
2002; Lachat et al.,2002; Hu et al., 2006; Shen et al., 2008; Okuda et al., 2008; Yang et al., 2011].
Beside the potential role in cancer development, an up-regulation of NDRG2 has been observed in
patients with Alzheimer disease and linked with neural differentiation, synapse formation, and
axon survival [Mitchelmore et al., 2004; Nichols et al., 2005]. NDRG2 is also up-regulated under
hypoxic conditions in cancer cell lines, which may indicate a role as a cell stress responding
molecule. This observation is further supported by experiments where NDRG2 silencing reduced
hypoxia-induced apoptosis suggesting that NDRG2 function as a positive regulator of hypoxia-
induced apoptosis [Melotte et al., 2010; Wang et al., 2008]. Furthermore, NDRG2 has been
associated with insulin-production, aldosterone-mediated epithelial sodium channel function, and
dendritic cell differentiation [Choi et al., 2003; Boulkroun et al., 2002; Burchfield et al., 2004;
Wielpütz et al., 2007].
As a member of the N-myc downstream-regulated gene family, NDRG2 is transcriptionally
regulated by Myc, which function as a master switch molecule in cell proliferation and
differentiation [Dang et al., 2008; Wierstra & Alves, 2008]. Shi et al. have also shown that C-Myc
can repress human NDRG2, and in experiments with colorectal cancer an increased level of Myc
was observed, whereas the level of NDRG2 was decreased [Shi et al., 2009]. This may indicate a
potential role for NDRG2 as an inhibitor of cancer cell proliferation [Shi et al., 2009].
In several types of cancer, the level of NDRG2 has been found to be decreased or undetectable,
and these observations may indicate an important role in initiation and progression of cancer cells.
Supporting the statement of NDRG2 as a tumor suppressor gene, NDRG2 was found to suppress
cell proliferation, cell survival, and induce apoptosis through regulation of cyclin D1 and T cell
19
factor (TCF)/β-catenin activity [Shi et al., 2009; Lorentzen & Mitchelmore, 2012; Yao et al., 2008;
Chu et al., 2011; Zheng et al., 2010; Liang et al., 2012].
1.3.8 NDRG2 & Cancer
Several experiments have confirmed different expression patterns for NDRG2 in tumors and
normal tissue [Deng et al., 2003; Lusis et al., 2005; Lorentzen et al., 2007; Choi et al., 2007; Liu et
al., 2007; Hu et al., 2004; Assämäki et al., 2007; Felsberg et al., 2006; Hummerich et al., 2006]. The
connection between low expression of NDRG2 and proliferation of cancer cells was first observed
in glioblastoma cells, and the NDRG2 expression was reduced by 56 % in human glioblastoma
tissue, when compared to normal tissue samples [Deng et al., 2003]. Furthermore, Deng et al.,
showed that NDRG2 was able to inhibit proliferation of glioblastoma cells, when NDRG2 was
expressed in the tissue [Deng et al., 2003]. Down-regulation or absence of NDRG2 expression on
both mRNA and protein level have been observed in several types of cancer, including colorectal
cancer, breast cancer, lung cancer, hepatocellular cancer, glioma, oral squamous cell carcinoma,
thyroid cancer, liver cancer, pancreas cancer, meningioma, clear cell renal cell carcinoma (CCRCC),
prostate cancer, gallbladder cancer, gastric cancer, and myeloid leukemia [Chu et al., 2011; Hu et
al., 2004; Lee et al., 2008; Lorentzen et al., 2007; Piepoli et al., 2009; Lorentzen et al., 2011; Zhao
et al., 2008; Ma et al., 2008; Wang et al., 2012; Chang et al., 2012]. In addition, nineteen different
types of cancer tissues have been studied, and an up-regulation of NDRG2 mRNA was only
observed in around 8 % of the tissues, whereas 62 % of the tissues showed unchanged expression
of NDRG2 mRNA. Every third sample of cancer tissue tested showed a down-regulation of NDRG2
expression when compared with normal tissue [Lorentzen et al., 2011].
In studies of breast cancer, low or no expression of NDRG2 was observed, and this may be of
importance for the metastatic potential by inducing bone morphogenetic protein 4 (BMP-4)
expression thereby suppressing matrix metallopeptidase 9 (MMP-9) activity, which has influence
on metastasis and angiogenesis [Zheng et al., 2010; Lorentzen et al., 2011]. Oh et al. have shown
clinically that NDRG2 suppresses tumor metastasis by decreasing the active autocrine
transforming growth factor beta (TGF-β) production, which leads to a significantly higher
recurrence and survival rate in patients [Oh et al., 2012]. CD24 glycoprotein is expressed on the
surface of most B lymphocytes and differentiated neuroblasts and function as an adhesion
20
molecule for P-selectin, which is related to tumor growth and metastasis. The CD24/P-selectin
binding pathway causes interactions with platelets and endothelial cells, which lead to spreading
of cancer cells. NDRG2 has thereby been identified as a regulator of adhesion and invasion
processes in breast cancer, hepatocellular carcinoma cancer, lung cancer, and gallbladder
carcinoma [Zheng et al., 2010; Song et al., 2012; Wang et al., 2012]. Lung cancer studies have also
shown a correlation between high CD24 levels and metastasis. NDRG2 is highly expressed in the
early stages of cancer development without any pathological metastasis in lung cancer patients.
Reduced NDRG2 expression is thereby associated with CD24 up-regulation and poor prognosis in
breast cancer, lung cancer, hepatocellular carcinoma, and gallbladder carcinoma [Wang et al.,
2012; Zheng et al., 2010; Zheng et al., 2011; Song et al., 2012]. High levels of CD24 have been
correlated with lymph node metastases, high TNM status, and lower survival rate [Song et al.,
2012].
Studies with hyperthermia have show changes in invasion capacity and apoptosis rate in both
hepatocellular carcinoma and gastric cancer [Tao et al., 2013; Guo et al., 2013]. Hyperthermia
leads to overexpression of NDRG2 by inhibition of MMP-2, MMP-9, and invasion in hepatocellular
carcinoma. Suppression of MMP-9 is correlated with a higher metastasis rate, but the
overexpression of NDRG2 suppresses the function of MMP-9 [Guo et al., 2013; Zheng et al., 2010;
Oh et al., 2012]. Knockdown of NDRG2 expression reverses the effect of the hyperthermia reaction
by inducing invasion [Guo et al., 2013]. Apoptosis rate in gastric cancer is increased by
approximately 8.3% after one hour of treatment [Tao et al., 2013].
In clear cell renal cell carcinoma (CCRCC), NDRG2 has been associated with inhibition of CCRCC cell
lines growth rates and induction of cell cycle arrest at G1 in vitro [Liang et al., 2012]. Furthermore,
NDRG2 expression was found to be down-regulated, which has been linked with oncogenic
properties, and NDRG2 may function as a potential prognostic biomarker. Additionally, the
decrease in NDRG2 expression has been associated with higher TNM stages [Song et al., 2011; Ma
et al., 2012; Liang et al., 2012].
Metastasis in cancer patients is the leading cause of death, and it is necessary to find a prognostic
marker to detect the different types of cancer early in their progression phase. Based on these
studies, NDRG2 is correlated with inhibition of invasion and metastasis and may function as a
21
prognostic marker in different cancer types [Zheng et al., 2010; Lorentzen et al., 2011]. High
expression of NDRG2 have also been connected with better survival chances and smaller risk of
developing metastases [Chu et al., 2011; Li et al., 2011; Oh et al., 2012; Zheng et al.,2010].
1.4 Avoiding of immune destruction
One of the hallmarks of cancer development is avoidance of immune destruction, and under
normal cellular conditions the immune system is responsible for protection against foreign
pathogens [De Visser et al., 2006; Hanahan & Weinberg, 2011]. All foreign pathogens, including
tumor cells, express antigens and the immune system is introduced to these antigens by major
histocompatibility complex (MHC) molecules on antigen-presenting cells (APCs) [Pardoll, 2012].
Tumor cells have developed different strategies to avoid the immune system, and the relationship
between them is described by a theory called immunoediting [Dunn et al., 2002; Bhardwaj, 2007;
Schreiber al., 2011].
1.4.1 Immune system
All living organisms have evolved
strategies to protect themselves
against pathogens, and these
strategies are collectively referred
to as the immune system
[Hoffmann et al., 1999; Murphy et
al., 2008; Waller et al., 2005]. The
system consists of the immediate
innate immune system and the highly specific adaptive immune system, and by collaboration of
these two systems the immune system is able to recognize and eliminate invading pathogens
[Palm & Medzhitov, 2009]. The system works through recognition, reaction, regulation, and
memory functions, which differ between the innate immune system and the adaptive immune
system [Medzhitov & Janeway, 1997; Cooper & Alder, 2006]. The innate immune system detects
infections by recognition of unique molecular structures, and the adaptive immune system uses
Figure 6 shows the different types of cells in the immune system [Dranoff, 2004]
22
highly specific receptors to recognize nearly any antigen and their clonal expression [Medzhitov &
Janeway, 1997; Cooper & Alder, 2006]. Both systems are composed and react through several
different cell types, see Figure 6 [Dranoff, 2004].
1.4.2 Innate immunity
The innate immune system is the first defense against pathogens, and it consists of several cell
types including the white blood cells. Besides B lymphocytes (B cells) and T lymphocytes (T cells),
both dendritic cells (DCs) and macrophages are important for the interplay between the innate
immune system and the adaptive immune system [Abbas et al., 2007]. All cell types mentioned in
Table 2 express specific recognition receptors and become activated during an inflammatory
response. After activation, the cells become effector cells, whose primary role is to combat
detected pathogens [Janeway & Medzhitov, 2002].
Cell types Function Reaction
Neutrophils The most important cell in the innate
immunity. Acts on many different
pathogens
Early phagocytosis and killing of pathogens
Dendritic cells
(DCs)
Linked to both innate and adaptive
immunity. Present antigens for T cells
Release cytokines, when introduced to pathogens
Macrophages Present antigens for T cells Efficient phagocytosis and killing pathogens,
secretion of cytokines that stimulate inflammation
Natural killer
(NK) cells
Recognize abnormal cells and kill them,
e.g. tumor cells.
Lysis of infected cells and activation of macrophages
Table 2 shows the most important cell types of the innate immune system and further describe their function and reaction pattern [Abbas et al., 2007; Murphy et al., 2008; Wood, 2006; Clancy, 1998]
The epithelial barrier and circulating plasma proteins have also been considered a part of the
innate immunity [Janeway & Medzhitov, 2002; Abbas et al., 2007]. The epithelial barrier produces
antibiotics, and together with lymphocytes it helps to prevent penetration into the host.
Circulating plasma proteins are a varied group of proteins e.g. the proteins of the complement
system, which are also able to recognize pathogens and serve as effector molecules [Abbas et al.,
2007].
23
1.4.3 Recognition of pathogens by the innate immune system
The innate immunity uses special structures called pathogen-associated molecular patterns
(PAMPs) to detect pathogens. These patterns are unique for each pathogen, and they are products
of pathways that are unique for the microbe, which allows for discrimination between self and
non-self molecules [Janeway & Medzhitov, 2002; Abbas et al., 2007; Medzhitov, 2007]. The innate
immune system uses a variety of pattern recognition receptors (PRRs), which are expressed on the
cell surface, in intracellular compartments, or in the circulating system, and the most well-known
are Toll-like receptors (TLRs) [Janeway & Medzhitov, 1997]. When the PRRs are bound to the
PAMPs, the PRRs become activated and induce one or two responses; activation of antimicrobial
and proinflammatory functions in the cells and/or facilitating pathogen uptake into the cell. Some
PRRs are soluble and ensure clearance of pathogens in blood and extracellular fluids [Abbas et al.,
2007]. As mentioned earlier, other mechanisms have evolved in the innate immune system to
recognize pathogens, and these include the complement system, specialized receptors for NK
cells, and other intracellular sensors [Hoebe et al., 2004]. The activation of the innate immune
system is important for the development of the adaptive immune systems responses [Takeda &
Akira, 2005].
1.4.4 The interplay between the innate immune system and the adaptive
immune system
Activation of the adaptive immune system requires two specific signals, where one is provided by
the innate immune system, and the interplay between these two systems is therefore of highest
importance in the recognition and elimination of pathogens [Kindt et al., 2007]. The adaptive
immune system recognizes pathogens through antigens presented by antigen-presenting cells
such as DCs and macrophages, which are part of the innate immune system [Hoebe et al., 2004;
Lydyard et al., 2001; Akira et al., 2006]. T cells and B cells are a major part of the adaptive immune
system, and they are also able to recognize antigens and produce appropriated responses. Besides
antigen presentation, an additional signal is needed for full activation of T cells and B cells to
ensure correct distinction between foreign antigens and self-antigens. This signal is provided by
different soluble molecules released by the innate immune system, e.g. cytokines [Abbas et al.,
2007].
24
1.4.5 Adaptive immunity
All responses by the adaptive immune system are primarily produced by lymphocytes, including B
cells and T cells, which are further described in a later section [Kindt et al., 2007; Werling et al.,
2003]. T cells and B cells become activated when they are presented to foreign antigens, which are
only expressed when pathogens are present in the host. All antigens have their own unique
molecular structure, and these structural variations are called the antigenic variation [Lydyard et
al., 2001; Wood, 2006; Murphy et al., 2008]. Antigens are able to interact with antibodies
produced by the host as a response against pathogens. Antibodies are glycoproteins grouped in
five distinct classes, and they interact with antigens with a high specificity and affinity [Lydyard et
al., 2001; Wood, 2006; Murphy et al., 2008]. Antigens are presented on the surface of APCs, and
APCs are only able to present the antibodies through MHC glycoprotein molecules. The MHC
molecules are encoded by large clusters of genes called MHC genes and are organized in three
classes of molecules [Reche & Reinherz, 2003]. MHC genes are expressed by different cell types.
MHC Class I molecules are expressed by almost every nucleated cell and assist in the presentation
of antigens to T cytotoxic (Tc) cells. MHC Class II molecules are expressed primarily on APCs and
present antigens to T helper (Th) cells. MHC Class III genes code for proteins e.g. in the
complement system, immune receptors, TNF, and regulatory receptors [Trowsdale, 2001; Kindt et
al., 2007]. When antigens are presented on APCs by MHC molecules, the adaptive immune system
will be able to distinguish between self and foreign antigens and produce a proper response. After
the antigen-antibody interaction occurs, the antigens are removed by specific antibody responses
leading to phagocytosis of foreign cells [Kumagai & Tsumoto, 2001; Wood, 2006; Lydyard et al.,
2001].
1.4.6 B cell and T cell function and activation
B cells and T cells are developed in the bone marrow and belong to the lymphocytes, which are
the only cells in the body capable of recognize and distinguishing foreign antigens. Furthermore,
they are responsible for the adaptive immune system’s characteristics, which include specificity
and memory [Abbas et al., 2007]. Both classes of cells are described in Table 3.
25
Class Function Antigen presentation
CD4+ T Helper (Th)
lymphocytes
B cell differentiation (Humoral immunity)
Macrophage activation (cell-mediated immunity)
Able to recognized antigens present
by MHC class II complexes
CD8+ T cytotoxic (Tc)
lymphocytes
Killing of cells infected by pathogens
Killing of tumor cells
Able to recognized antigens present
by MHC class I complexes
T Regulatory (Tregs)
cells
Suppress function of other T cells (regulation of
immune responses, maintenance of self-
tolerance)
B lymphocytes (B cells) Antibody production (humoral immunity) Surface antibody
Table 3 shows the most important cell types of the adaptive immunity, their function and antigen presentation [Abbas et al., 2007; Murphy et al., 2008; Wood, 2006; Clancy, 1998]
B cells are not fully developed, when they are released from the bone marrow, and they are called
naïve B cells until they are activated. They become activated by antigen-antibody interactions
through B cell receptors presenting membrane-bound antibodies. After the interaction, the
proliferation and differentiation stages are completed, and the B cells become memory B cells or
effector B cells also called plasma B cells. Memory B cells express the same membrane-bound
antibodies as naïve B cells, whereas plasma B cells produce antibodies released to the
environment as part of the humoral immunity [Kindt et al., 2007; Wood, 2006; Murphy et al.,
2008].
The production of T cells also starts in the bone marrow, but unlike B cells, T cells migrate to the
thymus in order to mature completely. Two types of T cells are produced, and both cell types
express T cell receptors (TCRs), see Table 3. The two T cells types differ from each other by
expressing different membrane glycoproteins on the surface. Th cells and Treg cells both express
CD4 on their surface, but differ in surface markers and activation. Tc cells express CD8 on their
surface. Most TCRs are only able to recognize antigens through MHC molecules [Kindt et al., 2007;
Wood, 2006; Murphy et al., 2008].
26
1.4.7 The Adaptive immunity is able to respond in two different ways
The adaptive immune system can react by using two different response mechanisms; humoral and
cell-mediated response. The humoral immunity only detects extracellular antigens, whereas the
cell-mediated immunity only detects intracellular pathogens, see Figure 7 [Kindt et al., 2007;
Abbas et al., 2007]. Humoral immunity is performed by activated B cells, which bind antigens
expressed by extracellular microbes through their receptors. Before the humoral response can be
launched, Th cells have to present the extracellular antigens to the B cells, which then leads to
activation and maturation of the B cells. The activated B cells mature into plasma B cells and
memory B cells, where plasma B cells release antibodies against the extracellular pathogens and
memory B cells recognize and remember the antigen specificity. The released antibodies interact
with the antigens and facilitate the
clearance of the pathogens [Kindt et al.,
2007; Murphy et al., 2008].
Cell-mediated immunity recognizes and
eliminates intracellular pathogens and
detects genetic modifications in cells, e.g.
modifications seen in tumor cells [Lydyard
et al., 2001; Kindt et al., 2007].
For activation of T cells, two different
signals are necessary provided by APCs
and co-stimulatory molecules. TCRs can
only bind to antigens presented by MHC
class II molecules and the co-stimulatory
molecules; B7 is expressed by APCs, which interact with CD28 on T cells [Chen et al., 1992; Guinan
et al., 1994]. Proliferation of naïve T cells begins, when both signals are present, and the activated
T cells become effector T cells, which include both Th cells and Tc cells. Memory T cells can be
developed instead of effector T cells during the proliferation, and they have almost the same
function as memory B cells, see Figure 7 [Lydyard et al., 2001; Kindt et al., 2007]. Activated Th cells
are separated into two different subtypes, Th1 and Th2 cells, where Th1 cells are primarily
Figure 7 shows the two types of adaptive immunity [Abbas et al., 2007]
27
developed to fight against infections caused by intracellular bacteria [Kindt et al., 2007]. They
trigger a phagocyte-mediated host response through the production of high levels of interferon
gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). Parasites and allergens activate Th2 cells
and trigger a phagocyte-independent host response by producing cytokines such as IL-4, IL-5 and
IL-6, which are all involved in B cell differentiation and maturation, thus indicating that Th2 cells
are primary involved in humoral immunity [Romagnani, 1995; Lydyard et al., 2001].
Many cytokines produced by Th cells are essential for activation of Tc cells, and naïve Tc cells are
incapable of eliminating any target cells. Tc cells recognize antigens presented by MHC class I
molecules expressed on all nucleated cells. Thereby, Tc cells are able to recognize and eliminate
almost any cell expressing MHC class I molecules [Kindt et al., 2007]. Before Tc cells can be
activated, they need three signals. Two of them are the same as mentioned for Th cells activation,
and the third signal is induced by inflammatory cytokines [Curtsinger & Mescher, 2010].
1.4.8 Cancer immunology
One of the issues about tumor formation is how cancer cells are able to progress and avoid the
immune system. The theory about immunosurveillance proposes that cells and tissues are
monitored constantly, and thereby the immune system is able to recognize and eliminate
precancer cells and nascent tumors [Hanahan & Weinberg, 2011]. Several studies support this
theory. Experiments have shown increased tumor development and higher progression rate in
mice that are genetically engineered for deficiency of various components of the immune system
when compared to mice with an intact immune system. These observations were made in mice
with engineered deficiencies in development or function of cytotoxic T cells, Th1 cells, or NK cells.
Furthermore, mice with combined deficiencies in both T cells and NK cells were even more
susceptible to cancer development [Bui & Schreiber, 2007; Finn, 2008; Vajdic & van Leeuwen,
2009; Teng et al., 2008; Kim et al., 2007]. Over time several theories and hypotheses have
attepted to answer the question of how tumor cells are still able to survive. The most recent
theory is called immunoediting, which includes three essential phases; elimination, equilibrium,
and escape [Kim et al., 2007].
28
1.4.9 Immunoediting: from immune surveillance to immune escape
In 1909, Ehrlich proposed a theory on the role of the immune system in protection against cancer
[Ehrlich, 1909]. This theory was later modified by Burnet and Thomas, who provided the theory
about immunosurveillance, which has later been validated by findings of tumor-associated
antigens in tumor transplantation models [Burnet, 1964 &Thomas, 1959]. It has now become clear
that the immune system has three distinct roles in preventing cancer development, which include
protection of the host against viral infections and virus-induced tumors, preventing establishment
of an inflammatory environment to prevent production of inflammatory components, and
elimination of nascent tumor cells [Schreiber et al., 2011]. In 2001, it became clear that the
immune system was controlling both tumor quantity and quality, also termed immunogenicity
[Shankaran et al., 2001; Dunn et al., 2002]. This was based on results of studies with
immunocompetent mice (intact immune system) and immunodeficient mice (lack of immune
components), which showed that tumors transplanted from immunocompetent mice into naïve
wild type recipients were able to develop in the new host, whereas tumors received from
immunodeficient mice were not able to continue to grow in the new host [Shankaran et al., 2001].
Furthermore, these results also revealed that tumors from immunocompetent mice were more
immunogenic than those from immunodeficient mice, and they indicate that the immune system
not only protects against tumor development, but also affects the immunogenicity in tumor cells
[Schreiber et al., 2011]. These statements form the basis of the cancer immunoediting hypothesis,
which brings us closer to a possible explanation of how tumor cells are able to survive and prevent
immune responses, including three different phases; elimination, equilibrium, and escape [Dunn
et al., 2002; Bhardwaj, 2007; Kim et al., 2007; Vesely et al., 2011; Dunn et al., 2004; Smyth et al.,
2006; Swann & Smyth, 2007].
1.4.10 From elimination to escape
The elimination of cancer cells is supported by antitumor immune responses, and experiments
with infiltration of Tc cells and NK cells in the tumor have shown better prognosis for the survival
[Bindea et al., 2010; Ferrone & Dranoff, 2010; Nelson, 2008]. It has been observed that
immunosuppressed organ transplant recipients can develop cancer after transplantation of a
29
tumor-free organ, which indicates that an intact immune system is important to hold the cancer
cells in check [Strauss & Thomas, 2010].
In the phase called equilibrium, the cancer cells are living in equilibrium with the immune system
of the host. Increased sculpting of cancer cells leads to production of cells with resistance to
immune effector cells, and through immune selection cancer cells with reduced immunogenicity
are able to survive the immune responses [Kim et al. 2007]. Tumor cells with reduced
immunogenicity are more capable of surviving in hosts with an intact immune system, and several
studies in mice with a range of deficiencies have indicated different degrees of immune selection
pressure [Kim et al., 2007]. The tumor-host equilibrium can be explained by the possibility that
highly immunogenic cancer cells are able to avoid the immune system through disabling of
important immune components involved in elimination of cancer cells [Shields et al., 2010;
Hanahan & Weinberg, 2011].
The phase of cancer cell escape can be described as the tumors’ ability to avoid and prevent
immune recognition. Several tumor-derived soluble factors are able to induce different
mechanisms, all important in escape from immune attacks [Kim et al., 2007]. This phase includes
multiple steps and strategies, such as loss of antigenic presentation, tumor antigens, sensitivity
against immune-effector molecules, and induction of Treg cells, which are all further described in
the section below.
1.4.11 Cancer cells’ most common strategies to avoid the immunity
All strategies presented in this section are the most well-known and documented, and they
represent many aspect of the immune system.
1.4.11.1 NK cells and the NKG2D receptor
Under normal conditions NK cells’ ability to kill directly is inhibited by the presence of MHC class I
molecules. Inhibition of NK cells happens when MHC class I molecules bind to NK cells through a
receptor called killer inhibitory receptor (KIR) [Cerwenka & Lanier, 2001]. Without the expression
of MHC class I molecules on the cell surface, NK cells are activated to kill the cell [Weinberg, 2013].
NK cells recognize tumor cells through different NK receptors, NKp46, NKp50 and NKG2D, where
the NKG2D receptors are the most well known [Gasse & Raulet, 2006]. As a result of genetic
30
damage, viral infection, and neoplastic transformation many cells express stress signal proteins,
and these can be detected by NK cells through the NKG2D receptors [Bui & Schreiber, 2007;
Weinberg, 2013]. Interaction between the NKG2D receptor and the stress signal proteins leads to
a release of IFN-γ, which consequently elicits several distinct responses. Many components of the
immune system are recruited by release of IFN-γ including macrophages, which are able to kill
either directly or indirectly through APCs. Additionally, IFN-γ also increases the expression of MHC
class I molecules on tumor cells leading to expression of tumor antigens and further adaptive
responses [Weinberg, 2013]. Under normal conditions tumor cells would be expected to release
stress signal proteins as a consequence of the neoplastic transformation leading to activation of
the NKG2D receptor and an up-regulation of stress signal proteins, but a down-regulation of these
stress signal proteins is observed in tumor cells instead [Bui & Schreiber, 2007; Gasser & Raulet,
2006]. This is supported by experiments in mice capable of expressing NKG2D, which have shown
that tumor cells suppress the expression of the Rae1 stress antigen in order to avoid NK cell attack
[Weinberg, 2013].
1.4.11.2 MHC molecules
NK cells and Tc cells use the same killing mechanism by introducing a protease to target the cell
and induce apoptosis. Tumor cells are able to avoid the attack from Tc cells by increasing the level
of inhibitor-of-apoptosis proteins [Krajewska et al., 2003]. Normally Tc cells recognize tumor cells
through tumor antigens presented by MHC class I molecules on APCs, but tumor cells have also
shown ability to down-regulate the expression of MHC class I molecules [Finn, 2008; Houghton &
Guevara, 2004; Hicklin et al., 1999; Garrido et al., 1997]. Down-regulation of MHC class I molecules
makes the tumor cells less antigenic, and thereby they avoid immune responses because of the
missing detection of tumor antigens. The expression of antigens is inhibited by promoter
methylation of antigen-coding genes, which makes the tumor cells invisible to effector immune
cells [Bicknell et al., 1994]. The migration of MHC class I molecules from the endoplasmic
reticulum (ER) to the cell surface depend on beta-2 microglobulin (B2m) proteins. Synthesis of
B2m proteins is inhibited by some tumor cells and the transport of MHC class I molecules is
prevented, which leads to repressed expression of antigens [Weinberg, 2013]. Another mechanism
seen especially in highly invasive and metastatic tumors is inhibition of the transcription of MHC
class I genes, which leads to reduced mRNA and prevents further synthesis of MHC class I
31
molecules [Weinberg, 2013]. In some cases, tumor antigens are essential for neoplastic
proliferation, and a down-regulation of the MHC class I molecules will lead to activation of NK cells
and elimination of the tumor cells. Six types of MHC class I molecules have been classified and
some tumor cells are able to selectively suppress expression of only one of these molecules, which
are normally expressed concomitantly by cells throughout the body. This may block the
presentation of tumor antigens and thereby prevent attack by Tc cells and NK cells [Weinberg,
2013].
1.4.11.3 Treg cells
A population of T cells known to express high levels of CD25 and other proteins are called Treg
cells, and under normal conditions they are produced to suppress autoreactive T cells through
contact-dependent mechanisms [Bhardwaj, 2007; Curiel, 2007]. Improved immune-mediated
tumor rejection and tumor antigen-specific immunity have been found in studies with mice lacking
Treg cells [Curiel, 2007]. Two different types of Treg cells have been found including natural and
adaptive Treg cells [Curiel, 2007]. Natural Treg cells are produced in the thymus to prevent
autoimmunity, whereas adaptive Treg cells are produced under inflammatory conditions such as
infections or cancer [Curiel, 2007]. Adaptive Treg cells suppress effector T cells through direct
contact and production of immune-suppressive cytokines, IL-10, and growth factor beta (TGF-β)
[Curiel, 2007; Zou, 2006; Bhardwaj, 2007; De Visser et al., 2006; Finn, 2008]. Tumors recruit
adaptive Treg cells by the chemokine CCL22, and they infiltrate the tumor microenvironment by
interaction with adaptive Treg receptors called CCR4. Located in the middle of the tumor they
inhibit the action of effector T cells [Weinberg, 2013]. The production of TGF-β in mice with
tumors has shown conversion of antitumor effector T cells into Treg cells [Woo et al., 2001 & Liu et
al., 2007]. Furthermore, TGF-β and tumor cells have been found to induce the differentiation of
Treg cells [Curiel, 2007].
1.4.11.4 Transforming growth factor beta (TGF-β)
Tumor cells produce different immunosuppressive products, such as TGF-β, Fas ligand (FasL), and
indolamine-2,3-dioxygenase (IDO), which are all produced to prevent or decrease immune
responses [Finn, 2008; Elgert et al., 1998; Chouaib et al., 1997]. TGF-β is a cytokine involved in
proliferation, activation, differentiation, and apoptosis of innate and adaptive immune cells and
32
functions by inhibition of antitumor immune responses [Igney & Krammer, 2002; Siegel &
Massagué, 2003]. TGF-β is able to reduce the presentation of antigens through APCs by inducing
apoptosis and thereby decreasing the number of APCs [Weinberg, 2013]. Furthermore, TGF-β has
an immunosuppressive cytostatic effect on T cells, and NK cells, and prevents maturation of DCs
by inhibition of MHC class II molecules expression [Weinberg, 2013; Siegel & Massagué, 2003].
1.4.11.5 Indolamine-2-3-dioxygenase (IDO)
Tumor cells are able to develop an immune-suppressive enzyme called IDO [Uyttenhove et al.,
2003 & Muller & Prendergast, 2007]. Earlier, the enzyme has been connected with tolerance
between mother and fetus, but later it has been found to regulate auto-immunity by T cell
activation [Munn et al., 2002]. IDO is up regulated in both tumor cells and DCs, and its function is
dependent on activation signals from APCs [Bhardwaj, 2007; Prendergast, 2008]. Most IDO is
found in the tryptophan catabolism function as a rate-limiting enzyme, whereby it induces a fall in
tryptophan level and has a negative consequence for T cell proliferation [Bhardwaj, 2007; Finn,
2008]. Studies with inhibitors of IDO in mice have shown an induction of immunity [Finn, 2008].
1.4.11.6 Fas receptor and Fas ligand
Fas receptor (FasR) and FasL molecules play important roles in immune escape and tolerance
[O’Connell et al., 1996; Zhang et al., 2007]. FasL is able to induce apoptosis in immune cells leading
to limited immune responses [Nguyen & Rusell, 2001; O’Connell et al., 1996]. Activation of
apoptosis happens when FasL interact with FasR, but tumor cells are able to develop resistance
against FasL-mediated mechanisms by releasing soluble FasL. The attention is then removed from
the tumor cells over to the different lymphocytes, where FasR will interact with the soluble FasL
[Connell et al., 1996; Weinberg, 2013]. Furthermore, tumor cells can attack Fas-sensitive tumor-
infiltrating lymphocytes (TILs) by increasing the level of FasL, which leads to tumor cell immunity
and cancer progression [Lin et al., 2001; Zhang et al., 2007].
33
1.5 Tumor-promoting inflammation
A second enabling characteristic is tumor-promoting inflammation, and this involves the balance
between the immune system and the tumor microenvironment. Chronic inflammation has been
associated with cancer progression and inadequate pathogen eradication, prolonged
inflammatory signaling, and defects in anti-inflammatory mechanisms [Han & Ulevitch, 2005]. The
tumors are infiltrated with cells from both the innate and adaptive immunity, and the
inflammation response contributes with bioactive molecules to the tumor microenvironment.
These bioactive molecules, including growth factors, survival factors and extracellular matrix-
modifying enzymes, can all lead to proliferation, continued survival, angiogenesis, invasion, and
metastasis [Hanahan & Weinberg, 2011; DeNardo et al., 2010; Grivennikov et al., 2010; Qian &
Pollard, 2010; Karnoub & Weinberg, 2006-2007]. The network of the tumor microenvironment
includes inflammatory cytokines, growth factors, and chemokines produced by tumor cells or
tumor-associated leukocytes and platelets [Balkwill & Mantovani, 2001; Grivennikov & Karin,
2010; Hsu & Chung, 2006; Lin & Karin, 2007].
1.5.1 Inflammation and cancer
Since the 19th century inflammatory cells have been observed within tumors, and tumors often
occur at sites of chronic inflammation, which provides the first indication of a possible link
between inflammation and cancer [Schetter et al., 2010; Grivennikov et al., 2010; Balkwill &
Mantovani, 2001]. Epidemiological studies now support these findings and suggest that up to 25 %
of cancer cases are related to inflammation, and that 15-25 % of all deaths from cancer are linked
to infections and inflammation [Aggarwal et al., 2009; Perwez Hussain & Harris, 2007; Balkwill &
Mantovani, 2001]. Several chronic inflammatory diseases have been connected with increased risk
of cancer development including Crohn’s disease, ulcerative colitis, chronic pancreatitis, and
chronic bronchitis [Ekbom et al., 1990; Gillen et al., 1994; Ekbom et al., 1993; Wu et al., 1995;
Mayne et al., 1999]. Furthermore, chronic inflammation caused by microbial or parasitic infections
is involved in development of cancer by viral hepatitis B and C, Helicobacter pylori, and parasitic
worms, which all lead to chronic inflammation and promotion of several cancer types [Schetter et
al., 2010; Tsukuma et al., 1993; Parsonnet et al., 1991; Grivennikov et al., 2010].
34
Studies have shown that 90 % of all cancers are associated with somatic mutations and
environmental factors, and many of these factors are related to chronic inflammation
[Grivennikov et al., 2010]. Acute inflammation often occurs as a response to microbial infection
and tissue damage, however whether chronic inflammation is triggered by microbial infections,
autoimmune disease, viral infections, exposure to allergens, toxic chemicals, obesity, and
inflammation is still unknown [Mantovani et al., 2008; Schetter et al., 2010]. Tissue homeostasis is
monitored by the immune system, and tissue damage or infections interrupt the balance and lead
to an immune response. Sometimes an imbalance of the response provided by the innate immune
system leads to constant activation of the immune system and subsequently to chronic
inflammation [De Visser et al., 2006]. Cells exposed frequently to chronic inflammation will
undergo oncogenic transformation, and the causes of this are many, e.g. induction of genomic
instability, increased angiogenesis, epigenetic altering, and increased proliferation. Furthermore,
inflammation is able to change the gene expression of oncogenes and tumor suppressor genes in
order to promote neoplastic transformation [Schetter et al., 2010]. Chronic inflammation can
provide the tumor environment with a supply of inflammatory mediators, which include growth
factors, survival factors, proangiogenic factors, extracellular matrix-modifying enzymes, and
inductive signals, all involved in either proliferation, apoptosis, angiogenesis, invasion, or
metastasis [DeNardo et al., 2010; Grivennikov et al., 2010; Qian & Pollard, 2010; Karnoub &
Weinberg, 2006-2007]. The outcome of an inflammatory immune response depends on the
balance between proinflammatory and anti-inflammatory mediators of the adaptive and innate
immune system, and inappropriate responses of the immune system can affect this balance, see
Figure 8 [Ben-Baruch, 2006; Kim et al., 2006; Schetter et al., 2010].
Figure 8 shows the importance of the overall balance between proinflammatory and anti-inflammatory mediators [Schetter et al., 2010]
35
The connection between cancer and inflammation
can be explained by the intrinsic and extrinsic
inflammation pathways, see Figure 9 [Mantovani et
al., 2008; Schetter et al., 2010]. The difference
between the two pathways is the way they are
activated. The intrinsic pathways are primarily
activated by genetic events including genetic
alterations of oncogenes and tumor suppressor
genes leading to cancer, whereas the extrinsic
pathways involved in cancer are caused by infection
and chronic inflammation [Mantovani et al., 2008;
Schetter et al., 2010]. The two pathways only differ
in their activation and cause of cancer promotion,
but both of them result in activation of
transcription factors such as NF-κB, STAT3 and
HIF1α in both tumor cells and immune cells [Karin,
2006; Yu et al., 2007]. When the transcription
factors are activated, they ensure production of
prostaglandins, chemokines, and cytokines by the
tumor cells, where the cytokines are signaling
molecules involved in many cellular functions especially
in inflammation and immune responses [Schetter et al., 2010; Mantovani et al., 2008]. Cytokines
are classified as either pro-inflammatory or anti-inflammatory mediators, and they are important
for the balance between promotion and suppression of cancer mentioned earlier, but also for the
recruitment and activation of leukocytes [Schetter et al., 2010]. Thereby the same transcription
factors are activated again, but this time in both inflammatory cells and tumor cells, which leads to
production of more inflammatory mediators, and the environment for cancer development is
generated [Mantovani et al., 2008]. One of the inflammatory mediators is the cytokine interleukin
6 (IL-6), which is important in transition from acute inflammation to chronic inflammation and
triggers the activation of the transcription factor STAT3 [Erreni et al., 2011].
Figure 9 shows the intrinsic and extrinsic inflammation pathways that connect cancer and inflammation [Mantovani et al., 2008]
36
1.5.2 Function of IL-6 and its role in cancer
The family of IL-6 cytokines consists of IL-6, leukemia inhibitory factor, oncostatin M,
cardiotrophin-1, ciliary neurotrophic factor, and cardiotrophin-like cytokine [Taga & Kishimoto,
1997; Derouet et al., 2004]. Common for all is the ability to activate the signal transducing
receptor protein Gp130, and thereby activation of target genes involved in differentiation,
survival, apoptosis, and proliferation [Heinrich, 2003].
IL-6 is a multifunctional cytokine with functions in both immune and non-immune cells produced
by lymphoid and non-lymphoid cells such as T cells, B cells, monocytes, fibroblasts, endothelial
cells, and several tumor cells [Heinrich, 2003; Becker et al., 2004; Grivennikov & Karin, 2008
Kishimoto, 1989]. IL-6 has both pro- and anti-inflammatory properties and is important for
immune responses, cell survival, apoptosis, proliferation, and production of acute phase proteins
[Heinrich, 2003 & Kishimoto, 1989]. The expression of IL-6 is regulated both negatively and
positively by different cytokines. TNF-α and IL-1 are able to increase the production of IL-6,
whereas glucocorticoids affect the production of IL-6 negatively [Kishimoto, 1989].
The group of IL-6 receptors is arranged into two subunits of receptors, non-signaling α-receptors
(IL-6Rα, IL-11Rα and CNTFRα) and signal transduction receptors (gp130, LIFR and OSMR) [Heinrich,
2003]. In order to establish the complex, IL-6 interacts with the α-receptor (IL-6Rα) integrated in
the cell membrane. The IL-6/ IL-6Rα complex then interacts with the gp130 glycoprotein on the
cell surface thus forming an activating receptor unit, which leads to activation of the STAT
pathway. Gp130 glycoproteins are expressed on almost every cell in the organisms, whereas the
expression of IL-6Rα is restricted and tightly regulated leading to the degree of cytokine sensitivity
in the cell [Grivennikov & Karin, 2008; Heinrich et al., 2003].
Cells missing the membrane-bound receptor can be activated by trans-signaling through the
soluble IL-6 receptor (sIL-6R). Together, sIL6R and IL-6 form a complex called the IL-6/sIL-6R
complex. This interaction shows exactly the same activation of the JAK/STAT pathway as seen
when interacting with IL-6Rα units [Lin & Karin, 2007; Becker et al., 2004]. When the JAK/STAT
pathway is activated, JAK1 handles the phosphorylation of STAT proteins including STAT1 and
STAT3, see Figure 10. Nuclear translocation and activation of specific target genes happen through
37
phosphorylation dimerization, and target genes are primarily involved in cell cycle progression and
suppression of apoptosis. STAT3 proteins are predominant in IL-6 signaling transduction and
further involved in malign cell proliferation and survival. STAT1 has been shown to inhibit the
growth of tumor cells, and studies in lung cancer cells have shown that knockdown and inhibition
of STAT3’s phosphorylation lead to delayed cell growth [Lin & Karin, 2007]. Moreover, a role as a
pro-tumor agent has been suggested for IL-6 supported by poor prognosis related to significantly
elevated levels of IL-6 found in lung and breast cancer
patients [Grivennikov & Karin, 2008].
In addition to activation of the JAK/STAT pathway, IL-6
can work directly with some immune cells in response
to pathogens. Further studies have shown IL-6
involvement in the final step in differentiation of B cells
and stimulation of B cells to become plasma B cells
[Abbas et al., 2007]. Kishimoto has suggested a
potential role for IL-6 as a factor for T cell activation and
proliferation, based on the fact that resting T cells
express IL-6Rα [Kishimoto, 1989]. Thereby IL-6 is an
important part of the immune system and the activation
of the JAK/STAT pathway.
1.5.3 Interleukin 6 and NDRG2
Immune responses in the tumor microenvironment can promote or inhibit cancer progression,
and the STAT proteins have a central role in determining the response outcome [Yu et al., 2009].
STAT3 and NF-kB function as nuclear transcription factors for genes involved in tumor
proliferation, survival, angiogenesis, and invasion, and NF-kB further targets genes coding for IL-6,
which functions as a STAT3 activator. STAT3 induces the expression of cytokines, growth factors,
and angiogenesis factors leading to activation of theirs associated receptors, which result in a
reactivation of STAT3, including IL-6 and IL-10 [Naugler & Karin, 2007; Yu et al., 2009]. Thereby a
feed-forward loop is created between tumor cells and immune cells in the tumor
microenvironment, and the persistent activation of STAT3 promotes tumor-inflammation and
Figure 10 shows JAK/STAT pathway [Shuai & Liu, 2003]
38
inhibits anti-tumor immunity [Yu et al., 2009]. IL-6 and IL-10 are both key activators of STAT3, and
NDRG2 expression has been found to induce SOCS1, which negatively regulates STAT3 activation
in breast cancer cells [Park et al., 2007]. Furthermore, NDRG2 expression has been observed to
modulate SOCS3 and STAT3 activity, which may lead to inhibition of IL-10 [Lee et al., 2010]. IL-6
and IL-10 are strongly connected, and therefore it would be interesting to see if a connection
between NDRG2 expression and IL-6 levels exists.
1.6 Short summary
Various mechanisms have been connected to tumorigenesis including emerging hallmarks and
enabling characteristics, which describe the relationship between the immune system and cancer
and genomic changes involved in tumorigenesis [Hanahan & Weinberg, 2011]. Silencing of tumor
suppressor genes is an important mechanism of tumorigenesis and can lead to inhibition of tumor
suppressor genes’ normal function in suppression of cancer development [Hanahan & Weinberg,
2011]. NDRG2 has been suggested as a potential tumor suppressor candidate, and the observation
of down-regulation of NDRG2 in several cancer types supports this statement [Chu et al., 2011; Hu
et al., 2004; Lee et al., 2008; Lorentzen et al., 2007; Piepoli et al., 2009; Lorentzen et al., 2011;
Zhao et al., 2008; Ma et al., 2008; Wang et al., 2012]. Under normal cellular conditions, the
immune system is responsible for protection against foreign pathogens including tumor cells [De
Visser et al., 2006; Hanahan & Weinberg, 2011]. Furthermore, the responses of the immune
system against inflammation and chronic inflammation have been associated with cancer
development. The oncogenic transformation of tumor cells exposed to frequent chronic
inflammation has been connected with epigenetic alterations and changed expression of tumor
suppressor genes [Schetter et al., 2010]. Additionally, the tumor environment is supplied with
inflammatory mediators including cytokines as a result of immune responses and chronic
inflammation [DeNardo et al., 2010; Grivennikov et al., 2010; Qian & Pollard, 2010; Karnoub &
Weinberg, 2006-2007]. An important cytokine is IL-6, which has been connected with chronic
inflammation through its ability to activate STAT3, and persistent activation of STAT3 leads to
inhibition of anti-tumor immunity [Yu et al., 2009]. NDRG2 expression has been reported to induce
SOCS1 expression leading to down-regulation of STAT3 in breast cancer, therefore it would be
39
interesting to study a possible connection between NDRG2 expression and IL-6 levels in colon
cancer cells [Lee et al., 2010].
2. The aim of the Master’s thesis
The aims of this Master’s thesis are to study the influence that IL-6 and NDRG2 may have on colon
cancer cells, and to study the impact that IL-6 may have on colon cancer cells proliferation with
and without the expression of NDRG2
Experimental description:
- To use colon cancer cell lines to analyze the expression level of NDRG2 on both mRNA and
protein level
- To treat colon cancer cell lines with IL-6 and then analyze the expression of NDRG2 on
mRNA level
- To establish stable cell lines expressing NDRG2
- To analyze if the expression of NDRG2 affects the growth rates of colon cancer cells
- To analyze if colon cancer cells expressing NDRG2 may have different growth rates
compared to cells without the expression of NDRG2
- To analyze if transfected colon cancer cells expressing NDRG2 have different growth rates
when treated with IL-6
To perform the above-mentioned analyses, the methods and techniques described in the next
section were used.
40
3. Materials & Methods
The sections provide a short introduction to the theory behind the methods used, followed by a
description of the particular use in this thesis. The experimental design is described briefly to give
a better understanding of the process.
3.1 Experimental Design
In the present study, the expression of NDRG2 and the growth of colon cancer cells were
examined under various conditions. The mRNA and protein expression of NDRG2 in the cell lines
were examined by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and
western blotting. The growth rate of the cell line was examined by growth assays.
The conducted experiments are categorized into two groups and investigate different aspects of
NDRG2 and the presence of IL-6, see Figure 11.
Figure 11 shows a schematic overview of the two groups of experiments conducted through this master thesis
NDRG2 expression
Expression in colon cancer cells
at mRNA level
Expression in colon cancer cells
at protein level
Expression in colon cancer cells
after IL-6 treatment
Growth assays
Normal colon cancer cells
Colon cancer cells with NDRG2
Colon cancer cells treated with IL-6
Colon cancer cells with NDRG2 and treated with IL-6
41
3.2 Cells Culture - growth, harvesting, and treatment
All cell experiments were conducted with colon cancer cell lines SW480 and HCT116. The colon
cancer cells lines SW480 and HCT116 were originally isolated from an adenocarcinoma and a
colorectal carcinoma. The cells were cultured in T25 or T75 culture flasks supplied with McCoy’s
5A medium (Sigma-aldrich, #M9309), 10% fetal bovine serum (Sigma-aldrich, #F0804), and 1%
Penicillin-streptomycin (Sigma-aldrich, #P0781) to provide nutrition and avoid infections. To
ensure optimal growth conditions, the cells were incubated at 37°C with 5% CO2 in a humidified
atmosphere.
The cells were passaged twice a week to ensure space for continued growth, and the frequency
was based on the individual growth rate for the cell lines. The splitting procedure was as follows:
wash with 10 ml PBS (Lonza, #BE17-512F) twice to remove the rest of the medium followed by
trypsinization with 2 ml Trypsin 1X (Sigma-aldrich, #T3924) to loosen the cells from the bottom of
the flasks. The cells were resuspended in 8 ml McCoy’s 5A medium to separate the cells
completely, and around 1.5-2 ml cell suspension was kept for further culture.
The rest of the cell suspension was used for isolation of RNA, DNA and protein. The cell suspension
was transferred to a 15 ml tube and spun down for five minutes at 1500 rpm to isolate the cells.
The supernatant was removed and the cells were resuspended in 3 ml PBS to remove any
remaining medium. 1 ml of the cell suspension was transferred to three 1.5 ml eppendorf tubes
and spun down for five minutes at 1500 rpm to isolate the washed cells. After removing the
supernatant the pellets were stored at -80 °C.
3.2.1 Treatment of Cells
Cancer cell lines HCT116 and SW480 were treated with recombinant human IL-6 (Sigma-Aldrich,
#H7416) in order to analyze the effect that different IL-6 levels may have on colon cancer cell lines
and the expression of NDRG2.
The treatment efficiency was first tested with different concentration and over a time period of 72
hours, in order to find the most efficient concentration and time. Hsu & Chung have tested
different concentrations for IL-6 treatment of colon cancer cells and based on their results the
42
following concentrations (10 ρM, 50 ρM and 10.000 ρM) were chosen for this study [Hsu & Chung,
2006]. The cells were plated at a density of 1 x 105 cells per well in 24-well plates for 24 hours, and
medium was changed before initiation of IL-6 treatment. All samples were handled in duplicates
or triplicates and treated with the following concentrations: 10 ρM, 50 ρM and 10.000 ρM over
distinct time periods of 24, 48, and 72 Hours. Hereafter, the cells were harvested for further
analysis. See Figure 12 for further information on the following procedure.
Figure 12 shows the whole procedure for the treated cells
When the most efficient concentration was found, the colon cancer cell line SW480 was treated
with IL-6 to analyze if the cytokine had an impact on the growth rate of SW480 cells with and
without expression of NDRG2. Only SW480 cells were used for treatment, because of the fact that
the HCT116 cells do not express NDRG2 protein. Cells were plated at a density of 1 x 104 cells per
well in 24-well plate for 24 hours, and the medium was changes before the treatment of IL-6
(10,000 ρM) was started. All samples were handled in duplicates and treated over a time period of
24, 96, and 168 hours.
Treatment of cellsMeasurement of
expression by qPCR
Growth assays
43
3.3 Quantification of NDRG2 mRNA level
The methods used to analyze the level of NDRG2 mRNA are described in the following section.
These include RNA extraction, cDNA synthesis, and qRT-PCR.
3.3.1 RNA extraction
Before initiation of RNA extraction, all materials used were cleaned
with RNaseZap (Sigma-aldrich, #R2020). RNA extraction includes the
steps: homogenization, extraction, precipitation, and solubilization,
see Figure 13:
1. The cells were disrupted to separate RNA from the cells
2. The homogenate was separated in an organic and an aqueous
phase. RNA is found in the aqueous phase
3. RNA was precipitated and the isolation of RNA was completed
after the wash steps
4. RNA was stored in Rnase-free water at -80°C until use
Throughout the whole procedure, the pellet must stay on ice. The pellet was resuspended in 1 ml
TRI reagent® solution (Ambion, #AM9738) and incubated at room temperature (RT) for five
minutes. TRI reagent® solution contains phenol and guanidine thiocyanate, which inhibit the
Rnase activity. The isolation of RNA from the cells is performed by centrifugation at 12000 x g, 4 °C
for 15 minutes, and hereafter the supernatant was transferred to new tubes and work continued
with these tubes.
To separate RNA and waste, 100 μL of Chloroform (100%) was added to the supernatant and the
tubes were vortexed shortly. The tubes were then incubated at RT for 10 minutes to ensure the
phase division, and the tubes were then centrifuged at 12,000 x g, 4°C for 15 minutes to enable
transfer of the RNA-containing phase to new tubes. Then 500 μL Isoprophanol (100%) was added
and the samples were vortexed shortly. Samples were incubated at RT for 5 minutes followed by
centrifugation at 12,000 x g, 4 °C for 20 minutes. Supernatants were discarded.
Figure 13 shows the RNA extraction in steps [Ambion, 2014]
44
Pellets were washed with 1 ml Ethanol 75% and centrifuged at 12000 x g, 4 °C for 20 minutes. The
ethanol was gently removed, and the tubes were air-dried for maximum 10 minutes. The samples
were then resolved in 50 μL of Rnase-free water and vortexed shortly, and the concentration of
RNA was measured at 260 nm on the NanoDrop® spectrophotometer ND-1000. Pellets were
stored at -80 °C or used immediately.
3.3.2 cDNA synthesis
By cDNA synthesis double stranded cDNA was
produced and used downstream for quantification
of mRNA levels of specific genes by qRT-PCR.
Random primers annealed to all RNA sequences,
and reverse transcriptase performed the synthesis
of cDNA. Hydrolysis of RNA strands happen when
the synthesis of cDNA is completed and the cDNA is
now ready to use. See Figure 14.
For cDNA synthesis, High capacity cDNA reverse
transcription kit (Applied Biosystems, #4368814)
was used, and the total volume of the mastermix for
each reaction was 10 μL. The mastermix was
prepared as follows: 2.0 μL 10X Reverse
transcriptase buffer, 0.8 μL 25X dNTP mix (100 mM),
2.0 μL 10X Reverse transcriptase Random Primers,
1.0 μL MultiScribe™ reverse transcriptase and 4.2 μL nuclease-free water. Then, the mastermix
was gently mixed and kept on ice until use to avoid activation of the reverse transcriptase.
In preparation, the RNA samples were diluted to a concentration of 100 ng/μL in order to ensure
an equal amount of RNA in all cDNA samples. 10 μL mastermix was transferred to 0.2 ml PCR tubes
with 10 μL of diluted RNA and mixed to resolve the RNA in the mastermix.
The thermal cycler was set to run at 25 °C for 10 minutes, 37 °C for 120 minutes, 85 °C for 5 sec
and paused at 4 °C. The cDNA was stored at -20 °C.
Figure 14 shows the cDNA synthesis step by step [Addison Wesley Longman, 1999]
45
To minimize the risk of contamination and to ensure that only the desired RNA was in the samples,
two different cDNA control reactions were performed; one without RNA and one without
MultiScribe™ reverse transcriptase.
3.3.3 Quantitative real time polymerase chain reaction (qRT-PCR)
Expression of mRNA of a preferred gene was measured by use of qRT-PCR, which is a procedure
including several steps, see Figure 15:
1. By denaturation at 95°C, double stranded DNA was separated.
2. By primer annealing, forward and reverse primers were attached to each of the DNA
strands at varied temperatures depending on the specific primer set.
3. Extension was performed by DNA polymerase enzyme at 72°C.
In this study the SYBR Green technique was used, which is a commonly used fluorescent DNA
binding dye. Through binding to all double-stranded DNA, the increase in fluorescence can be
measured through the cycle and is equal to the amount of DNA amplified over time.
Figure 15 shows the different steps in the qRT-PCR procedure [Lu et al., 2011 (modified)]
For qPCR, Quantitech® SYBR® Green PCR kit (Quigen, #204141) was used, and for each reaction
the mastermix contained; 5 μL 2X Quantitech SYBR Green PCR mastermix, 0.5 μL (10 ρmol/μL)
forward primer, 0.5 μL (10 ρmol/μL) reverse primer, and 3 μL nuclease-free water. Each reaction
consisted of 1 μL cDNA and 9 μL mastermix, giving a total volume of 10 μL. Primer sequences and
annealing temperatures can be seen in
46
Table 4 shows primer sequences and annealing temperatures (Tm) for quantitative Real Time Reverse Transcriptase
Polymerase Chain Reaction (qRT-PCR)
Table 4 shows primer sequences and annealing temperatures (Tm) for quantitative Real Time Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR)
The program at the thermo cycler was as follows; 95°C for 15 minutes, 95°C for 15 sec, annealing
temperature (varied with primers) for 30 sec, and lastly 72°C for 30 sec, repeated for 40 cycles.
3.3.4 Standard curve
To assess the qPCR reaction, the efficiency of the primers is important, and by use of standard
curves it is possible to optimize the qPCR. A template known to express the gene of interest was
diluted at different concentrations to constitute a dilution series. The standard curve gives a linear
regression formula, which can be used to determine the reaction efficiency. The optimal slope of
the standard curve has a correlation coefficient (R2) of 0.999, but an R2 value greater than 0.98 is
ok for an optimized PCR reaction. The slope of the standard curve can be analyzed and reaction
efficiency can be evaluated over a range of different concentrations. Normal human liver cDNA
(BD biosciences, #636742) was used for the standard curve for NDRG2 expression and normal
human heart cDNA (BD biosciences, #636742) was used for RPLP0. The standard curve for MCL-1
was prepared from human colon cancer cell line HCT116.
Gene Forward Primer 5’ 3’ Reverse Primer 3’ 5’ Tm (°C)
NDRG2 GCTACAACAACCGCCGAGAC ACAGGCGAGTCATGCAGGAT 55
RPLP0 GCTTCCTGGAGGGTGTCC GGACTCGTTTGTACCCGTTG 52
MCL-1 TAAGGACAAAACGGACTGG CCTCTTGCCACTTGCTTTTC 60
47
3.4 Detection of NDRG2 protein expression
Methods to detect NDRG2 protein are described in the following sections. These include
transfection, whole cell protein extraction, and Western blotting. The NDRG2 protein levels were
investigated in the cancer cell lines HCT116 and SW480. Furthermore, these cells were transfected
with three different plasmids, pCMV-EGFP, pcDNA6V5-HIS-A-EGFP and pcDNA6-NDRG2L-V5, see
appendix II. After the transfection the NDRG2 protein level was investigated again. All further
information on the antibodies and solutions used in the study can be found in Appendix I.
3.4.1 Transfection
Specific DNA is transfected into cells to control
the gene expression. In stable transfection, the
DNA is introduced into cells long-term. The
plasmid used for transfection contains the gene
of interest and a blasticidin resistance gene.
Selection is possible through blasticidin
treatment, because of the blasticidin resistance
gene. The process of transfection can be seen
in Figure 16.
For this project the transfection was prepared
by plating cells at a density of 3x105 per well in
6-well plates for 24 hours. Hereafter they were
ready for transfection, which was conducted
with three different plasmids; pCMV-EGFP, pcDNA6V5-HIS-A-EGFP and pcDNA6-NDRG2L-V5.
Transfection mastermix was prepared by diluting 1 μg of plasmid in 100 μL McCoy’s 5A medium. 2
μL gently vortex Turbofect Transfection Reagent (Thermo Scientific, #R0539) was added to the
diluted plasmid. The transfection mastermix was resuspended and incubated at RT for 20 minutes.
100 μL Transfection mastermix was added drop-wise to each well, and the plate was gently
rocked. The plate was then incubated at 37°C with 5% CO2.
Figure 16 shows the transfection step by step [Ibidi, 2014]
48
After 24 hours, the transfection was controlled and medium changed to ensure the best growth
conditions for the cells. Control of the transfection was performed by use of fluorescence
microscopy (Leica DMIRB) and the cells tranfected with the pCMV-EGFP plasmid. Hereafter, the
transfected cells were grown in a selected medium for at least 14-21 days, and 12 μL (1mg/ml)
blasticidin S HCI (Invitrogen, #46-1120) was used for selection.
When the cells had been exposed to the selection factor for sufficient time, they were harvested
by the followed procedure: washing with 1 ml PBS, trypsinizing with 100 μL trypsin for 5 minutes,
resuspension in 900 μL McCoy’s 5A medium, and again resuspended in 1 ml PBS and spun down at
14000 rpm for 5 minutes. Supernatant was removed and the pellet was stored at -80°C.
3.4.2 Whole cell protein extraction
To isolate protein from whole cells, a whole cell protein extraction method was used. All steps
were performed on ice.
Lysis Buffer++ was prepared using 1 ml Lysis Buffer (see Appendix I for the lysis buffer solution), 2
μL 0.5M DTT, and 2 μL protease inhibitor (Sigma-aldrich, #P8340). The cell pellet was resuspended
in 30 μL Lysis Buffer++ and incubated on ice for 30 minutes. Then the sample was spun down at
14000 x g for 10 minutes at 4°C. Supernatant containing the protein fraction was transferred to a
new tube and stored at -80°C until use.
3.4.3 Western Blot
Western Blotting is used to investigate the expression of a specific protein. Proteins are separated
by size through gel electrophoresis. Proteins are then transferred to a membrane, and by using
primary and secondary antibodies the proteins can be detected by the chemiluminescent
detection method. The western blot is incubated with substrate that will luminesce when exposed
to the reporter on the secondary antibody, and by use of Biospectrum® imaging system (UVP) the
light makes the protein visible, see Figure 17.
49
3.4.4 Protein measurement
Protein content was measured by use of Bradford standard curve assay. Bradford is an acidic
solution of Coomassie Brilliant Blue dye, which interacts with the protein and changes the
absorbance from 465 nm to 595 nm. This absorbance shift can be measured by a
spectrophotometer to determine the protein concentration in the samples. The standard curve
was prepared based on known concentrations of Bovine Serum Albumin (BSA). The concentration
of unknown proteins can be determined by the absorbance given from the standard curve at
known concentrations. See Appendix I for the Bradford solution.
The standard curve was established by: 1 ml Bradford solution and varied concentrations of BSA of
0, 1, 2, 4, 6, and 8 μg/μL. All samples were prepared in duplicates and incubated in darkness for 10
minutes. The concentrations were then measured at the spectrophotometer (Biophotometer from
eppendorf) at 595 nm.
Protein samples with unknown concentrations could now be measured. All samples were
prepared by adding of 1 ml Bradford solution and 2 μL protein lysate. A blank sample was
prepared by use of 2 μL Lysis Buffer instead of protein. Then all samples were incubated at 10
minutes in complete darkness and measured on the spectrophotometer at 595 nm.
3.4.5 Separation of proteins by gel electrophoresis
All protein samples were prepared by mixing 30 μg protein, 3.75 μL 4X Nupage® LDS sample Buffer
(Invitrogen, #46-5030), 1.5 μL 0.5M DTT, and H2O to a total of 15 μL. Nupage® LDS sample Buffer
had been incubated at 30°C before use. Before loading the gel, all samples were heated at 70°C for
Figure 17 shows the antibody binding and the luminesce of light [Advansta, 2014]
50
10 minutes. Nupage® 4-12% Bis-Tris Gel (Novex, #NP0322BOX) was prepared while the samples
were heated. First, the comb was gently removed from the wells, then the wells were washed by
deionized water and the tape was peeled off. The gel was placed in an XCell Surelock System and
the chambers were filled with Running Buffer. The samples and markers were loaded and the gel
was run at 200V for 1 hour. The marker used was PageRuler™ prestained protein ladder
(Fermentas, #SM1811).
3.4.6 Transfer of proteins by electro-blotting
The membrane was prepared before the end of the gel run. All blotting pads were soaked in
Transfer Buffer. The membrane was soaked in 96% ethanol for 30 sec, flushed with deionized
water, and then placed in the transfer buffer until use.
After the gel electrophoresis, the gel was separated from the cassette and the XCell II Blot module
was prepared. All blotting pads were squeezed to remove air bubbles, filter paper was soaked in
transfer buffer shortly before use, and blotting pads, filter paper, membrane, and gel was pressed
together in layers as illustrated in Figure 18.
When the XCell II Blot Module was prepared, it was filled with Transfer buffer and ran at 25V for 1
Hour.
Figure 18 shows the composition in the XCell II Blot Module
51
3.4.7 Antigen binding
After transfer, the membrane was separated in two. Both parts were loaded with the same
samples, and detection of two different proteins could therefore be made at the same time.
Membranes were washed briefly in transfer buffer and then incubated in 20 ml blocking buffer for
30 minutes at RT. A rocking table was used at all steps to ensure full cover of the membranes.
Membranes were then washed 2 x 2 minutes in 20 ml Wash buffer. The primary antibody was
diluted in Dilution buffer and then added to the membranes after wash. The membranes were
incubated overnight with the primary antibodies at 4°C. All solutions can be found in Appendix I.
The secondary antibody was also prepared and diluted in Dilution buffer. The next day, the
membranes were washed 2 x 10 minutes in 20 ml Wash buffer. The secondary antibody was
added, and the membranes were incubated for 1 hour at RT. After incubation the membranes
were washed 4 x 10 minutes in 20 ml Wash buffer. Antibodies and dilutions can be found in
appendix I.
3.4.8 Protein detection
To detect the proteins the membranes were incubated for 10 minutes with 1 ml SuperSignal ®
West Dura Extended Duration Substrate mix (Thermo Scientific, #34076) for each membrane. In
order to obtain the best results, the incubation had to be performed in completely darkness and
the proteins were detected by photography of the membranes by use of Biospectrum® imaging
system (UVP).
52
3.5 Quantification of cell proliferation under varied conditions
The description of growth assays can be found in the follow section.
The growth assays were performed with cancer cell line SW480 with both +/- NDRG2 expression
and +/- IL-6 treatment. Only the cell line SW480 was selected for further work, based on the
undetectable level of NDRG2 expression in transfected HCT116 cells.
3.5.1 Growth assay for transfected cancer cell line SW480
The growth rate for the transfected cancer cell line SW480 (pcDNA6V5-His-A-EGFP plasmid) and
SW480 (pcDNA6-NDRG2L-V5 plasmid) were analyzed to define the growth of the cell line without
NDRG2 and with NDRG2. Because the pcDNA6V5-His-A-EGFP plasmid does not contain NDRG2,
the cells are considered to grow as normal SW480 cells. All cells were plated at a density of 1x104
cells per well in 24-well plates, and all samples were conducted in triplicates. The cells were
harvested and counted over a time period from 24-168 hours. The counting was performed by use
of Beckmann Coulter Z2 Particle Count and size Analyzer, which had to be cleaned before use by
primer and flushed with 10 ml 0.9 % biological salt water. Hereafter a blank sample consisting of
10 ml 0.9 % biological salt water was counted, and now the unknown samples consisting of 200 μL
sample diluted in 10 ml 0.9 % biological salt water could be counted.
3.5.2 Growth assay for transfected cancer cell line SW480 treated with IL-6
The growth rates for the transfected cancer cell line SW480 (pcDNA6-NDRG2L-V5 and pcDNA6V5-
His-A-EGFP plasmid) were analyzed in order to define the effect of IL-6 with NDRG2 and without.
The cells were plated in duplicate at a density of 1x104 cells per well in 24-well plates. After 24h
the cells were treated with IL-6 and set to grow for a further 24h, 96h, and 168h before being
harvested. The counting was performed by use of Beckmann Coulter Z2 Particle Count and size
Analyzer as described above.
53
3.6 Statistic
Some of the results could be evaluated through statistical tests, and student’s t-test and F-test
were used. The F-test is used to compare the variance between two data sets and can only be
used in normal distributed populations, and high values of F indicate a difference between the
dispersions in the two populations. All F-values over 0.05 were not significant and reversed. The
student’s t-test can be used to study whether average values in two populations can be presumed
to be identical. It is a condition for the use of the t-test that the populations are normal
distributed. Different t-tests are available depending on the result of the F-test. If the variance was
significant for the F-test, the populations were assumed to be equal. Thereby two different t-tests
were available; paired t-test for two populations with equal variance, and unpaired t-test for two
populations without equal variance. Both were used in this study and like the F-test, all t-values
above 0.05 were considered not significant.
54
4. Results
Results obtained in order to evaluate the hypotheses of the study are examined and analyzed in
the following sections.
4.1 NDRG2 expression in cancer cell lines SW480 and HCT116 at the mRNA
level
Previous studies have reported a down-regulation of NDRG2 expression in colorectal cancer and
several other types of cancer [Chu et al., 2011; Hu et al., 2004; Lee et al., 2008; Lorentzen et al.,
2007; Piepoli et al., 2009; Lorentzen et al., 2011; Zhao et al., 2008; Ma et al., 2008; Wang et al.,
2012], and therefore it could be interesting to examine if the same tendency could be seen in
human cancer cell lines SW480 and HCT116. This result was important for further analyzes
through the study and was analyzed by use of qRT-PCR.
To be able to evaluate the NDRG2 expression level in the cell lines, a tissue with a known high
expression of NDRG2 was used for normalization. Normal brain tissue was selected for the
normalization, because brain tissue express high levels of NDRG2 [Deng et al., 2003]. A reference
gene, RPLP0, was used and worked as an invariable endogenous control. The results of the
examination of NDRG2 mRNA expression in the cell lines SW480 and HCT116 are illustrated in
Figure 19.
Figure 19 shows the expression levels of NDRG2 mRNA in cancer cell lines SW480 and HCT116 normalized to normal brain tissue. Columns represent the fold expression of NDRG2 mRNA expression. Standard deviations are included on the graph
When the expression of NDRG2 mRNA was compared with normal brain tissue levels, the results
show low expression of NDRG2 mRNA in cell lines SW480 and HCT116. In order to evaluate the
00,20,40,60,8
11,2
Brain SW480 HCT116
NDR
G2
mRN
A ex
pres
sion
55
obtained results, a student’s t-test was used to analyze the observed difference between normal
brain tissue and the cell lines and to conclude if the difference was significant. All calculated p-
values can be seen in Table 5, and all values below 0.05 mean that the differences were significant.
Based on the observed results and p-values, NDRG2 mRNA expression was significantly lower in
both cell lines when compared to normal brain tissue.
Table 5 shows the p-values for the student’s t-test. Down-regulation of NDRG2 were significant for both cell lines, because of p-values below 0.05
4.2 NDRG2 expression in cancer cell lines SW480 and HCT116 at the
protein level
Previous studies have reported decreased levels of NDRG2 protein in different cancer types and
cancer cell lines. The precise biological function of the NDRG2 protein is still unknown, but
expression of NDRG2 protein has been observed to decrease the growth of cancer cells [Kim et al.,
2009]. Even though NDRG2 mRNA was low in both cell lines, this may not affect the expression of
NDRG2 protein. Therefore, it was of interest to examine the levels of NDRG2 protein in both cell
lines to see if the same tendency, as seen in previous studies, exists in these cell lines.
Furthermore, the presence of NDRG2 was important for analyses of the growth rates of the cell
lines.
The protein level of NDRG2 could be examined and evaluated through extraction of total protein
from both cell lines and western blotting. Before the cell lines could be examined and evaluated,
they was transfected with an empty plasmid, pcDNA6V5-His-A-EGFP, which was assumed not to
effect the normal NDRG2 protein level. This assumption could be confirmed by western blotting.
The plasmid was constructed with a V5 and histidine-tag, which is approximately 2000 dalton ≈ 2
kilodalton (kDa), and this construction made it possible to perform the analyses with both NDRG2-
and V5 specific antibodies. The samples of interest were named by the cell lines name with V5
added in the end. One positive control was included, which has previously shown NDRG2 protein
Cell line p-value SW480 2.4 x 10-15
HCT116 1.1 x 10-17
56
expression. The results of the western blotting with specific NDRG2- and V5 antibodies are
illustrated below in Figure 20.
Membrane A was analyzed with specific NDRG2 antibody, and if the cell lines expressed NDRG2
protein, the expected band would appear at approximately 41 kDa. All samples in membrane A
named with a V5 in the end did not show any bands at all, which means that both cell lines did not
express NDRG2 protein, and that the empty vector pcDNA6-V5-HIS-A-EGFP did not affect the
expression of NDRG2. Furthermore, the positive control only showed a clear band at the
approximate size for NDRG2. Membrane B was analyzed with a specific V5 antibody, but because
the V5-His-tag is only around 2 kDa it is not visible and therefore no bands were expected. Again
the samples with a V5 in the end were of interest, and the result of the analysis with the specific
V5 antibody did not show any bands as expected.
Based on these results it must be concluded that neither of the cell lines express NDRG2 at the
protein level, and therefore a transfection with a plasmid containing NDRG2 was necessary for
further analysis of how the presence of NDRG2 protein affects the growth rate, and how IL-6
treatment affects the NDRG2 expression.
Figure 20 shows the two membranes from the Western blotting result with SW480 and HCT116 cells. Picture A, shows the membrane analyzed with NDRG2 specific antibody and Picture B, shows the membrane analyzed with V5 specific antibody
57
4.3 Analysis of the most efficient concentration and time for the treatment
with IL-6
In order to optimize the treatment with IL-6, the cell lines were treated with different
concentrations over a given time period. This was important for optimal treatment of the cell
lines, when the effect of IL-6 on NDRG2 expression and growth rate was evaluated.
In order to find the most efficient concentration and time, the following concentrations of 10 ρM,
50 ρM, and 10,000 ρM of recombinant human IL-6 were used over a time period of 72 hours.
When IL-6 is used for treatment, it is known to affect the activation of the JAK/STAT pathway,
which normally leads to increased expression of Mcl-1 [Grivennikov et al., 2009; Jourdan et al.,
2003]. Therefore, to be able to estimate the efficiency of the IL-6 treatment, the mRNA expression
level of Mcl-1 in human cancer cell lines SW480 and HCT116 was also analyzed. In preparation,
cDNA was synthesized and then analyzed by qRT-PCR. The expression levels of Mcl-1 were
normalized to RPLP0.
Figure 21 shows the expression levels of IL-6 mRNA in cancer cell lines SW480 at different concentrations and times. Columns represent the fold expression of Mcl-1 mRNA expression. Standard deviations are included.
The obtained levels of Mcl-1 mRNA after treatment of SW480 cells with different IL-6
concentrations are illustrated in Figure 21. The highest fold increase of Mcl-1 mRNA expression
was observed after treatment with 10,000 ρM of IL-6 for 72 hours.
When HCT116 cells were treated with different concentrations of IL-6, the obtained levels of Mcl-1
mRNA were different from the result with SW480 cells. The results are illustrated in Figure 22
0
5
10
15
24 48 72
Mcl
-1 m
RNA
fold
incr
ease
Hours
SW480
10 ρM
50 ρM
10000 ρM
58
below, and they show that HCT116 cells express more Mcl-1 mRNA, when treated with 50 ρM of
IL-6 for 72 hours.
Figure 22 shows the expression levels of IL-6 mRNA in cancer cell lines HCT116 at different concentrations and times. Columns represent the fold expression of Mcl-1 mRNA expression. Standard deviations are included
Therefore, all further treatments of the cell lines SW480 and HCT116 were made with 10,000 ρM
for SW480 cells and 50 ρM for HCT116 cells respectively in order to obtain the most efficient
treatment.
4.4 NDRG2 expression in treated cancer cell lines SW480 and HCT116 at
mRNA level
Association between IL-6 and colon cancer has been observed in previous studies, where elevated
levels of IL-6 in serum were reported in patients with colon cancer and have been further
connected with prognosis and survival. Furthermore, IL-6 has been connected with promotion of
proliferation and invasion in colon cancer [Hsu et al., 2011; Nikiteas et al., 2005; Chung & Chang,
2003; Hsu & Chang, 2006; Becker et al., 2004]. All these connections together with the observed
functions of NDRG2 in colon cancer, makes it interesting to examine if NDRG2 expression is
affected by elevated levels of IL-6.
The expression of NDRG2 mRNA was evaluated in both cell lines when treated with appropriate
concentrations of recombinant human IL-6. Expression levels of NDRG2 mRNA in treated cell lines
were examined using qRT-PCR to evaluate if IL-6 treatment had any influence on the expression
level of NDRG2 mRNA. All expression levels were normalized to the reference gene, RPLP0. The
results of the qRT-PCR analysis are illustrated in Figure 23.
0
20
40
60
24 48 72
Mcl
-1 m
RNA
fold
incr
ease
Hours
HCT116
10 ρM
50 ρM
10000 ρM
59
Figure 23 shows the expression levels of NDRG2 mRNA in cancer cell lines SW480 and HCt116 treated and untreated with IL-6. Columns represent the fold expression of NDRG2 mRNA expression. Standard deviations were included in the figure
The results of the analyses show that the expression of NDRG2 mRNA in both cell lines was
increased after treatment with IL-6. To evaluate if the observed increase was significant, a
student’s t-test was used. The p-values for both cell lines show that the small increases observed
in NDRG2 mRNA expression for treated cells were not significant, when compared with untreated
cells. The p-values can be seen in Table 6.
Table 6 shows the p-values. All values over 0.05 indicate no significantly difference between treated and untreated SW480 and HCT116 cell lines
These results indicate that elevated IL-6 levels in both cell lines did not affect the expression of
NDRG2 mRNA levels.
0
0,0002
0,0004
0,0006
0,0008
0,001
0,0012
SW480 treated SW480 untreated HCT116 treated HCT116 untreated
NDR
G2
mRN
A fo
ld in
crea
se
Cell lines p-value
SW480 0.237
HCT116 0.402
60
4.5 Analysis of transfection efficiency
The results of western blotting with an empty plasmid showed that none of the cell lines express
NDRG2 protein. In order to examine if the presence of NDRG2 affects the growth rate in the cell
lines, both cell lines were therefore transfected with the following plasmids; pcDNA6V5-His-A-
EGFP, pcDNA6-NDRG2L-V5, and pCMV-EGFP. A plasmid containing enhanced green fluorescence
protein (EGFP) was used to verify transfection efficiency. EGFP functions as a powerful reporter
molecule for estimation of transfection efficiency. All cells who have obtained the plasmid will
fluoresce, when exposed to light by a fluorescence microscope camera at 470 nm. A picture of
cells transfected with pCMV-EGFP plasmids is shown in Figure 24.
The protocol for the transfection kit recommends transfection efficiency of minimum 10% before
the work with the cells continues. When cells were exposed to fluorescence, around 40% were
positive for fluorescence, and the transfection efficiency was approved for further work.
4.6 Analysis of NDRG2 protein level after transfection
In order to evaluate if the transfection had been successful, the protein level of NDRG2 was
analyzed through western blotting. If the cell lines do not express any NDRG2 protein, it does not
necessarily mean that the transfection was unsuccessful, because the plasmid can be incorporated
in parts of the genome, which are not activated. Then no NDRG2 protein will be detectable, but it
is possible to perform selections, which show if the plasmid has been obtained. The result was
important for further analysis in order to evaluate the effect of the presence of NDRG2 in the cell
Figure 24 shows pictures of the EGFP-transfected cell samples obtained with and without a fluorescence microscope, the two pictures are not from the same day and comparison cannot be made
61
lines. In this western blot, only plasmid pcDNA6-NDRG2L-V5 was examined because of the
conduction with NDRG2. The plasmid contained the long version of NDRG2 protein and was
approximately 41 kDa. A V5-his-tag was coupled to NDRG2L and functioned as positive control for
the presence of NDRG2. Therefore, proteins detected with a specific V5 antibody were expected
to be approximately the same size as proteins detected with a specific NDRG2 antibody. The
results of the western blot can be seen below in Figure 25, which includes both membranes
analyzed with NDRG2 antibody and V5 antibody, respectively. The samples of interest were named
with NDRG2 in the end.
The results of the analysis with specific NDRG2 antibody are shown in membrane A and only the
sample with SW480 cells showed an intense band at approximately 40-45 kDa, which was
consistent with the expected size of NDRG2 protein. The fact that the sample with HCT116 cells
did not show any band does not necessarily mean that the transfection was not successful, which
was based on a successful selection. This can be explained by the fact that the plasmid with
NDRG2 could be taken up by the cells without being activated in the genome. Membrane B was
analyzed with a specific V5 antibody, where the V5-tag coupled to NDRG2L function as a positive
control for the presence of NDRG2. This analysis showed the same observations as seen in
membrane A and the only difference was that the observed band for the sample with SW480 cells
Figure 25 shows the result of western blotting and all samples named with N (NDRG2) are of interest. The picture (A) show the membrane analyzed with NDRG2 antibody and picture (B) show the membrane analyzed with V5 antibody. Only samples named with NDRG2 in the end are of interest
62
was weaker when analyzed with the V5 antibody, but approximately the same size as NDRG2
protein.
Because HCT116 cells did not express NDRG2 protein after the transfection, all further work was
continued with SW480 cells expressing NDRD2 protein.
4.7 Growth assays comparing cancer cell lines SW480 growth rate +/-
NDRG2
To examine if the presence of NDRG2 in the cells affect the growth rate, growth assays were
performed with transfected SW480 cells +/- NDRG2 in order to compare the growth rates. In
previous studies, the presence of NDRG2 has been associated with decreased growth, and
therefore it would be interesting to see if the same observations could be seen in SW480 cells.
Transfected SW480 cells +/- NDRG2 were grown over a time period at 168 hours and counted
every day. Figure 26 illustrates the growth rates for transfected SW480 cells +/- expression of
NDRG2. The highest increase in growth rate was observed after 168 hours. The observed
difference in growth rates was around 60 % after 168 hours of growth, when the growth rates for
SW480 cells +/- NDRG2 were compared.
Figure 26 shows the result for the growth assay performed with SW480 cell line +/- NDRG2. Small standard deviations are illustrated
0
200000
400000
600000
800000
1000000
24 48 72 96 120 144 168
Cells
per
ml
Hours
SW480
Untreated SW480 cells with NDRG2
Untreated SW480 cells without NDRG2
63
The measured p-values further support the results illustrated in Figure 26 by showing a significant
difference in growth rates between SW480 cells +/- NDRG2, see Table 7. The significant difference
was first observed after 96 hours and up to 168 hours.
Table 7 shows the F-values and p-values for SW480 growth assay +/- NDRG2. Significant differences (values under 0.05) are seen for the cells growing over 96 hours up to 168 hours
Therefore, the conclusion of the growth assay is that cells with NDRG2 show increased growth
rate, when compared with cells without NDRG2.
4.8 Growth assays comparing growth rates for SW480 cells untreated or
treated with IL-6
Based on the knowledge from previous studies, which have shown a correlation between
increased levels of IL-6 and proliferation in colon cancer, it would be interesting to examine if IL-6
affects the growth rate of SW480 cells.
The cell line was treated with a concentration of IL-6 selected based on knowledge from previous
analyses in this study, over a time period at 168 hours. Figure 27 illustrates the results of the
growth assays, where the highest increase was observed after 96 hours of treatment and a smaller
increase was seen after 168 hours of treatment. Treatment of the cells at 24 hours did not show
any difference in growth rates, which indicate that IL-6 does not have any effect on the growth
rate, when treated for less than 24 hours.
Hours of growth p-value
24 0.408
48 0.052
72 0.241
96 0.003
120 6.2 x 10-4
144 0.001
168 5 x 10-4
64
Figure 27 shows the result of the growth assay with untreated and treated SW480 cells. Small standard deviations were seen
The p-values show that the observed increase in growth rates after 96 and 168 hours in treated
SW480 cells was significant, when compared with untreated SW480 cells, see Table 8.
Hours of growth p-value
24 0.318
96 0.003
168 0.022
Table 8 shows the P-values for untreated and treated SW480 cells
Therefore it can be concluded that treatment of SW480 cells with IL-6 does affect the proliferation
significantly after at least 96 hours of treatment.
4.9 Growth assay comparing transfected cancer cell line SW480 treated
and untreated with IL-6
In order to examine how SW480 cells grow in the presence of NDRG2 and elevated levels of IL-6,
the transfected cells were treated with IL-6 and comparison of the growth rates were made with
untreated SW480 cells. In previous analyses through this study, both NDRG2 and IL-6 were
observed to increase the growth rates, and the same concentration as previously was used for the
treatment over a time period at 168 hours.
The results of this growth assay are illustrated in Figure 28, where an increased growth rate was
observed for transfected SW480 cells after at least 96 hours of treatment with IL-6. The biggest
0100000200000300000400000500000600000
24 96 168
Cells
per
ML
Hours
SW480
Treated SW480 cells
Untreated SW480 cells
65
difference between untreated and treated SW480 cells was seen after 96 hours, and the highest
increase was observed after 168 hours of treatment, which was confirmed by the p-values
showing significant difference between treated and untreated SW480 cells at all times, see Table
9.
Figure 28 shows the results for growth assays with SW480 cells containing NDRG2 and treated with and without IL-6. Small standard deviations are illustrated
This indicates that the presence of NDRG2 together with treatment with IL-6 increases the growth
rate significantly for SW480 cells.
Hours of growth p-value
24 0.049
96 1 x 10-5
168 0.0001
Table 9 shows the p-values
0200000400000600000800000
1000000
24 96 168
Cells
per
ml
Hours
SW480 cells
Untreated SW480 cells with NDRG2
Treated SW480 cells with NDRG2
66
4.10 Short summary
Based on the results in this section, it was observed that both SW480 and HCT116 cells did not
show natural expression of NDRG2 at both mRNA and protein level, and further that treatment
with IL-6 did not affect the expression of NDRG2 at mRNA level. Through transfection with a
plasmid containing NDRG2 it was possible to get SW480 cells to express NDRG2 protein, which
was required for most of the remaining analyses through this study. The presence of NDRG2 in
SW480 cells affected the growth rate by increasing it, and the same tendency was observed in
SW480 cells treated with IL-6, which did not express NDRG2. Furthermore, the presence of NDRG2
and increased levels of IL-6 combined affected the growth rate for transfected SW480 cells by
increasing the proliferation.
67
5. Discussion
Cancer is a leading cause of death, and more than 30 million people are living with the disease
worldwide [WHO, 2014]. Therefore research of the molecular mechanisms behind tumorigenesis
is of great importance in order to elucidate, how to treat and prevent the events of tumorigenesis.
Various mechanisms have been connected to cancer development and growth including emerging
hallmarks and enabling characteristics, which describe the relationship between the immune
system and cancer and genomic changes behind tumorigenesis [Hanahan & Weinberg, 2011]. One
of the genomic aspects in cancer development is tumor suppressor genes, such as the newly found
tumor suppressor gene, NDRG2, which has been reported to be down-regulated in several cancer
types and epigenetically modulated [Piepoli et al., 2009; Chu et al., 2011; Hu et al., 2004; Lee et
al., 2008; Lorentzen et al., 2007; Piepoli et al., 2009; Lorentzen et al., 2011; Zhao et al., 2008; Ma
et al., 2008; Wang et al., 2012]. Other important mediators of tumorigenesis are inflammatory
factors including the cytokine IL-6, which has been connected with induction of progression,
growth, and invasiveness in human colon carcinoma [Hsu et al., 2011; Hsu & Chung, 2006].
Through this study human cancer cell lines HCT116 and SW480 were used to validate the
previously observed down-regulation of NDRG2 in colon cancer, and further to investigate if IL-6
had any impact on the NDRG2 expression and growth rate.
5.1 The demonstration of the observed down-regulation of NDRG2
expression
In both cells lines, the NDRG2 expression at mRNA level was compared with the expression in
normal brain tissue. In previous studies with colon cancer tissue and different cell lines, a down-
regulation of the NDRG2 expression had been reported [Feng et al., 2011; Lorentzen et al., 2007;
Chu et al., 2011; Kim et al., 2009]. Consistent with these findings obtained by other groups, the
result of this study showed a significantly lower NDRG2 expression in HCT116 and SW480 cancer
cell lines, when compared to normal brain tissue. A possible explanation of the observed low
expression of NDRG2 could be the elevated evidence of transcriptional silencing of NDRG2
through hypermethylation of NDRG2 promoter region [Feng et al., 2011]. Furthermore, studies
with colon cancer have shown a significant increase of NDRG2 mRNA expression after treatment
68
with a demethylating agent, which indicates that methylation, can affect the expression of NDRG2
mRNA [Piepoli et al., 2009; Feng et al., 2011].
Proteins have many distinct biological functions. They are the resulting product of the gene
expression, and therefore the protein expression of NDRG2 is of interest. In order to investigate
the protein level, protein extraction was performed and analyzed by western blotting. The
obtained results show that none of the cell lines express NDRG2 protein, and this observation is
supported by other studies with colon cancer cells [Shi et al., 2009; Lorentzen & Mitchelmore,
2012]. miRNAs have been connected with regulation of gene expression through
posttranscriptional silencing by binding to the 3’ untranslated region of mRNA [Baer et al., 2013;
Sharma et al., 2010]. Feng et al. have investigated miRNAs impact on the expression of NDRG2 and
found that down-regulation of NDRG2 was inversely associated with up-regulation of miRNAs
[Feng et al., 2011]. This may indicate a possible role in aberrant expression of NDRG2 mRNA and
protein, because of miRNAs ability to inhibit protein synthesis and RNA degradation [Baer et al.,
2013; He & Hannon, 2004]. Furthermore, miRNAs have been associated with biological processes
as proliferation, apoptosis, and differentiation, also supporting a potential role in tumorigenesis
[Saito & Jones, 2006].
5.2 Possible impact of IL-6 on the expression of NDRG2
In order to elucidate the impact of IL-6 on NDRG2 expression, both cell lines were transfected with
plasmid (pcDNA6-NDRG2L-V5) containing NDRG2 to ensure NDRG2 expression and then treated
with recombinant human IL-6. The doses used were determined and modified based on a previous
published study, where the authors have treated colon cancer cells with IL-6 [Hsu & Chung, 2006].
As already mentioned IL-6 functions as a key activator of the JAK/STAT pathway leading to
activation of transcription factor STAT3. The expression of Mcl-1 was used to measure the
efficiency of the treatment with IL-6, because Mcl-1 expression is induced by STAT3. The most
efficient doses for treatment of both cell lines were high, when compared with normal levels of IL-
6 in humans. Becker et al. have showed that treatment with hyper-IL-6 (IL-6 and covalently linked
soluble IL-6 receptor) affect proliferation and phosphorylation of STAT3. Additionally, they showed
a down-regulation of IL-6R in colon cancer patients [Becker et al., 2004]. This down-regulation of
69
IL-6R may affect the activation of the JAK/STAT pathway and thereby the expression of Mcl-1,
which could explain the necessity of high doses of recombinant human IL-6 for treatment.
NDRG2 expression has been reported to modulate SOCS3 and STAT3 activity and inhibit the
production of the cytokine IL-10, which is strongly connected with IL-6 through the JAK/STAT
pathway [Lee et al., 2010; Hamidullah et al., 2012]. Because of the important role of IL-6 in the
activation of the JAK/STAT pathway, it may also be involved in the production and regulation of IL-
10 [Lee et al., 2010]. No studies have investigated IL-6 impact on NDRG2 expression in cancer, and
for that reason the results of this experiment are completely unknown. In both cell lines a small
increase in NDRG2 mRNA were observed after the treatment with IL-6, but the observation were
not significant. This may indicate that IL-6 does not have an influence on the regulation of NDRG2
expression, but further knowledge of NDRG2’s impact on IL-6 is needed to be able to elucidate a
possible connection.
5.3 The growth rate of cancer cell lines and the influence of NDRG2
expression
To investigate how NDRG2 expression affects the growth of colon cancer cell lines, a growth assay
was performed. Normally the NDRG2 expression is transcriptionally regulated by MYC, which is
important for cell proliferation and differentiation [Dang et al., 2008; Wierstra & Alves, 2008]. In
colorectal cancer an increase in the MYC level has been observed together with a decrease in
NDRG2 expression, which may indicate that MYC is able to repress human NDRG2 [Shi et al.,
2009]. Furthermore, several studies with cancers have reported a down-regulation of NDRG2 and
suppression of proliferation and induced apoptosis through regulation of cyclin D1 and T cell
factor/β-catenin activity, all supporting a potential role for NDRG2 as an inhibitor of cancer cell
proliferation [Shi et al., 2009; Lorentzen & Mitchelmore, 2012; Yao et al., 2008; Chu et al., 2011;
Zheng et al., 2010; Liang et al., 2012]. The results of the growth assay does not consist with other
observations, in that a significantly increased growth rate was observed in both cell lines, when
NDRG2 was present. Because HCT116 did not show any protein expression of NDRG2 after the
transfection, the result of the growth assay for this cell line was unclear. The increased growth
rate for SW480 cell line may be explained by the fact that other studies with colon cancer cell lines
have shown that an increase in expression of MYC leads to a decrease in the expression of NDRG2.
70
This may lead to increased growth rate, because NDRG2 expression has been correlated with
proliferation [Shi et al., 2009; Zhang et al., 2006]. In order to evaluate this theory, the expression
of MYC has to be examined in SW480 cells.
5.4 The growth rate and the influence of IL-6 treatment
To elucidate if IL-6 has an impact on the growth rate of the colon cancer cell line SW480, two
growth assays were performed; one with the presence of NDRG2 and one without. Studies have
shown an association between elevated levels of IL-6 in patients with colon carcinoma and tumor
growth [Hsu et al., 2011; Hsu & Chung, 2006; Becker et al., 2004]. Also, IL-6 signaling regulates
many cellular functions, including growth, differentiation, and angiogenesis, and IL-6 has been
observed to increase the proliferation of colorectal carcinoma cells [Hirano et al., 2000; Jee et al.,
2004; Leu et al., 2003; Köbel et al., 2005; Lahm et al., 1992]. The results of the first growth assay
with no expression of NDRG2 showed an increase in growth rate, when compared with untreated
cells, and this result is consistent with other studies.
The second growth assay was performed with cells expressing NDRG2 to investigate if IL-6 and
NDRG2 together affect the growth rate. As mentioned earlier, NDRG2 is known to decrease the
growth rate of colon cancer cells, while IL-6 has been shown to increase the growth rate
previously in this study. The NDRG2-presenting cells treated with IL-6 showed a significant
increase in growth rate, when compared to cells without NDRG2. Based on previous results in this
study, both the presence of NDRG2 and treatment with IL-6 were expected to affect the growth
rate positively by an increase in proliferation. This is also consistent with the results. For further
analyzes it would be necessary to performed growth assays with NDRG2-presenting cell lines
treated with IL-6 and cell lines without NDRG2 present but still treated with IL-6 to investigate the
exact role of NDRG2 in IL-6 treated cell lines.
Through this study it was found that NDRG2 was down-regulated in colon cancer cell lines both at
mRNA and protein level, that IL-6 did not affect the NDRG2 expression at the level of mRNA, that
an increase in growth was seen both in cells with NDRG2 and treated with IL-6, and lastly that IL-6
may affect the growth rate positively when NDRG2 is present.
71
6. Conclusion
The present thesis has contributed with further evidence of the reported down-regulation of
NDRG2 expression in colon cancer cell lines. In the human cell lines HCT116 and SW480, a
significantly lower expression of NDRG2 was shown, when compared to expression levels in
normal human brain tissue. These findings correlated with an undetectable level of NDRG2 protein
in both cell lines. Treatment of both cell lines with IL-6 showed a small increase in NDRG2 mRNA
expression, but the increase was not significant, and further research is necessary in order to
investigate if IL-6 has an impact on NDRG2.
When NDRG2 was expressed in the human cell line SW480 through transfection, an increase in
growth rate was observed. These observations were not consistent with other studies, and they
do not support the theory of NDRG2 as a potential tumor suppressor gene. However, this thesis
was based on small experimental data sets, and this may affect the strength of the results.
Additionally, treatment of the human cell line SW480 with IL-6 caused a significantly increased
growth rate after 96 hours, which was consistent with other published results. In order to evaluate
if IL-6 treatment and presence of NDRG2 affected the growth rate of human cell line SW480, a
growth assay was performed and a significantly increased growth rate was observed, when NDRG2
was present in the cell line and treatment with IL-6 had been performed. This may indicate a
potential correlation between NDRG2 and IL-6, but further knowledge, larger experimental data
sets, and more research are needed.
72
7. Perspectives
At present, the exact mechanisms behind a possible connection between IL-6 and NDRG2 still
remains unknown. To be able to elucidate this matter and examine a possible connection, further
research and knowledge of both IL-6 and NDRG2s function in development, proliferation of cancer
cells, and regulation are required. Several different cancer types have been associated with down-
regulated NDRG2 expression, whereas IL-6 has only been associated with a few types, including
colon cancer. If the published findings of NDRG2 expressions involvement in the JAK/STAT
pathway in breast cancer are correct, it might be possible to investigate if this involvement also
appears to influence the progression of colon cancer cells. Additionally, NDRG2 expression has
already been connected with decreased proliferation of colon cancer cells, whereas the levels of
IL-6 have been reported increased in colon cancer cells.
A possible extension of the present work would be to optimize the experiment of the impact of IL-
6 on the NDRG2 mRNA level by examining the expression level of NDRG2 mRNA in normal colon
cancer cells in order to be able to compare the expression of NDRG2 mRNA with the presence in
colon cancer cells. Hereafter, treatment with IL-6 could be performed to see if the treatment
affects the expression of NDRG2 in normal colon cells. These studies would make it possible to
examine, if IL-6 has an impact on the NDRG2 expression in colon cancer cells, and thus provide
important information in order to develop new immunotherapeutic agents against colon cancer.
Although an association between NDRG2 expression and IL-6 treatment was not supported by
data obtained in the present thesis, findings in other studies indicate a potential role of TGF-β in
the connection between NDRG2 and IL-6 in colon cancer. Therefore, it would be interesting to
investigate if TGF-β affects NDRG2 expression and its function in inhibition of IL-6-trans signaling in
colon cancer.
73
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9. Appendices
9.1 Appendix I
Lysis Buffer
200 μL 1M Hepes (PH 7.9)
15 μL 1M MgCl2
4.2 ml 1M NaCl
2.9 ml 87% Glycerol
2.7 ml Milli-Q water
Bradford Reagent (AppliChem, #A3480,0010)
200 mg Coomassie Brilliant Blue
100 ml 96% Ethanol
200 ml 85% Phosphoric acid
Milli-Q water until 2 Liters
Transfer Buffer (Life technologies, #NP0006-1)
50 ml 20X NuPAGE® Transfer Buffer
200 ml 96% Ethanol
750 ml Milli-Q water
Blocking Buffer (GE healthcare, #RPN2125V)
5 % 20 ml PBS-T
1 g Blocker
Dilution Buffer 2.5% (GE healthcare, #RPN2125V)
20 ml PBS-T
0.5 g Blocker
Running Buffer (Life technologies, #NP0001)
50 ml 20X NuPAGE® MOPS SDS
950 ml Milli-Q water
PBS 10X (Ph 7.3)
1.4 M NaCl
27 mM KCl
101 mM Na2HPO4
18 mM KH2PO4
TE-Buffer
10 mM TrisCl (PH 8.0)
1 mM EDTA
TENS-Buffer:
100 mM TrisCl (PH 8.5)
5 mM EDTA
200 mM NaCl
0.2% SDS
Wash Buffer (PBS-T 0.01%) (Sigma-Aldrich, #P2287)
1 liter 1X PBS
1 ml Tween-2
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Antigens Molecular
Weight (kDa)
Primary antibody/
Secondary antibody
Dilution Supplier
NDRG2 41 Goat polyclonal IgG/
Donkey IgG-HRP
1:5000 Santa-Gruz Biotechnology,
#sc-19568/Santa-Gruz
Biotechnology, #sc2056
V5 14 + 41 (NDRG2) Rabbit monoclonal IgG/
Goat IgG-HRP
1:5000/
1:10000
Sigma-Aldrich, #V8137/Pierce,
#1858415
Β-actin 42 Mouse monoclonal IgG/
Goat IgG-HRP
1:5000 Sigma-Aldrich, #A5411/Pierce,
#1858413
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9.2 Appendix II
Plasmid pCMV-EGFP
Plasmid pcDNA6V5-His-A-EGFP
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Plasmid pcDNA6-NDRG2L-V5