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BS 592 M.Sc. Project Dissertation

DEVELOPMENT OF GLIOMA GENOME

AND PROTEOME DATABASE FOR

PATHWAY ANALYSIS

Project dissertation submitted by

NAVEEN PRAKASH BOKOLIA

09530022

In partial fulfillment of the requirements for the award of the

degree of Master of Science (Biotechnology)

Guide: Dr. Sanjeeva Srivastava

Department of Biosciences and Bioengineering

INDIAN INSTITUTE OF TECHNOLOGY, BOMBAY

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ACKNOWLEDGEMENT

I would like to express my sincere gratitude towards my supervisor Dr.

Sanjeeva Srivastava and for giving me an opportunity to work in their labs and

for guiding me throughout my project.

I would specially want to thank Dr. G. Subharmanyam and Dr. Ashutosh

Kumar for their kind support and guidance.

I would like to thank my lab mates Karthik, Shipra, Meghna and Renissa, for

helping me out throughout the project and also for making my stay in the lab

memorable.

I would also like to convey my regards to my batchmates Komal and Kishore

for making my stay at IITB a memorable one.

Finally, I express my sincere gratitude to my parents, my sisters and my friends

for their everlasting support.

NAME: Naveen Prakash Bokolia

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I. CONTENTS

1. INTRODUCTION

1.1 Glioma 10-11

1.1.1 Classification of Glioma or Tumor Grading 11-14

1.1.2 Genetic Alterations in the Glioma 14-15

1.2 Proteomics and Glioma 15

1.3 Databases

1.3.1 Need of Databases 15

1.3.2 Disease Database - Oncomine Database 15-16

1.3.3 Protein Interaction Database – HPRD 16

1.3.4 Need of Glioma Database 16

1.4 Pathway Analysis 16-17

1.5 Ingenuity Pathway Analysis Software 17

2. OBJECTIVES

2.1 Glioma Genome and Proteome Databse 17

2.2 Pathway Analysis/ Analysis of Database Containing Proteins and Genes 17-18

3. GLIOMA DATABASE

3.1 Davelopment Stages of the Glioma Database 18-19

3.2 Important features of the Glioma Database 19-21

4. MATERIALS AND METHODS FOR PATHWAY ANALYSIS

4.1 INGENUITY PATHWAY ANALYSIS SOFTWARE 21

4.2 DAVID Bioinformatics Resource 6.7 21

4.3 PANTHER (Protein ANalysis THrough Evolutionary Relationships) 21

4.4 Input Files 22

4.5 Software and Websites 22

5. OUTPUTS AS THE RESULTS FROM THE IPA: 23-24

6. RESULTS FROM THE IPA FOR EACH DATASET FILE:

6.1 Results for the High Grade 1 24-34

6.2 Results for the High Grade 2 35-37

6.3 Results for the Glioblastoma 37-39

6.4 Results for Astrocytoma 39-42

6.5 Results for Low Grade 42-43

6.6 Results for Validated High Grade 43-45

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7. ANALYSIS AND DISCUSSION PART - WITH THE HELP OF RESEARCH

ARTICLES:

7.1 Criteria for Analyzing Pathways with the Help of Research Articles 45-46

7.2 Pathways Already Well Established in the Glioma 46-54

7.3 Novel Information for Signaling Pathways Involved in the Glioma 54-68

7.4 New findings and New Correlations of Canonical Pathways with the Glioma 68-76

8. CONCLUSION 76-77

9. REFERENCES 77-86

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LIST OF FIGURES

Figure 1: Immunohistochemical Image of the Anaplastic Astrocytoma

Figure 2: Immunohistochemical Image of the Glioblastoma Tumor

Figure 3: Excel Image of the Glioma Genome and Proteome-Manual Curation Part

Figure 4: Pathways Categories

Figure 5: Canonical Pathways for the High Grade1

Figure 6: Canonical Pathways for the High Grade2

Figure 7: Canonical Pathways for the Glioblastoma

Figure 8: Canonical Pathways for the Astrocytoma

Figure 9: Canonical Pathways for the Low Grade

Figure 10: Canonical Pathways for the Validated High Grade

Figure 11: GBM Signaling

Figure 12: GBM Signaling from the Literature

Figure 13: PTEN Signaling

Figure 14: PI3K-AKT signaling

Figure 15: p53 Signaling

Figure 16: Glioma Signaling

Figure 17: Glioma Invasiveness Signaling

Figure 18: Integrin Signaling

Figure 19: Axonal Guidance Signaling

Figure 20: Semaphorins signaling in Neurons

Figure 21: IL-8 Signaling

Figure 22: IL-6 Signaling

Figure 23: HIF-1 Alpha Signaling

Figure 24: Feedback Regulation of PHD

Figure 25: Wnt-beta catenin signaling

Figure 26: Glucocorticoid Signaling

Figure 27: Newly Suggested role of Glucocorticoid Signaling in Glioma

Figure 28: Arachidonic Acid Metabolism

Figure 29: Newly Suggested Role of Upregulated Arachidonic Acid metabolism in

Glioma

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Figure 30: Role of NANOG in Embryonic cell Pluripotency

Figure 31: Sonic HEDGHOG Signaling

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Gliomas are the most common primary brain tumors that originate from the cancerous glial cells.

These glial cells are the mainly astrocytes, oligodendrocytes and Schwann cells. When the

genetic alterations occur in these types of cells they become cancerous. The degree of genetic

alterations varies from low grade to high grade or less malignant stage to the higher malignant

stage of glioma. The major objective of this project is to understand the various possible

molecular mechanisms and signaling pathways that leads to the glioma. Therefore the first part

of this project focused on manual curation of glioma data from the literatures to identify

candidate genes/proteins based on the genomics and proteomics studies. By manually curating

the data we extracted scientifically important information: genes, proteins, fold change values

techniques etc. After completion of this manual curation part we documented all the genes and

proteins information related to glioma and made a master table for further analysis. We obtained

the entire gene IDs and organized the data for further analysis by using various softwares. We

used Ingenuity Pathway Analysis (IPA), DAVID and PANTHER software to obtain the various

possible pathways from our genome and proteome dataset. The results obtained from this

analysis revealed that many signaling pathways are possible to build from our input genes and

proteins information. These pathways are ranked according to their p-values so that we can

obtain information for their significance. We further looked at the relevance of these signaling

pathways in the context of glioma and other diseases. For example, our results demonstrated

integrin and wnt-catenin signaling could be involved in glioma but we don’t know which type of

integrin or wnt plays role in the glioma. Furthermore, we modified few pathways by adding new

information from the research papers. Few pathways obtained in our study such as glioma

signaling, GBM Signaling and PTEN Signaling validated that our findings are similar to

published studies in literature but we did not perform further investigation about these pathways

since these are known targets. Our analysis and discussion is more focused on new potential

signaling pathways in glioma in the possible short time period. Finally, this study enabled us to

provide an overview of various pathways involved in glioma, find out new correlations and

connections as well as possible new roles of various signaling pathways in glioma.

1. INTRODUCTION

1.1 Glioma: Gliomas are the most common primary brain tumor which develops from the

cancerous glial cells, therefore called gliomas. There are several different kinds of glial

cells: astrocytes, oligodendrocytes and ependymal cells. Primary brain tumors are 1.4%

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among all the cancer types; and 2.4% deaths are only due to this type of cancer in the

United States. Where approximately 20,500 newly diagnosed cases come and 12,500

deaths are accounted every year due to the primary malignant tumor [Gladson et al.,

2010].

Unlike other cancers, glioma tumors grow in the defined space inside the head. So they

grow by pushing healthy cells and due to space limitation they kill healthy cells also. To

kill healthy nerve cells, glioma tumors release large quantities of the neurotransmitter

glutamate [Takano et al., 2001]. Large amount of glutamate is very toxic to neurons and

causes seizures in most of the people. Depending on the size of the tumor and location,

other symptoms also found like paralysis, behavior changes and dizziness. One major

difference between other cancer and glioma is that metastasis term is not used for the

glioma because malignant form does not affect or spread to the other organs in the body.

1.1.1 Classification of Glioma or Tumor Grading: Tumors are mainly graded based on

their microscopic appearances. The grade indicates the level of malignancy. Tumors

are graded based on their mitotic index (growth rate), vascularity (blood supply),

presence of a necrotic center, invasive potential (border distinctness) and the extent of

similarity to normal cells. Glioma tumors are histologically divided into four grades,

according to the World Health Organization (WHO) criteria [Gladson et al., 2010].

Grade I: These tumors typically having a good prognosis and are least malignant

form of the tumor. These tumors grow slowly and microscopically visible in the

almost normal form. Grade I tumors are more frequently occur in children [Wen and

Kesari, 2008; Pollack, 1994]. Example: Pilocytic astrocytoma

Grade II: These tumors grow slightly faster than grade I tumors and have a slightly

abnormal microscopic appearance; characterized by hypercellularity (based on

histologic examination). These tumors may invade surrounding normal tissue, and

may recur as a grade II or higher tumor. Grade II tumor patients have the 5–8-year

median survival [Gladson et al., 2010]. Mixed gliomas contain astrocytoma cells and

either oligodendroglioma or ependymoma cells or both. So these are commonly

graded as II or III grade.

Grade III: These tumors are malignant. These tumors contain actively reproducing

abnormal cells and invade surrounding normal tissue. The general characteristics of

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these tumors are hypercellularity, nuclear atypia and mitotic figures (based on

histologic examination) [Gladson et al. 2010]. Figure 1 is showing the characteristics

of Grade III tumors. Grade III tumors can be frequently converted in to the the grade

IV tumors. Grade III astrocytoma tumors (anaplastic astrocytoma tumors) have a 3-

year median survival [Dai et al. 2001, Kleihues et al. 2002, Kleihues et al. 1993 (New

York: Springer. 112 pp), Kleihues et al. 1993 (Brain Pathol. 3:255–68), Shih and

Holland 2006].

Figure 1: This image is the result of immunohistochemical staining of the Anaplastic

astrocytoma (World Health Organization Grade III) tumor cells. The center bottom part is

showing that cells are undergoing in to the mitosis and tumor nuclei are also

pleomorphic. [Gladson et al., 2010].

Grade IV: These tumors are the most malignant of tumor and invade wide areas of

surrounding normal tissue. These tumors have the general characteristics of

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hypercellularity, nuclear atypia, mitotic figures, and evidence of angiogenesis and/or

necrosis [Gladson et al. 2010]. These characteristics are showing in Figure 2 for the GBM

tumor. These are also specifically characterized by the presence of extensive area of

necrosis and hypoxia [Mendez et al. 2010]. In Grade IV tumors new blood vessels

formation takes place that allows their rapid growth [Gladson et al. 2010]. Glioblastoma

multiforme is the most common grade IV tumor. The median survival of GBM patients

are 12–18 months [Wen et al. 2008, Stupp et al. 2005,] but older patients (>60 years of

age) have a less median survival than younger patients.

Figure 2 This image is the result of immunohistochemical staining of the Glioblastoma

tumor (World Health Organization Grade IV) cells. The center part of this image is

showing the endothelial cell proliferation (angiogenesis). [Gladson et al., 2010].

Glioblastoma multiforme, anaplastic astrocytoma, and oligodendroglioma are referred to

as “High Grade Gliomas.”Tumors without any characteristics of nuclear atypia, mitosis,

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endothelial proliferation and necrosis or having only one feature (usually atypia) are

“Low Grade Glioma”. Example: Low Grade Astrocytoma.

1.1.2 Genetic Alterations in the Glioma: Glioma is usually caused by changes in the

genetic structure. Grade I tumors are frequently benign and typically do not progress to

Grade II, III, or IV tumors. And their genetic alterations are also different from those

found in the Grade II–IV tumors. Therefore further discussion is based on grade II- IV.

Loss of Heterozygosity is the most common genetic alteration found in

Oligodendroglioma (WHO Grade II) and anaplastic oligodendroglioma tumors (WHO

Grade III). This occurs very frequently and takes place on 1p and 19q arms of the

chromosomes. This is observed in 40%–90% of glioma biopsies [Louis, 2006; Mason and

Cairncross, 2008; Rao et al. 2003; Sathornsumetee and Rich 2008]. But still, it is not

known that which genes at the 1p and 19q loci are involved [Gladson et al., 2010]. Loss

of Heterozygosity is the major feature by which glioma biopsy is identified [Gladson et

al. 2010]. In addition to this, downregulation of the tumor suppressor and lipid

phosphatase PTEN gene also occurs in these tumors [Gladson et al., 2010].

Downregulation of this gene is found in 50% tumors and due to this downregulation

methylation of the promoter region takes place [Wiencke et al., 2007]. Amplification of

platelet-derived growth factor receptor alpha (PDGFRα) occurs in approximately 7% of

the oligodendroglial tumors.

In contrast to oligodendroglial tumors, astrocytoma tumors (WHO Grade II) frequently

(3%–33%) show the amplification of the PDGFRα and/or PDGFRβ genes [Shih and

Holland, 2006]. Loss of the p53 gene is also the common event in astrocytoma tumors

(WHO Grade II) [Wen and Kesari, 2008; Pollack, 1994; Dai et al., 2001]. The more

malignant form of the tumor is the anaplastic astrocytoma (WHO Grade III). In which the

deletion of cell cycle regulator gene Rb was found in 30% of tumors [Louis, 2006; Mason

and Cairncross, 2008; Rao et al. 2003; Sathornsumetee and Rich 2008; Maher et al.,

2001]. This gene is located on chromosome 13q13. Downregulation or mutation of the

tumor-suppressor gene p16INK4A/CDKN2A occurs in approximately 50% of these

tumors [Gladson et al., 2010]. p53 gene mutation shows approximately 50% of the

astrocytoma tumors [Louis, 2006; Mason and Cairncross, 2008; Rao et al., 2003;

Sathornsumetee and Rich 2008]. In addition to this, the p53 inhibitor MDM2 is also

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amplified in 13% to 43% of these tumors [Reifenberger et al., 1993; Hulleman and Helin

2005], so cell cycle regulation becomes interrupted and proliferation takes place. The

primary GBM tumors (accounts 90% of all GBM) exhibit the mutation and/ or

amplification in epidermal growth factor receptor (EGFR) gene that is located on

chromosome 7. This event occurs in up to 60% of primary GBM tumors [Rao et al.,

2003; Soni et al., 2005]. The most common mutation is the gain of function mutation due

to an in frame deletion of exons. The results of this mutation is the constitutive activation

of EGFR which can promote glioma cell proliferation and invasion [Rao et al., 2003;

Soni et al., 2005; Ohgaki and Kleihues, 2005; Fujisawa et al. 2000; Ohgaki et al., 2004].

The deletion of the lipid phosphatase gene PTEN is also the major genetic changes in the

primary GBM tumors [Gladson et al., 2010]. This is due to the loss of heterozygosity of

chromosome 10q or mutation [Gladson et al., 2010]. Due to the loss of this gene the

AKT/mTOR activity increases which promotes cell proliferation, survival and invasion

[Louis, 2006; Mason and Cairncross, 2008; Rao et al., 2003; Sathornsumetee and Rich

2008].

1.2 Proteomics and Glioma: Since from recent years due to the emergence of proteomics

science in the gllioma or cancer research has provided significant information; that plays

important part (as a different and new view) in the understanding of this disease. These

techniques are 2-Dimensional Electrophoresis, Mass Spectrometry and Microarray

(cDNA and protein microarray).

1.3 DATABASES:

1.3.1 Need of Databases: Databases are important tools that provide scientific research

information in the manually curated form. So anyone can easily access the

information through the databases in the precise form and less time period. PRIDE

PRoteomics IDEntifications database is the example of proteomic database.

1.3.2 Disease Database-Oncomine Database: Oncomine is the cancer microarray

database and integrated data mining plateform. Oncomine contains 65 gene

expression datasets. These datasets comprises nearly 48 million gene expression

measurements that are formed by 4700 microarray experiments. Therefore, this

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database provides the information about the expression levels of the cancerous

proteins that has been done by many studies. So anyone can do rationale study by

literature searching and by focusing on the expression profiles that are generated by

cancer tissue samples. So we directly become aware about the level of expressed

proteins that have been done in various study. We can find the way by which new

hypothesis can be made.

Glioma database is also comparatively similar to the oncomine database in the

manner by providing proteins fold change or expression level information. So here

also we are providing information about the proteins that are expressed during the

glioma disease. Anyone can directly find out the proteins that are identified in the

particular study and also their relative expression value if given.

1.3.3 Protein interaction database – HPRD: This is the Human Protein Reference

Database that is available on the internet. It is the database of large number of

proteins and their associated information. It provides the information about the

protein and its various interactions with other genes or proteins. It tells about the post

translational modifications of the protein or enzyme its enzyme substrate relationships

and also about the associated diseases with the particular protein.

1.3.4 Need of Glioma Database: The most important objective of the glioma database is to

provide the research information (on a large scale) precisely in the manually curated

form; those would be helpful for the scientific or research community. Especially

those are working on glioma research.

1.4 Pathway Analysis: Since glioma is the disaster cancerous disease that is originated from

the genetically altered glial cells. There are large number of genetically altered genes

have been identified in this particular study those affect many different kinds of pathways

in different manner and finally altered cell’s normal functions. These altered pathways

may fall by virtue of the any kind of genetic alterations those are mentioned below:

1. Mutation in the particular gene. Example: p53 gene mutation, IDH1 mutation

2. Overexpression of the gene with mutation. Example: overexpression of EGFR (in

primary GBM) receptor with mutation so the pathway becomes constitutively

activated without any attenuation.

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3. Overexpression of the gene without mutation. Example: overexpression of GFAP

(Biomarker), overexpression of semaphorin 3A

4. Deletion of the gene: Loss of Heterozygosity on Chromosome 10 (deletion of the

PTEN gene)

5. Alteration of the Metabolism: Glutamate Metabolism

But here we want to analyze the various pathways those are possibly involved in

this disease. Therefore we purposefully used our database containing proteins and

genes for the analysis of various pathways. The short introduction is given here

about the software that we have used for the analysis part.

1.5 Ingenuity Pathway Analysis Software (IPA): IPA is the web based software

application that enables us to analyze, integrate and understand data derived from gene

expressions and proteomic experiments. IPA core analysis delivers a rapid assessment of

the signaling and metabolic pathways, molecular networks, and biological processes that

are most significantly perturbed (or may involve) in our dataset of interest.

2. OBJECTIVES:

2.1 Manual Curation of Glioma Genome and Proteome Data for Glioma Database: The

objective of the glioma database is to provide the research information (on a large scale)

precisely in the manually curated form; those would be helpful for the scientific or

research community. Especially those are working on glioma research.

2.2 Pathway Analysis/ Analysis of Database Containing Proteins and Genes: This is the

objective in my project stage 2 part. The important reasons or needs behind the inclusion

of this (as part of the project) analysis are followings:

1. The glioma database comprises all the genes and proteins information those were

extracted from 490 research articles (approximately).

2. All these proteins or genes represented as the newly identified genes, up regulated

genes, down regulated genes, functional genes (involve in the migration, invasion,

attachment, proliferation, cell cycle regulation, cellular differentiation, cell

growth, cell survival, apoptosis etc.), enzymes, metabolic intermediates etc.

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3. Therefore the glioma database provides an interesting platform to analyze the

pathways in the integrative manner.

4. Firstly, we will be able to visualize that how many different pathways are possible

to emerge out from our database containing proteins and genes.

5. Secondly, we will select representative pathways for the glioma disease or we can

say these are core pathways in glioma disease. For example: GBM Signaling,

Glioma Signaling. Therefore, these signaling pathways are not needed any

validation or further investigation with the help of research articles.

6. But there will be the large number of others different pathways those would

acquire further investigation with the help of research articles, so we will become

enable to make new correlations and connections of these pathways with the

glioma disease.

7. So finally it would be very insightful when we will be able to represent new

findings (pathways), new correlations and new connections between two or three

different canonical pathways with respect to glioma disease.

3. GLIOMA DATABASE:

3.1 Development stages of the Glioma Database:

A. Literature Survey for the Glioma Research Articles: The first step was to search out

all the glioma research articles with the help of Pubmed. We collected all the Glioma

research articles (in the pdf form) those were published in the duration from 1981-2010.

But the large number of papers was collected those were published in the duration from

2000-2010. We collected a few number of papers those were published in the earlier

duration (1981-2000).

B. Study of the Glioma Research Papers and Collection of the Scientifically Important

Information: Since the documentation of Database was done by me and three other

project students. Therefore our work was also divided accordingly. After the end of

individual working the each of our database was documented in to the single format. The

short information about our each of work is following:

PROJECT STUDENTS NUMBER OF

DOCUMENTED PAPERS

TECHNIQUES USED

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IN THE DATABASE

Naveen Bokolia 89 cDNA Microarray or Gene

Microarray

Other Students

(Worked Previously)

373 Mass Spectrometry, 2DE,

Gene and Protein

Microarray

List of these 89 papers which are documented in this database are mentioned in the

reference section. We collected following information from the research papers and

documented in to the Glioma Database. These are following: Below I am providing the

list of various parameters that we used.

PMID JOURNAL

DETAILS

TYPES OF GLIOMA SAMPLE

TYPE

BIOMOLECULE

STUDIED

Entry Entry

Code

Grade

(L/I/H)

WHO

Grade

Cell

Line

Clinical

Sample

Gene/Protein

Continued….

Gene/

Protein

Name

Gene/

Protein

Symbol

Accession

ID

Fold

Change

Values

Patient

Details

Techniques Validation

Techniques

In this database I documented 1854 proteins and genes entries. On the web there is

Glioma information Diseases Database (www.diseasesdatabase.com/ddb31468.htm) is

available in which information about the all types of glioma is given.

3.2 Important features of the Glioma Database: The Glioma genome and proteome

database will be very insightful for the glioma researchers. They can easily get the

information about the any glioma proteins or genes and also their respective fold change

value (p-value also). In addition to this they can also get the information about the

conditions and techniques under which particular research or experiment was done.

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Therefore, they can also change their experimental conditions to find out new results in

their researchers. These are very important features of our glioma database. Below we are

providing two images of the excel file which is showing characteristic features of the

manual curation part. We can see the list of various parameters and their respective

entries.

Cont…

Cont…

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Figure 3: Images of the Glioma genome and proteome manual curation part (in excel

form), where we can see the various parameters and their respective entries.

4. MATERIALS AND METHODS FOR PATHWAY ANALYSIS:

4.1 Ingenuity Pathway Analysis Software (IPA): IPA is the web based software (licensed

by the Stanford University) application that enables us to analyze, integrate and

understand data derived from gene expressions and proteomic experiments. The most

important tool of this software is the IPA Core Analysis that we used purposefully. IPA

core analysis delivers a rapid assessment of the signaling and metabolic pathways,

molecular networks, and biological processes that are most significantly perturbed (or

may involve) in our dataset of interest. So firstly we upload the dataset file then we click

on the New Core Analysis option, then followed by we create and run analysis.

4.2 DAVID Bioinformatics Resources 6.7: This is the bioinformatics based software. We

used this software to get the signaling pathways results. We used same input files as we

used in the IPA. This gives the pathways results those are mapped by the KEGG and

Biocarta.

4.3 PANTHER (Protein ANalysis THrough Evolutionary Relationships): This is

generally used for the classification of the genes according to their functions, and

according to that it also classifies the signaling pathways.

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4.4 Input Files for the IPA Software: Since our glioma database is documented by the large

numbers of proteins and genes those are coming from all types of tumor grades (grade I-

IV), that included low and high grade both. So, we prepared and divided analysis files

(Input files) according to that. There are 6 input files that I have made separately. These

are in following table with number of genes:

Type of Input File

Numbers of Genes Accession No.

Glioma High Grade 1

(Grade III and Grade IV)

5,000 Genes Accession Numbers

Glioma High Grade 2

(Grade III and Grade IV)

7,556 Genes Accession Numbers

Glioblastoma (Grade IV)

10,913 Genes Accession Numbers

Astrocytoma (Grade III and IV) 1,565 Genes Accession Numbers

Glioma Low Grade ( Grade I and II) 2,358 Genes Accession Numbers

Glioma High Grade-Validated Genes (Grade III

and Grade IV)

1,379 Genes Accession Numbers

After the preparation of input files we uploaded each file separately (one by one) and got

out puts as the results for each file by the help of IPA.

4.5 Softwares and Websites for the Accession Numbers: When we had to prepare our

input files for the analysis then the major problem was to find out genes accession

numbers those were not available in the database. Since IPA input file format is mostly

suitable with the genes accession numbers. So, we used following online softwares and

websites to get the genes accession numbers.

1. UNIGENE WEBSITE (IN NCBI)

2. http://biodbnet.abcc.ncifcrf.gov/db/db2dbRes.php

3. http://smd.stanford.edu/cgi-bin/source/sourceBatchSearch

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5. OUTPUTS AS THE RESULTS FROM THE IPA: When we analyze any of our

dataset file (input file) by uploading in to the IPA New Core Analysis option, we get

following results.

A. Summary of Analysis: This gives the overall summary of our analysis results those

are created from our uploaded gene lists (dataset file). It tells about the top networks,

top biofunctions (diseases, molecular and cellular functions), top canonical pathways,

top tox lists and top tox functions. But here in our analysis the most important

analysis part is the canonical pathways.

B. Networks: Networks are the possible interactions between a particular group of

genes and proteins those are responsible for the different kinds of functions. For each

network we can see the list of molecules those are involving and also the top

functions.

C. Canonical Pathways: This is the very important part of our results that provides very

interesting platform for further analysis and investigation. We have analyzed and

discussed our results based on the canonical pathways results. For each pathway we

can see the list of molecules those are generating canonical pathways. For each

pathway there is the particular p-value and also the ratio (molecules present in our

dataset v/s molecules not present in our dataset) or percentage of our molecules

present in the pathway. P-value tells that how much the pathway is significant. So it’s

the matter of percentage v/s probability (both). The ratio gives us amount of

association of the pathway with disease and significance (p-value) gives the

confidence of association. For example, if a pathway has a high ratio (percentage) and

a very low p-value, the pathway is probably associated with the data and a large

portion of the pathway may be involved or affected. These two criteria (-log and

percentage or ratio) will be very helpful in the analysis of individual canonical

pathway.

D. Molecular and Cellular Functions: These are also important results and in

correlation with canonical pathways. We can see the all molecular and cellular

functions in chart form. In addition to this we can also see the list of molecules

associated with particular kind of function or the particular kind of disease.

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E. Toxicological lists and Toxicological Functions: Toxicological lists included about

the toxic pathways, for example Hepatic fibrosis and Cardiac hypertrophy. And

toxicological functions tell about the toxicity associated with particular disease, for

example increased level of alkaline phosphatase, liver necrosis and kidney failure etc.

But since these are not relevant to our analysis part so we have not included. Same

goes for the networks as well.

6. RESULTS FROM IPA FOR EACH DATASET FILE:

6.1 Results for the High Grade 1: Since our dataset file contains large numbers of genes

and proteins lists so we divided in to the two parts: High Grade 1 and High Grade 2.

Now firstly we are providing the results for high grade 1.

Canonical Pathways: Since canonical pathways are the most important part of

analysis and there is the important need to set the criteria for the pathways those are

possibly relevant to the glioma disease. So, firstly we included all those pathways

those are having higher –log (p-value) (significant value). Secondly after the taking

account of –log (p-value) basis, we visualize remaining pathways carefully and

investigated with the help of research articles. And among those remaining pathways

if we found any correlation with the glioma disease or glial cells then we analyzed

those pathways also. Finally we modified some pathways purposefully because other

proteins also play important roles in the regulation of particular mechanism. This

point will become more clarified in the analysis part when modified pathways will be

provided with the associated information.

The above given information for canonical pathways also applied for the others files

also (remaining 5 files for which we got canonical pathways).

Now we take the results of canonical pathways for this (High Grade 1) file according

to the first criteria of p-values. We found that most of the relevant canonical pathways

occupied above the –log (p-value) = 15, below 15 most of the pathways are irrelevant.

In the image of canonical pathways for High Grade 1, we distributed pathways in

three different categories: 1. Known Well Established Pathways, 2. Different

Pathways and 3. Pathways Not Mentioned in the Literatures. We have given the

following figure as an example for the overall visualization of these pathways.

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Figure 4: Pathways in three different categories.

Canonical pathways for High Grade 1 are given in Figure 5.

NOTE: Before starting of the canonical pathways we have given the summary for

High Grade 1 results.

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29

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See continuously…..

Figure 5: Canonical Pathways for the High Grade 1. Pathways denoted by stars

are relevant to the glioma disease.

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See continuously…

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See continuously…

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See continuously…

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6.2 Results for the High Grade 2: This is the second part of High Grade. We take results

about this.

Canonical Pathways: The required information about the canonical pathways is

already given so here the images of canonical pathways are given. The maximum

pathways are relies above the –log (p-value) = 15. Below 15 only few pathways are

relevant. The Canonical Pathways for High Grade 2 are given in Figure 6.

See continuously…

Figure 6: Canonical Pathways for the High Grade 2

36

See continuously…

37

6.3 Results for the Glioblastoma: Glioblastoma is the high grade (grade IV) and most

malignant brain tumor.

Canonical Pathways: For glioblastoma we also found most of the relevant pathways

above the –log (p-value) = 15. Here below 15 some pathways are relevant to glioma.

The canonical pathways for glioblastoma are given in Figure 7.

38

See continuously…

Figure 7: Canonical Pathways for the Glioblastoma Multiforme

39

6.4 Results for Astrocytoma: Astrocytomas are included in grade III or grade IV tumors.

These are also having importance equivalent to GBM.

Canonical Pathways: Canonical pathways for Astrocytoma are given in Figure 8.

40

See continuously…

Figure 8: Canonical Pathways for the Astrocytoma

41

See continuously…

42

6.5 Results for Low Grade: Although low grade is the less malignant and curable by

surgery and other treatments, however it can be progress in to more malignant form.

Therefore, we understand the analysis of low grade as the essential part.

Canonical Pathways: The canonical pathways for low grade are given in Figure 9.

43

6.6 Results for Validated High Grade: This is the part of high grade but the main

difference here is that we are considering only validated proteins or genes from the

database.

Canonical Pathways: The canonical pathways for the Validated High Grade are given in

Figure 10.

Figure 9: Canonical Pathways for the Low Grade

44

See continuously…

Figure 10: Canonical Pathways for the Validated High Grade

45

7. ANALYSIS and DISCUSSION PART - WITH THE HELP OF RESEARCH

ARTICLES:

7.1 Criteria for Analyzing Pathways with the Help of Research Articles: Before the

starting of the analysis of canonical pathways, we have made some criteria’s or points

according to which our analysis part will be considered. These are following:

46

1. Direct relationship or association of the pathway with glioma disease/glial cells.

2. Additional proteins in the pathway are making association with this disease/glial

cells.

3. Mainly central protein of the pathway or metabolism having direct relationship

with the glioma disease/ glial cells.

4. Possible new interlinks between two or three canonical pathways.

7.2 Pathways Already Well Established in the Glioma: Now firstly we describe all those

canonical pathways as results those are common in the High Grade, Glioblastoma and

Astrocytoma results. Since, High Grade glioma itself included Glioblastoma and

Astrocytoma and all three are malignant. Following pathways are already well

established in the glioma.

1. Glioblastoma Multiforme Signaling (GBM Signaling): This is the strong result

as canonical pathway and describes the major genetic alterations in the glioma. It

represents the alterations in the following pathways:

a. EGFR ⁄ RAS ⁄ NF1 ⁄ PTEN ⁄ PI3K Pathway (amplification of EGFR,

mutations in NF1, PTEN and PI3K) [Ohgaki et al., 2009].

b. TP53 ⁄MDM2⁄MDM4⁄ p14ARF Pathway (genetic alterations in p53,

MDM2, MDM4 and p14ARF) [ohgaki et al., 2009].

c. p16INK4a ⁄CDK4 ⁄ RB1 Pathway (genetic alterations in p16INK4a and

CDK4 and loss of normal function of RB1) [ohgaki et al., 2009].

Since these are the major genetic alterations and pathways in the glioma

and very well investigated and established so no need to analyze

furthermore about this. The GBM signaling pathway is given in Figure 8.

In this figure the major mutations are denoted by the small stars (*). We

have given the one more same pathway (in Figure 11) that also represents

the same genetic alterations in the glioma.

The above mentioned three different pathways (genetically altered in

glioma) are finally affects the cell growth, proliferation and cell survival

[ohgaki et al., 2009].

47

Figure 11: GBM Signaling

48

Figure 12 Major signaling pathways involved in the pathogenesis of

glioblastomas. pGBM, primary glioblastoma; sGBM, secondary

glioblastoma [Ohgaki et al., 2009]

2. PTEN Signaling: In above mentioned GBM signaling we have seen that the

PTEN is the major mutation in glioma and inhibits the PIP3 signal and thereby

inhibiting cell proliferation. But there, this was with respect to only PIP3 signal,

but if we see the overall final affects of PTEN mutation on the cell these will be

following. PTEN signaling pathway is given in Figure 13.

a. Increased Cell Migration.

b. Increased Cell Growth.

49

c. Increased Cell Survival and

d. Increased Cell Cycle Progression

NOTE: For all canonical pathways their final functions are denoted by:

So it’s clear that how PTEN act as the major mutation in glioma or cancer.

Figure 13: PTEN Signaling

50

3. PI3K-AKT Signaling: Here this is also the part of GBM signaling and now we

also want to see how this pathway affects cell’s final functions and contribute to

glioma formation. We can see here the overall effects of PI3K-AKT signaling like

as we have seen in PTEN signaling. This pathway is given in Figure 14. This

pathway is mainly responsible for the cell growth, cellular proliferation, cellular

survival and protein synthesis. These functions are ubiquitous for every cell. But

in glioma by virtue of the mutation in the PTEN gene (as we have seen

previously) this protein kinase B (PKB/AKT) activity is elevated in glioblastoma

[Kogan et al., 1998], therefore this signaling pathway becomes activated in

glioma.

Additional Proteins: PTEN/PI3K/Akt Pathway also regulates the Side Population

Phenotype and ABCG2 Activity in Glioma Tumor Stem-like Cells. This is the

important finding that Akt regulates ABCG2 activity. ABCG2 is the transporter

that provides chemo resistance in stem cells. It has been found that Akt, but not its

downstream target mTOR, regulates ABCG2 activity, and loss of PTEN increases

the SP [Bleau A M et al., 2009]. This is represented in the PI3K-AKT signaling

pathway.

51

4. p53 Signaling: We have already discussed about the mutation of p53 and its role

in the GBM signaling as the major genetic alterations in glioma. So there is no

need of further discussion and investigation about this. The role of p53 mutation

is very well established in glioma. We have given p53 signaling in the Figure 15.

Figure 14: PI3-AKT Signaling

52

5. Glioma Signaling: Glioma signaling is the part of GBM signaling. So we will not

discuss furthermore. This signaling is given in Figure 16.

Figure 15: p53 Signaling

53

6. Glioma Invasiveness: This signaling is showing the role of proteins those are

highly responsible for the invasive character of glioma. This signaling is showing

the role of Urokinase Plasminogen Activator (UPA) in the conversion of

plasminogen in to plasmin. Then followed by this plasmin activates the matrix

metalloproteinases (MMP’s). In the case of glioma MMP-2 and MMP-9 plays

Figure 16: Glioma Signaling

54

major role in the invasiveness by degrading the extracellular matrix (ECM). Here

Tissue Inhibitor Metalloproteinases (TIMP) inhibits the function of MMP’s but in

glioma the lower expression of TIMP has been identified that is also related to

patient’s longer survival (Aaberg-Jessen et al., 2009). In the figure we can also

see that Rho, Ras, ERK-1/2 and PI3K also promote the invasive character of

glioma by cytoskeleton rearrangement and cell migration mainly. The roles of

TIMP, MMP-2 and UPA are very well established in the glioma. We have given

glioma invasiveness signaling in Figure 17.

7.3 Novel Information for Signaling Pathways Involved in the Glioma. Here we will

discuss about those pathways which have important roles in the glioma but requires more

Figure 17: Glioma Invasiveness Signaling

55

novel and additional information. So, we analyzed and investigated following pathways

in every possible way.

1. Integrin Signaling: The integrin signaling maintains the structural link between

cells with the help of extracellular matrix proteins. So this signaling is mediated

by the extracellular matrix proteins and these extracellular proteins interact with

the integrin receptors; these interactions makes a signaling cascade and link to the

cytoskeleton proteins (actin filaments) [van der Flier et al., 2001]. Therefore this

signaling is important in maintaining cell shape, cell architecture and cell

migration (Ridley et al., 2003). Integrin signaling is given in the Figure 18.

Now we see how and in which way this signaling is important with respect to the

glioma. Firstly we want to see that which type of extracellular matrix (ECM)

proteins contribute to the integrin signaling in glioma. And secondly which types

of integrin receptors have role in glioma because up to date 20 dimeric proteins

have been identified among them 14 alpha and 8 beta subunits.

After the investigation, we found the following aspects and findings with respect

to the integrin signaling.

a. The expression of the vitronectin protein (ECM protein) is overexpressed in

the Anaplastic Astrocytoma (Grade III) but undetectable in the low grade

astrocytoma and normal brain tissues [Gladson et al., 1991 and Gladson et al.,

1995].

b. Tenascin C is also an extracellular matrix component is expressed at the

higher level in malignant glioma [Zagzag et al., 1995]. Vitronectin expression

is correlated with the tumor grade [Herold-Mende et al., 2002 and Leins et al.,

2003]. It mainly founds at the area of blood vessels [Behrem et al., 2005]

c. Finding: The expression of alpha6beta1 has been found. In which alpha6beta1

stably express in glioma cells and plays important role in cell growth, cell

migration and invasion. Here laminin-111 act as the ligand and interacts with

alpha6beta1 [Delamarre et al., 2009].

d. Finding: Secondly alphavbeta3 and alphavbeta5 expression has been found in

the glioma periphery but alphavbeta3 expression is important because it co

localized with the matrix metalloproteinase 2 (MMP-2), therefore its play

56

important role in cell migration [Lorenzo et al., 2001]. Additionally

alphavbeta3 expression is also associated with the vascular endothelial growth

factor (VEGF) and both are colocalized, so this promotes angiogenesis as well

[Takano et al., 2000]. Here osteopontin act as the ligand.

In Integrin signaling figure it’s difficult to show the co localization and how

its work because its complex network furthermore.

e. However in figure 18 we have shown about the alpha6beta1, and in which

manner it can promote migration. In this figure I have additionally mentioned

that the FAK expression (FAK) is higher in the high grade tumor [Rutka et al.,

1999] and provide attachment help with other proteins in this signaling

pathway. This is the Integrin Linked kinase (ILK), although we got this

signaing pathway (ILK signaling) differently. In one study it has been found

that this ILK (FAK) is inhibited or regulated by the MMAC1 (PTEN) tumor

suppressor gene. And since we have already discussed above that PTEN is

one of the major mutations in glioma so here integrin pathway will become

more activated due to the deregulated FAK or more activated FAK (ILK).

f. Additional Proteins: In this alpha6beta1 signaling pathway one final protein

that leads to the cytoskeletal rearrangements is the myosin regulatory light

chain (MRLC); it has been shown that the phosphorylation of MRLC

increases the motility in glioma cells. The phosphorylation of this protein is

done by MRLC-interacting protein (MIR)-interacting saposin -like protein

(MSAP). This MSAP protein is also overexpressed in the glioma and through

the phosphorylation of MRLC promotes migration [Bornhauser et al., 2005].

So, above mentioned important features we have added in the integrin

signaling (in figure 18).

57

2. Axonal Guidance Signaling: Axonal guidance is the key event in the formation

of the neuronal network. Axons are guided by the variety of guidance factors like

semaphorins, ephrins and netrin. Their receptors are found on the axonal cones for

example plexin functions as receptors for the repulsive axonal guidance molecules

semaphorins. This plays very important role during the various developmental

processes.

But now we want to look this with respect to glioma, or we want to analyze that

which particular pathway may also have role in the glioma. So firstly we look at

the following findings of the axonal guidance molecules with respect to the

glioma.

Figure 18: Integrin Signaling

58

a. Neurons and glial cells both plays role in axonal guidance [Chotard et al.,

2004].

b. Slits, semaphorins and netrins are three families of proteins that can attract or

repel growing axons and migrating neurons in the developing nervous system

of vertebrates and invertebrates.

c. Finding: Autocrine semaphorin 3A signaling promotes glioblastoma dispersal

[Bagci et al., 2009]: It has been found that Sema 3A is overexpressed in a

subset of human GBM’s compared with the nonneoplastic human brain. This

semaphorin3A act as an autocrine signal for neurophilin-1 and promote GBM

dispersal by modulating substrate adhesion. This study suggests that targeting

Sema-3A- neurophilin signaling may limit GBM infiltration. And here

Figure 19: Axonal Guidance Signaling

59

neurophilin-1 receptor significantly over expressed in the GBM’s. Because

inhibition of neurophilin-1 decreases the GBM cell migration therefore this

molecule is required for the GBM cell migration. This pathway is as the part

of axonal guidance signaling that is given in Figure 19. But we got semphorin

signaling in neurons also. So because of the purpose of understanding this

pathway we have given Semaphorin Signaling also in Figure 20. In this

semaphorin signaling we can see that semaphorin3A interacts with the

neurophilin-1 receptor (associated with PlexinA1), followed by this is

signaling is dependent on the Rac1 activity (in GBM). We can clearly see the

final effect of this pathway is that cofilin is inhibited. So now free cofilin will

not be available for the decreasing depolymerization, and depolymerization

process frequently takes place in GBM cells followed by cells become

dispersed and become able to migrate or infiltrate in to others areas.

d. Additional Proteins: In the Semaphorin Signaling above we discussed about

the Sema3A that positively affect the glioma progression. Now we will

discuss about the semaphorin3B that regulate the glioma cell growth. It has

been identified that Sema3B is the direct transcriptional target of p53 (Ochi et

al., 2002). And the mRNA level of Sema3B increases in the presence of wild

type p53 in normal cells while we are applying UV radiation (stress

condition). We can see that this expression level can be increased in the

normal cells because p53 is not mutated; but in glioma p53 is the major

mutation that will not allow increasing the transcriptional level of Sema3B.

Therefore the Sema3B will not play any significant role in regulating glioma

growth. Although this also interacts in the same manner as Sema3A interacts.

Sema3B protein and its interaction are represented in the figure 20.

e. Finding: Slit2-Robo1 receptor signaling also play important role in glioma, we

can see this part in the axonal guidance signaling image (figure 16). It has

been found that this signaling plays role in the glioma cell migration [Mertsch

et al., 2008].

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f. Finding: Here is another important part of axonal guidance signaling is the

ephrins receptor signaling. It has been found that Ephrin-B3 ligand promotes

glioma invasion through activation of Rac1[Nakada et al., 2006]. Ephrin

receptors are the tyrosine kinase receptors those involve in the nervous system

development. In the case of glioma, Ephrin-B3 ligand is upregulated and its

phosphorylation promotes migration and invasion in glioma cells. Therefore,

here phosphorylation and overexpression both have important role in

migration and invasion. This pathway act in the Rac1 dependent manner that

Figure 20: Semaphorin Signaling in Neurons

61

is shown in the figure 16 also. Since we got ephrins receptor signaling as a

different part that is common with the axonal guidance signaling, so ephrins

receptor signaling is also given in the figure 16.

g. Finally so far we analyzed about the semaphorins, slit2 molecules and ephrins

system with respect to glioma as axon guidance signaling part.

3. Interleukin 8 Signaling (IL-8): Interleukin 8 is a chemokine and angiogenic

factor. This signaling plays important role in gliomagenesis. The role of

interleukin 8 signaling is very important in the GBM because of the following

findings:

a. Findings: The expression of interleukin 8 is regulated by reduced oxygen

pressure (hypoxia) in glioblastoma multiforme [Desbaillets et al., 1999]. It has

been found that IL-8 gene expression induced under the anoxic conditions.

And it’s the very important finding because hypoxic conditions are very

favorable for glioma progression; hypoxic conditions promote angiogenesis

and glycolysis mainly. Additionally IL-8 also play important role in the

leukocyte activation, chemotaxis and angiogenesis [Desbaillets et al., 1997].

b. Since IL-8 has proangiogenic activity. IL-8 mainly binds to the CXCR1 or

CXCR2 receptors of the leukocytes (neutrophils, macrophages), T-

lymphocytes and endothelial cells and promotes angiogenesis and recruits the

neutrophils [Brat et al., 2005]. Finally IL-8 also stimulates the expression of

MMP-2 and MMP-9 [Li et al., 2003], their (MMP-2 and 9) enzymatic activity

is required for the modification of the extracellular matrix and promotes

endothelial cell migration. Above mentioned all these features are mentioned

in the IL-8 signaling Figure 21.

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4. Interleukin 6 Signaling: Finding: Interleukin 6 is required for the glioma

development in mouse model [Weissenberger et al., 2004]. It was already known

that IL-6 level is elevated in malignant glioma but here they found that this IL-6 is

required for the glioma development. They also demonstrated the phosphorylation

and translocation of the signal transducer and activator of transcription (STAT) 3,

which is the downstream factor of IL-6 signaling (in figure 18). The activation of

STAT3 increases with the malignancy. Therefore the elevated level of IL-6

constitutively activates the STAT-3. These findings indicate the important role of

IL-6 in anaplastic astrocytoma (glioma III grade) progression. The IL-6 signaling

is given in Figure 22.

Figure 21: Interleukin-8 Signaling

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5. HIF-1 alpha Signaling: Alone this signaling is able to contribute effectively in

the glioma formation. It has very excellent features that drastically affect the

glioma progression. Firstly we understand the important features of this signaling

by various aspects and findings. HIF-1 alpha signaling is given in Figure 23.

Figure 22: Interleukin-6 Signaling

64

a. Glioblastoma tumors have the extensive areas of hypoxia and necrosis. HIF-1

alpha is the master regulator which responds under the hypoxic conditions.

b. HIF-1 alpha is the heterodimeric (HIF-1 alpha and beta) transcription factor

which up regulates or activates the expression of those genes which are

mainly responsible for the angiogenesis, glycolysis and erythropoiesis. This is

done by binding to the hypoxia response element (HRE) on the DNA or gene.

[Kaur et al., 2005]

c. Figure 20 is given for the HIF-1 signaling. We can see in this pathway that

HIF-1 alpha involves in the angiogenesis and glycolysis by activating the

expression of VEGF (for angiogenesis), glycolytic enzymes and glucose

Figure 23: Hypoxia Inducible Factor-1 Signaling

65

transporter 3 (GLUT-3) proteins (for glycolysis) respectively. Although it also

stimulates erythropoietin expression but in glioma it was found that it only

slightly effects the growth and survival [Yin et al., 2007]. Erythropoietin

receptors have been found on the GBM cells. One study suggests that

erythropoietin signaling via STAT3 is required for stem cell maintenance

[Cao et al., 2010]. We got erythropoietin signaling also as the different

pathway (in canonical pathways of GBM or High grade).

d. Recently one study have shown that HIF-1 alpha involve in the invasion and

migration of the glioma cells [Mendez et al., 2010]

e. Induction of glycolytic enzymes indicates the Warburg effect for cancer cells.

In which cancer cells predominantly depend on the glycolysis and

mitochondrial functions become defective.

f. In the HIF-1 signaling we can see that the ubiquitylation and degradation of

HIF-1 alpha is mediated by the VHL. Before that hydroxylation is required by

the prolyl hydroxylases (PHD proteins). This is done by the negative feedback

of the PHD proteins. Since excess HIF-1 alpha can arrest the cell cycle [Goda

et al., 2003] and can kill the cell therefore in cancer cells this degradation

pathway and negative feedback control mechanism is necessary. For the

understanding of this degradation and feedback control we have given one

pathway (in Figure 24) from the literature.

g. Above mentioned facts indicates the very important roles of HIF-1 alpha in

glioma disease.

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Figure 24: Feedback mechanism of PHD in HIF-1 alpha degradation

6. Wnt/beta (β) catenin signaling: Wnt/beta catenin signaling is among one of the

top most important signaling like GBM signaling and HIF-1 alpha signaling. We

got this canonical pathway at the –log (p-value) = 17 (approx.). This pathway is

very significant with respect our results and glioma also. We look at the various

aspects and findings about this pathway. This pathway is given in Figure 25.

a. Wnt proteins are a larger family of cysteine rich secreted molecules, which

consists 19 members in mammals up to date [Kamino et al., 2011]. Wnt

regulates cell growth, cell motility and cell fate [Logan et al., 2004 and

Kikuchi et al., 2009].

b. There are at least three different intracellular pathways are activated by the

Wnt proteins, but the β-catenin dependent canonical pathway is highly

conserved among species [Kamino et al., 2011].

c. When Wnt acts on the cell surface receptors, which consist of Frizzled (Fz)

and LRP5/6, and β-catenin is stabilized by release from the Axin mediated

degradation complex (given in Figure 25).

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d. Now the accumulated beta catenin is translocated to the nucleus where it binds

to the translocation factor T-cell factor ⁄ lymphoid enhancer factor and thereby

stimulates the expression of various target genes [Kamino et al., 2011].

e. There are some Wnts which includes Wnt-1, 3a and 7a activates the β-catenin

dependent pathway. The accumulation of β-catenin is already observed in

many malignant tumors.

f. Finding: In the most recent study it is identified that Wnt-5a is highly

expressed in advanced stages of glioma (GBM), and the expression of Wnt-5a

is correlated or involved in the infiltrative (invasive) activity of glioma by

inducing migration [Kamino et al., 2011].

Figure 25: Wnt/beta Catenin Signaling

68

g. Furthermore the induction of MMP-2 (matrix metalloproteinase-2) is also

dependent on the Wnt-5a by which the invasive activity of glioblastoma

increases [Kamino et al., 2011]. The elevated level of MMP-2 and MMP-9 is

already reported in GBM. MMP’s are responsible for the degradation of

surrounding tissues.

h. Finding: In another previous finding it was already shown that the wnt-5a has

the role in proliferation of glioblastoma cells [Yu et al. 2007].

i. So above mentioned important features are helpful in understanding the role

of Wnt-5a with our wnt/ β-catenin signaling pathway (in Figure 25).

Here we completed our canonical pathways with their novel information and

those are common in the High Grade, Glioblastoma and Astrocytoma. But

some pathways are remaining like phospholipase – c signaling, BMP (Bone

Morphogenetic Protein) signaling, endothelin-1 signaling, HMGB 1 signaling

etc. Since due to the limitation of time period we have not included right now

but these also have roles in glioma.

7.4 New findings and New Correlations of Canonical Pathways with the Glioma: Now

we will discuss about those canonical pathways which we got as the results for high

grade, GBM or astrocytoma. We have put in to this category because we have not found

very significant or no direct correlations of these pathways with the glioma disease. But

after the investigation we found that if any pathway is associated with the glial cells or

brain then it might have the probability to be altered during glioma progression. We look

at the following pathways and see how these pathways may also have important role

during glioma progression.

1. Glucocorticoid Receptor Signaling: This is one of the top most new kinds of

important finding from our results as canonical pathway. Firstly we look at the

important aspects and finding with respect to the glial cells and brain. This

signaling is given in Figure 26.

69

a. Glucocorticoid receptors are very widely distributed in most of the major parts

of the brain[Morimoto et al., 1996]

b. Glucocorticoid receptors are predominantly localized in the cell nucleus and

their expressions are found in the cytoplasm [Morimoto et al., 1996].

c. Glucocorticoids inhibit glucose transport (uptake) in neuronal and glial cells

[Horner et al., 1990]. This is the important finding with respect to glioma

because in glioma the glucose transport process is elevated by the up-

regulation of the GLUT (part of Warburg effect).

d. It has been found that glucocorticoid receptors involve in the cell surface

modulation. There are two important following findings with respect to this:

e. Finding 1: Cell surface modulation of gene expression in brain cells by down

regulation of glucocorticoid receptors [McGinnis et al., 1981] In this study

they shown that downregulation of the glucocorticoid receptors modulate the

Figure 26: Glucocorticoid Receptor Signaling

70

cell-contact. This study was done for the C6 glioma cells and brain cells

(normal oligodendrocytes and astrocytes).

f. Finding 2: Glucocorticoid hormone modulation of both cell surface and

cytoskeleton related to growth control of rat glioma cells [Armelin et al.,

1983]. This study was very important because they found that glucocorticoids

modulate both structure and function of cell surface and cytoskeleton.

Glucocorticoids thereby increase the cell flattening, cell adhesion, extensive

fibronectin deposition and microtubule rearrangement. Therefore these

functions of glucocorticoids involves in the growth control of glioma.

g. Now we take the functions of glucocorticoids at the molecular level. Since

there is no clear finding of the glucocorticoid regulated genes in the glioma

disease. But however we found that glucocorticoid regulate three important

genes in glial cells. We look at this finding.

h. Glucocorticoids regulate the expression of following genes in glial cells.

Glial Fibrillary Acidic Protein (GFAP) ↓ (Downregulated)

[O'Callaghan et al., 1991and Nicholas et al., 2005]

Transforming growth factor beta (TGF-β1) ↓ (Downregulated)

[Nicholas et al., 2005]

N-myc downstream-regulated gene-2 (Ndrg-2) and ↑ (Upregulated)

[Nicholas et al., 2005]

Glycerol Phosphate Dehydrogenase (GPDH) [Kumar et al., 1985]

Above mentioned all genes are regulated by the glucocorticoids, but only first

three plays important roles in glioma. The fourth one (GPDH) does not play

any role in glioma. So we will discuss about these one by one.

Glial Fibrillary Acidic Protein: This protein is the biomarker for astrocytes.

GFAP has been identified as the biomarker for glioblastoma because the

expression level of GFAP is found to be elevated in the serum of glioblastoma

patients [Jung et al., 2007. But the important point is that this is not only

elevated at the serum level but the expression of GFAP also increases at the

cytoplasmic level with the malignant progression of glioma.

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GFAP is the intermediate filament that increases the mechanical strength of

the cells.

Transforming growth factor beta (TGF-β1): the enhanced expression of

TGF- β1 and TGF-β2 is already reported in glioblastoma. And both are

potential growth regulators of glioma [Golestaneh et al., 2005]; the mitogenic

response either positive or negative depending upon the degree of anaplasia.

N-myc downstream-regulated gene-2 (Ndrg-2): Ndrg is newly identified as

the metastasis suppressor gene. There are four family members of this gene:

Ndrg-1, Ndrg-2, Ndrg-3 and Ndrg-4. But only Nrdg-2 is upregulated under the

glucocorticoid response. The role of only Nrdg-1 is very well understood in

the cancer but now in one study it was found that Nrdg-2 inhibits the

glioblastoma cell proliferation [Deng et al., 2003]. In this study they found the

expression of Nrdg-2 in normal brain but very low expression in the GBM

tissues. So this tumor suppressor gene is expressed at very low level in

glioblastomas. But the regulation behind that is still unclear; however from

our analysis and investigation suggests the possible role of glucocorticoid

regulation or signaling.

So here we become able to demonstrate a new kind of regulation pathway for

glucocorticoids that affects the expression of three important genes. Such kind

of regulation is still not studied with respect to glioma or may be for another

cancer.

We become able to demonstrate glucocorticoid regulation in this way: (see

Figure 27)

72

Figure 27: Newly suggested role of Downregulated Glucocorticoid signaling

in Glioma.

Now we take the glucocorticoid signaling pathway that we got from the IPA

as the result for GBM, High Grade or Astrocytoma. This pathway is given in

Figure 23. In this pathway also we can see the many things those are still not

clear like regulation of Bcl-2 which involve in the proliferation and survival;

and regulation of cytokines (interleukins). We can see in this pathway that

how glucocorticoids inhibits the Tgf-beta/Smad signaling pathway, that is

also the major pathway in cell proliferation. Finally, we can suggest from

above observations that role of glucocorticoids are very helpful in controlling

the tumor growth.

2. Arachidonic Acid Metabolism: We got this signaling pathway for the GBM and

High grade. After the investigation we also found that this metabolism may play

important role in glioma.

73

There are some important findings :

a. Findings: Arachidonic acid induces the prolonged inhibition of glutamate

uptake in to glial cells [Barbour et al., 1989]. This is the very important

finding because in glioma progression the high glutamate release is the most

common phenomenon and has a distinct growth advantage. One study

suggests that activation of NMDA receptors (when glutamate bound to the

NMDA) facilitates the tumor expansion. Additionally on one hand glutamate

promotes tumor expansion and on the other hand it kills the neuronal cells

also that help in the expansion of tumor growth. It has also been found that

glutamate act as the autocrine or paracrine signaling thereby both Ca2+

oscillations induces and glioma migration enhances via activation of the

AMPA receptors [Lyons et al., 2007]. We got the glutamate metabolism as

the separate pathway. In glioma the glutamate metabolism is found to be

elevated.

b. Therefore we can understand the important role of arachidonic acid

metabolism with respect to the glutamate uptake, because extracellular

concentration of glutamate affects glioma growth positively. So here we

Figure 28: Arachidonic Acid Metabolism

74

suggest that arachidonic acid metabolism may be upregulated in the glioma

tissue. We have not found any study about the correlation between in vivo

arachidonic acid metabolism and glutamate uptake related to glioma.

c. We can understand the newly suggested role of upregulated arachidonic acid

metabolism by following representation: Figure: 29

The Arachidonic Acid Metabolism is given in Figure 28.

Figure 29: Newly suggested role of Upregulated Arachidonic Acid Metabolism in

Glioma Disease.

3. Role of NANOG in embryonic stem cell pluripotency: Although NANOG

plays important role in the pluripotency but one recent study finds out that

NANOG plays important role as the mediator between HEDGHOG-GL1

signaling. They found that NANOG plays important role in the tumorigenecity

75

and modulated by the p53 [Zbinden et al., 2010]. For understanding we have

given the HEDGHOG signaling pathway in figure 31 with the new features. And

we have also given the role of NANOG in embryonic stem cell pluripotency in

Figure 30.

Figure30: Role of NANOG in Embryonic Stem Cell

Pluripotency

76

8. CONCLUSION: We manually curated genomic and proteomic data of glioma from the

literatures and further analyzed it by using Ingenuity Pathway Analysis software. We

were able to find out different types of canonical pathways those play important roles in

the glioma disease. We further analyzed these results from literature to find out the

relevance of these findings in context of glioma. This analysis not only revealed that

Figure 31: Sonic Hedg Hog Signaling

77

known pathways such as PTEN, p53 and GBM signaling obtained in our data set are

relevant but also many new connections for other canonical pathways such as arachidonic

acid metabolism, glucocorticoids and gene regulations, integrin signaling and PI3K-AKT

signaling etc. Additionally we analyzed our dataset using online software (DAVID and

PANTHER) to get the signaling pathways results. A comparison of these results from

IPA, DAVID and PANTHER suggested that that most of the pathways are common.

These results further provided higher confidence level for our analysis and results.

9. REFERENCES:

Aaberg-Jessen C, Christensen K, Offenberg H, Bartels A, Dreehsen T, Hansen

S, Schrøder HD, Brünner N, Kristensen BW (2009). Low expression of tissue

inhibitor of metalloproteinases-1 (TIMP-1) in glioblastoma predicts longer patient

survival. J Neurooncol., 95(1):117-28.

Armelin MC, Armelin HA(1983). Glucocorticoid hormone modulation of both cell

surface and cytoskeleton related to growth control of rat glioma cells. J Cell Biol.,

97(2):459-65.

Bagci T, Wu JK, Pfannl R, Ilag LL and Jay DG (2009). Autocrine semaphorin 3A

signaling promotes glioblastoma dispersal. Oncogene, 28:3537–3550.

Barbour B, Szatkowski M, Ingledew N, Attwell D (1989). Arachidonic acid induces a

prolonged inhibition of glutamate uptake into glial cells. Nature, 342(6252):918-20.

Behrem S, Zarkovic K, Eskinja N, et al. (2005). Distribution pattern of tenascin-C in

glioblastoma: correlation with angiogenesis and tumor cell proliferation. Pathol

Oncol Res., 11:229–35.

Bleau AM, Hambardzumyan D, Ozawa T, Fomchenko EI, Huse JT, Brennan CW,

and Holland EC (2009). PTEN/PI3K/Akt Pathway Regulates the Side Population

Phenotype and ABCG2 Activity in Glioma Tumor Stem-like Cells. Cell Stem Cell;

4:226–235.

78

Bornhauser BC, and Lindholm D (2005). MSAP enhances migration of C6 glioma

cells through phosphorylation of the myosin regulatory light chain. CMLS, Cell. Mol.

Life Sci., 62:1260–1266.

Brat DJ, Bellail AC, Van Meir EG (2005). The role of interleukin-8 and its receptors

in gliomagenesis and tumoral angiogenesis. Neuro Oncol., 7(2):122-33.

Brunn GJ, Williams J, Sabers C (1996). Direct inhibition of the signaling functions

of the mammalian target of rapamycin by the phosphoinositide kinase inhibitors,

wortmannin and LY294002. EMBO J., 15: 5256-5267.

Candece L. Gladson, Richard A. Prayson, and Wei Michael Liu (2010). The

Pathobiology of Glioma Tumors. Annu. Rev. Pathol. Mech., Dis. 5:33–50

Cao Y, Lathia JD, Eyler CE, Wu Q, Li Z, Wang H, McLendon RE, Hjelmeland

AB, Rich JN (2010). Erythropoietin Receptor Signaling Through STAT3 Is Required

For Glioma Stem Cell Maintenance. Genes and Cancer, 1(1):50-61.

Chen-ping, ZHANG Jun-Ping (2005). New developments in chemotherapy for

malignant brain tumors: Molecular targeted therapy. Chinese Journal of Neuro-

Oncology, 3(1):1-6.

Chotard C and Salecker I (2004). Neurons and glia: team players in axon guidance.

TRENDS in Neurosciences, 27(11).

Ciardiello F. (2000). Epidermal growth factor receptor tyrosine kinase inhibitors as

anticancer agents. Drugs, 60:25-32.

Dai C, Celestino JC, Okada Y, Louis DN, Fuller GN, Holland EC. (2001). PDGF

autocrine stimulation dedifferentiates cultured astrocytes and induces

oligodendrogliomas and oligoastrocytomas from neural progenitors and astrocytes in

vivo. Genes Dev., 15:1913–25

Delamarre E, Taboubi S, Mathieu S, Be´renguer C, Rigot V, Lissitzky JC, Branger

DF, Ouafik L’H, and Luis J (2009) Expression of Integrin α6β1 Enhances

Tumorigenesis in Glioma Cells. The American Journal of Pathology, 175(2).

79

Deng Y, Yao L, Chau L, Ng SS, Peng Y, Liu X, Au WS, Wang J, Li F, Ji S, Han

H, Nie X, Li Q, Kung HF, Leung SY, Lin MC (2003). N-Myc downstream-regulated

gene 2 (NDRG2) inhibits glioblastoma cell proliferation. Int J Cancer., 106(3):342-7.

Desbaillets I, Diserens AC, Tribolet N, Hamou MF and Van Meir EG (1999).

Regulation of interleukin-8 expression by reduced oxygen pressure in human

glioblastoma. Oncogene, 18:1447-1456.

Desbaillets I, Diserens AC, Tribolet N, Hamou MF and Van Meir EG (1997).

Upregulation of Interleukin 8 by Oxygen-deprived Cells in Glioblastoma Suggests a

Role in Leukocyte Activation, Chemotaxis, and Angiogenesis. J. Exp. Med.,

186(8):1201–1212.

Fujisawa H, Reis RM, Nakamura M, Colella S, Yonekawa Y, et al. (2000). Loss of

heterozygosity on chromosome 10 is more extensive in primary (de novo) than in

secondary glioblastomas. Lab. Investig., 80:65–72

Furuta M, Weil J Robert, Vortmeyer O Alexander, Huang Steve, Lei Jingqi, Tai-

Nan Huang et al. (2004). Protein patterns and proteins that identify subtypes of

glioblastoma Multiforme. Oncogene, 23:6806–6814

Gladson CL, Cheresh DA (1991). Glioblastoma expression of vitronectin and the

alpha v beta 3 integrin. Adhesion mechanism for transformed glial cells. J Clin

Invest., 88:1924–32.

Gladson CL, Prayson RA, and Liu WM (2010). The Pathobiology of Glioma Tumors.

Annu. Rev. Pathol. Mech. Dis., 5:33–50.

Gladson CL, Wilcox JN, Sanders L, et al. (1995). Cerebral microenvironment

influences expression of the vitronectin gene in astrocytic tumors. J Cell Sci.,

108(Pt.3):947–56.

Golestaneh N, Mishra B(2005). TGF-beta, neuronal stem cells and glioblastoma.

Oncogene, 24(37):5722-30.

80

Herold-Mende C, Mueller MM, Bonsanto MM, et al. (2002). Clinical impact and

functional aspects of tenascin-C expression during glioma progression. Int J Cancer,

98:362–9.

Horner HC, Packan DR, Sapolsky RM (1990). Glucocorticoids inhibit glucose

transport in cultured hippocampal neurons and glia. Neuroendocrinology, 52(1):57-

64.

Hulleman E, Helin K. 2005. Molecular mechanisms in gliomagenesis. Adv. Cancer

Res., 94:1–27

Jung CS, Foerch C, Schänzer A, Heck A, Plate KH, Seifert V, Steinmetz H, Raabe

A, Sitzer M (2007). Serum GFAP is a diagnostic marker for glioblastoma multiforme.

Brain, 130(Pt 12):3336-41.

Kamino M, Kishida M, Kibe T, Ikoma K, Iijima M, Hirano H, Tokudome M, Chen

L, Koriyama C, Yamada K, Arita K, Kishida S (2011). Wnt-5a signaling is correlated

with infiltrative activity in human glioma by inducing cellular migration and MMP-2.

Cancer Sci., 102(3):540-8.

Kaur B, Khwaja FW, Severson EA, Matheny SL, Brat DJ, Van Meir EG (2005).

Hypoxia and the hypoxia-inducible-factor pathway in glioma growth and

angiogenesis. Neuro Oncol., 7(2):134-53.

Kikuchi A, Yamamoto H, Sato A (2009). Selective activation mechanisms of Wnt

signaling pathways. Trends Cell Biol, 19: 119–29.

Kilic T, Alberta JA, Zdunek PR (2000) Intracranial inhibition of platelet derived

growth factor glioblastoma cell growth by an orally active kinase inhibitor of the 2

phenylaminopyrimidine class. Cancer Res., 60:5143-5150.

Kleihues P, Burger PC, Scheithauer BW. (1993). Histological Typing of Tumours of

the Central Nervous System. New York: Springer, 112 pp.

Kleihues P, Burger PC, Scheithauer BW. (1993). The new WHO classification of

brain tumours. Brain Pathol., 3:255–68

81

Kleihues P, Louis DN, Scheithouer BW, Rorck LLB, Reifenberg G (2002). The

WHO classification of tumors in the nervous system. J. Neuropathol. Exp. Neurol.,

60:215–25

Kogan DH, Shalev N, Wong M, Mills G, Yount G and Stokoe D (1998) Protein

kinase B (PKB/Akt) activity is elevated in glioblastoma cells due to mutation of the

tumor suppressor PTEN/MMAC. Current Biology, 8(21):1195–1198.

Korkolopoulou P, Christodoulou P, Kouzelis K, Hadjiyannakis M, Priftis A (1997).

MDM2 and p53 expression in gliomas: a multivariate survival analysis including

proliferation markers and epidermal growth factor receptor. Br. J. Cancer, 75:1269–

78

Kristin Kobb (2006), “Microarrays” The search for meaning in a vast sea of data.

Biomedical Computational Review.

Kumar S, Sachar K, Huber J, Weingarten DP, de Vellis J (1985). Glucocorticoids

regulate the transcription of glycerol phosphate dehydrogenase in cultured glial cells.

J Biol Chem., 260(27):14743-7.

Leins A, Riva P, Lindstedt R, et al. (2003). Expression of tenascin-C in various

human brain tumors and its relevance for survival in patients with astrocytoma.

Cancer, 98:2430–9.

Li, A., Dubey, S., Varney, M.L., Dave, B.J., and Singh, R.K. (2003) IL-8 directly

enhanced endothelial cell survival, proliferation, and matrix metalloproteinases

production and regulated angiogenesis. J. Immunol., 170, 3369–3376.

Logan CY, Nusse R (2004). The Wnt signaling pathway in development and disease.

Annu Rev Cell Dev Biol, 20: 781–810.

Lorenzo B, Francolini M, Marthyn P, Zhang J, Carroll RS, Nikas DC, Strasser JF,

Villani R, Cheresh DA, Black PMcL (2001). and αvβ5 Integrin Expression in Glioma

Periphery. Neurosurgery, 49(2).

Louis DN. (2006). Molecular pathology of malignant gliomas. Annu. Rev. Pathol.

Mech. Dis., 1:97–117

82

Lyons SA, Chung WJ, Weaver AK, Ogunrinu T, Sontheimer H (2007). Autocrine

glutamate signaling promotes glioma cell invasion. Cancer Res., 67(19):9463-71.

Maher EA, Furnari FB, Bachoo RM, Rowitch DH, Louis DN (2001) Malignant

glioma: genetics and biology of a grave matter. Genes Dev., 15:1311–33

Mason WP, Cairncross JG. (2008). The expanding impact of molecular biology on

the diagnosis and treatment of gliomas. Neurology, 71:365–73

Mattei A, Ramina R, Tatagiba M, Aguiar P (2005). Past, present and future

perspectives of genetic therapy in gliomas. Indian Journal of Human Genetics, 11(1)

McGinnis JF, de Vellis J (1981). Cell surface modulation of gene expression in brain

cells by down regulation of glucocorticoid receptors. Proc Natl Acad Sci U S A.

78(2):1288-92.

Mendez O, Zavadil J, Esencay M, Lukynov Y (2010), Knock down of HIF-1α in

glioma cells reduces migration in vitro and invasion in vivo and impairs their ability

to form tumor spheres. Molecular Cancer, 9:133

Mertsch S, Schmitz N, Jeibmann A, Geng JG, Paulus W, Senner V (2008). Slit2

involvement in glioma cell migration is mediated by Robo1 receptor. J Neurooncol,

87:1–7.

Morimoto AM, Tomlinson MG, Nakatani K, Bolen JB, Roth RA, Herbst R (2000).

The MMAC1 tumor suppressor phosphatase inhibits phospholipase C and integrin-

linked kinase activity. Oncogene, 19(2):200-9.

Morimoto M, Morita N, Ozawa H, Yokoyama K, Kawata M (1996). Distribution of

glucocorticoid receptor immunoreactivity and mRNA in the rat brain: an

immunohistochemical and in situ hybridization study. Neurosci Res., 26(3):235-69.

Nakada M, Drake KL, Nakada S, Niska JA, and Berens ME (2006). Ephrin-B3

Ligand Promotes Glioma Invasion through Activation of Rac1. Cancer Res, 66: (17).

83

Nichols NR, Agolley D, Zieba M, Bye N (2005). Glucocorticoid regulation of glial

responses during hippocampal neurodegeneration and regeneration. Brain Res Brain

Res Rev. 48(2):287-301.

Nigro M, Anjan M, Zhang L, Smirnov I, Colman H, Griffin C, Ozburn N (2005)

Integrated Array-Comparative Genomic Hybridization and Expression Array Profiles

Identify Clinically Relevant Molecular Subtypes of Glioblastoma. Cancer

Res., 65: 1678

Noguera-Troise, C. Daly, N. J. Papadopoulos (2006). Blockade of Dll4 inhibits

tumour growth by promoting non-productive angiogenesis. Nature, 444(7122):1032–

1037,.

Norden A.D., Drappatz J., and Wen P.Y. (2008). “Novel anti-angiogenic therapies for

malignant gliomas,” The Lancet Neurology, 7(12):1152–1160,.

O'Callaghan JP, Brinton RE, McEwen BS(1991). Glucocorticoids regulate the

synthesis of glial fibrillary acidic protein in intact and adrenalectomized rats but do

not affect its expression following brain injury. J Neurochem., 57(3):860-9.

Ochi K, Mori T, Toyama Y, Nakamura Y and Arakawa H (2002). Identification of

Semaphorin3B as a Direct Target of p53. Neoplasia, 4(1):82 – 87.

Ohgaki H and Kleihues P (2009). Genetic alterations and signaling pathways in the

evolution of gliomas. Cancer Sci., 100(12):2235-41.

Ohgaki H, Dessen P, Jourde B, Horstmann S, Nishikawa T (2004). Genetic pathways

to glioblastoma: a population-based study. Cancer Res., 64:6892–99

Ohgaki H, Kleihues P. (2007). Genetic pathways to primary and secondary

glioblastoma. Am. J. Pathol., 170:1445–53

Pollack IF. (1994). Brain tumors in children. N. Engl. J. Med., 331:1500–7

84

Ragel T. Brian, Couldwell T. William, Gillespie L. David & Jensen L.Randy (2007).

Identification of hypoxia-induced genes in a malignant glioma cell line (U-251) by

cDNA microarray analysis. Neurosurg Rev., 30:181–187

Rainov NG, Dobberstein KU, Bahn H, Holzhausen HJ, Lautenschlager C, et al.

(1997). Prognostic factors in malignant glioma: influence of the overexpression of

oncogene and tumor-suppressor gene products on survival. J. Neurooncol., 35:13–28

Rao RD, Uhm JH, Krishnan S, James CD. (2003). Genetic and signaling pathway

alterations in glioblastoma: relevance to novel targeted therapies. Front. Biosci.,

8:e270–80

Reifenberger G, Liu L, Ichimura K, Schmidt EE, Collins VP. (1993). Amplification

and overexpression of the MDM2 gene in a subset of human malignant gliomas

without p53 mutations. Cancer Res., 53:2736–39

Ridley AJ, Schwartz MA, Burridge K, et al. (2003). Cell migration: integrating

signals from front to back. Science, 302:1704–9.

Rutka JT, Muller M, Hubbard SL, et al. (1999). Astrocytoma adhesion to

extracellular matrix: functional significance of integrin and focal adhesion kinase

expression. J Neuropathol Exp Neurol., 58:198–209.

Sathornsumetee S, Rich JN. (2008). Designer therapies for glioblastoma multiforme.

Ann. N.Y. Acad. Sci., 1142:108–32

Shih AH, Holland EC. (2006). Platelet-derived growth factor (PDGF) and glial

tumorigenesis. Cancer Lett., 232:139–47

Shih AH, Holland EC. (2006). Platelet-derived growth factor (PDGF) and glial

tumorigenesis. Cancer Lett., 232:139–47

Shingo Takano,

Toshiharu Yamashita,

and Osamu Ohneda (2010). “

Molecular

Therapeutic Targets for Glioma Angiogenesis” Journal of Oncology, Article ID

351908, 11 pages doi:10.1155/2010/351908

85

Soni D, King JA, Kaye AH, Hovens CM. (2005). Genetics of glioblastoma

multiforme: mitogenic signaling and cell cycle pathways converge. J. Clin. Neurosci.,

12:1–5

Stamos J, Sliwkowski MX, Eigenbrot C (2002). Structure of the epidermal growth

factor receptor kinase domain alone and in complex with a 4-anilinoquinazoline

inhibitor. J Biol Chem., 277:46265-46272.

Stupp R, Mason WP, Van Den Bent MJ,Weller M, Fisher B, (2005). Radiotherapy

plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med.,

352:987–96

Takano S, Tsuboi K, Tomono Y, Mitsui Y and Nose T (2000). Tissue factor,

osteopontin, αvβ3 integrin expression in microvasculature of gliomas associated with

vascular endothelial growth factor expression. British Journal of Cancer,

82(12):1967–1973.

Takano T, Jane H.-C. Lin, Gregory Arcuino, Qun Gao, Jay Yang & Maiken

Nedergaard (2001). Glutamate release promotes growth of malignant gliomas ©

Nature, 7:1010 - 1015

Takano T, Lin JH, Arcuino G, Gao Q, Yang J, Nedergaard M (2001). Glutamate

release promotes growth of malignant gliomas. Nat Med., 7(9):1010-5.

Traxler P, Bold G, Buchdunger E (2001). Tyrosine kinase inhibitors: from rational

design to clinical trials. Med Res Rev,21:499-512.Wen PY, Kesari S. 2008.

Malignant gliomas in adults. N. Engl. J. Med., 359:492–507

van der Flier A, Sonnenberg A (2001). Function and interactions of integrins. Cell

Tissue Res., 305:285–98.

Weissenberger J, Loeffler S, Kappeler A, Kopf M, Lukes A, Afanasieva TA, Aguzzi

A, Weis J (2004). IL-6 is required for glioma development in a mouse model.

Oncogene, 23(19):3308-16.

86

Wiencke JK, Zheng S, Jelluma N, Tihan T, Vandenberg S (2007). Methylation of the

PTEN promoter defines low-grade gliomas and secondary glioblastoma. Neuro-

Oncology, 9:271–79

Yin D, Kawabata H, Tcherniamtchouk O, Huynh T, Black KL, Koeffler HP (2007).

Glioblastoma multiforme cells: expression of erythropoietin receptor and response to

erythropoietin. Int J Oncol., 31(5):1193-8.

Yu JM, Jun ES, Jung JS, Suh SY, Han JY, Kim JY, Kim KW, Jung JS (2007). Role

of Wnt5a in the proliferation of human glioblastoma cells. Cancer Lett., 257(2):172-

81.

Zagzag D, Friedlander DR, Miller DC, et al. (1995). Tenascin expression in

astrocytomas correlates with angiogenesis. Cancer Res, 55: 907–14.

Zbinden M, Duquet A, Lorente-Trigos A, Ngwabyt SN, Borges I, Ruiz i Altaba

A(2010). NANOG regulates glioma stem cells and is essential in vivo acting in a

cross-functional network with GLI1 and p53. EMBO J., 29(15):2659-74

87