Research ArticleIdentification of Gastric Cancer-Related Circular RNA throughMicroarray Analysis and Bioinformatics Analysis

Wei Gu1 Ying Sun1 Xiong Zheng1 Jin Ma1 Xiao-Ying Hu1

Tian Gao 2 andMei-Jie Hu 1

1Department of Gastroenterology Ruijin Hospital Luwan Branch Shanghai Jiaotong University School of Medicine Shanghai China2Department of Geriatrics Renji Hospital Shanghai Jiaotong University School of Medicine Shanghai China

Correspondence should be addressed to Tian Gao gaotianmedmailcomcn and Mei-Jie Hu mjhu0119126com

Received 5 July 2017 Accepted 13 December 2017 Published 18 March 2018

Academic Editor Akira Hara

Copyright copy 2018 Wei Gu et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Gastric cancer is one of the commonmalignant tumors worldwide Increasing studies have indicated that circular RNAs (circRNAs)play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets However theprecise mechanism and functions of most circRNAs are still unknown in gastric cancer In the present study we performed amicroarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples The miRNAexpression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) Thedifferentially expressed circRNAs and miRNAs were identified through fold change filtering The interactions between circRNAsand miRNAs were predicted by Arraystarrsquos home-made miRNA target prediction software After circRNA-related miRNAs anddysregulated miRNAs were intersected 23 miRNAs were selected The target mRNAs of miRNAs were predicted by TarBasev70 Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computationalmethods for the target mRNAs The results of pathway analysis showed that p53 signaling pathway and hippo signal pathwaywere significantly enriched and CCND2 was a cross-talk gene associated with them Finally a circRNA-miRNA-mRNA regulationnetwork was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes andhsa circRNA 101504 played a central role in the network

1 Introduction

Gastric cancer is one of the commonmalignant tumors in theclinic with the second leading cause of cancer-related deathworldwide [1 2] Although the treatment of gastric cancerhas gradually improved cure rate of gastric cancer is still lowWith a lack of obvious clinical symptoms at early stage mostpatients have lost the opportunity of surgical therapy whengastric cancer is detected at advanced stage [3] Recurrenceis the chief cause of gastric cancer-related death Accordingto recent statistics more than 30 of patients sufferingfrom stage III gastric cancer who undergo surgical resectiondevelop recurrence or distant metastasis with a 14-monthmedian recurrence-free survival time [4 5] Therefore pre-vention and cure of gastric cancer are still challenged in theclinic and the search for new molecular markers to monitorand intervene in gastric cancer carcinogenesis is urgent

Recent research has shown that circular RNAs (circR-NAs) play a critical role in the initiation and progression ofhuman diseases especially in tumors and may function aspotential molecular markers for disease diagnosis and treat-ment [6] Circular RNAs are a class of endogenous noncodingRNAs characterized by their covalently closed-loop struc-tures without a 51015840 cap or a 31015840 Poly(A) tail Previous studiesdemonstrated that circRNAs existed widely in all kinds oforganizations [7ndash9] and played a strong regulatory functionin cancer [10 11] For example circRNAs are dysregulated inepithelial tumors such as laryngeal cancer and digestive sys-tem cancers [12ndash14] and in stromal tumors such as gliomas[15] Compared with mRNAs circRNAs are more stable dueto the existence of ring structure Thus circRNA can servesas a convenient tool for qRT-PCR measurements in cancer

CircRNAs have been investigated for more than 40 years[16] but they have been considered as a result of splicing

HindawiBioMed Research InternationalVolume 2018 Article ID 2381680 9 pageshttpsdoiorg10115520182381680

2 BioMed Research International

errors for several decades and their biological functions arelargely unknown With the development of RNA sequenc-ing (RNA-seq) technologies and bioinformatics circRNAshave been extensively explored in recent years and severalfunctions of circRNA have been revealed such as acting asscaffolds in the assembly of protein complexes [17] seques-tering proteins from their native subcellular localization [18]modulating the expression of parental genes [19] regulatingalternative splicing [20] and RNA-protein interactions [21]and functioning as microRNA (miRNA) sponges [8]

Our study aimed to establish the expression profile of gas-tric cancer through circRNA microarray chip detection Ourresults revealed the potential role of circRNAs in gastric can-cer We also aimed to identify the hub circRNAs involved ingastric cancer through bioinformatics analysis The miRNAexpression profiles from the National Center of Biotech-nology Information Gene Expression Omnibus (GEO) wereused to identify circRNA-related dysregulated miRNAs ingastric cancer Gene ontology (GO) enrichment analysis andpathway analysis revealed the potential biology functionof miRNA target genes Finally a circRNA-miRNA-mRNAregulation networkwas constructed to selected hub genes andwe found that hsa circRNA 101504 played a central role in thenetwork

2 Methods

21 Clinical Samples Six pairs of tumor and adjacent nontu-mor tissues were obtained from patients with gastric cancerwho underwent surgery at the Ruijin Hospital ShanghaiJiaotong University School of Medicine between May 2011andMay 2014 None of the patients had received neoadjuvanttherapy and the samples were pathologically confirmedpostoperatively as gastric cancer The samples were takenwithin 10min after tumor excision immediately immersedin RNAlater stabilization solution (Thermo Fisher Scien-tific Carlsbad CA USA) and then stored at minus80∘C untilbeing used in the experiments The study was performed inaccordance with the ethical standards of the Declaration ofHelsinki andwas approved by the Ethics Committee of RuijinHospital Informed consent was obtained from all patientsparticipating in the present study

22 circRNA Microarray Analysis Total RNA from eachsample was quantified using the NanoDrop ND-1000 Thesample preparation and microarray hybridization were per-formed based on Arraystarrsquos standard protocols Briefly totalRNA from each sample was amplified and transcribed intofluorescent cRNA utilizing random primer according toArraystarrsquos Super RNA Labeling protocol (Arraystar Inc)The labeled cRNAs were hybridized onto the ArraystarHuman circRNA Array (6x7K Arraystar) After havingwashed the slides the arrays were scanned by the AxonGenePix 4000B microarray scanner Scanned images werethen imported into GenePix Pro 60 software (Axon) forgrid alignment and data extraction Quantile normalizationand subsequent data processing were performed using theR software package Differentially expressed circRNAs withstatistical significance between two groups were identified

through volcano plot filtering Differentially expressed cir-cRNAs between two samples were identified through foldchange filtering Hierarchical clustering was performed toshow the distinguishable circRNAs expression pattern amongsamples

23 Annotation for circRNAmiRNA Interaction Recent evi-dences have demonstrated that circular RNAs play a crucialrole in fine tuning the level of miRNA mediated regula-tion of gene expression by sequestering the miRNAs Theirinteraction with disease associated miRNAs indicates thatcircRNAs are important for disease regulation The cir-cRNAmicroRNA interaction was predicted with Arraystarrsquoshome-made miRNA target prediction software based onTargetScan [22] and miRanda [23] and the differentiallyexpressed circRNAs within all the comparisons were anno-tated in detail with the circRNA-miRNA interaction informa-tion

24miRNADatasets andDataAnalysis TheoriginalmiRNAexpression profile of GSE23739 used in the present studywas downloaded from the National Center of BiotechnologyInformation Gene Expression Omnibus (GEO) MicroRNAexpression of twenty pairs of tissue samples collected frompatients diagnosed with gastric cancer was determined bymiRNA microarrays (platform was GPL19071) in this dataEach pair included resected primary tumor and corre-sponding healthy gastric mucosa There were no replicatesDifferentially expressed miRNAs were identified by usingGEO2RThe target genes of differentially expressed miRNAswere predicted by using TarBase v70 [24] with a predictionscore ge08 and all the miRNA-mRNA interactions wereexperimentally supported

25 Gene Function Analysis Gene ontology (GO) enrich-ment analysis of miRNA target genes was implemented withDAVID (httpdavidabccncifcrfgov) GO terms (molecu-lar function biological processes and cellular components)with 119875 value less than 005 were considered significantlyenriched by differential expressed genes Kyoto Encyclopediaof Genes and Genomes (KEGG) is a database resource forunderstanding high-level functions and effects of the biolog-ical system (httpwwwgenomejpkegg) DAVID was alsoused to test the statistical enrichment of genes or target genesof miRNA with differential expression in KEGG pathwaysThe networks of the pathways and pathway-related geneswere constructed by using Cytoscape (version 340) pluginClueGO [25] + Cluepedia [26] app

26 Construction of the circRNA-miRNA-mRNA RegulationNetwork Significantly expressed circRNAs andmiRNAs andpredicted mRNAs were superimposed onto the circRNA-miRNA-mRNA network The network was constructed byusing Cytoscape (version 340) and the network topologywas analyzed by using CentiScaPe app [27]

27 Statistical Analysis Statistical analysis was performedusing SPSS 220 (Chicago IL USA) Significant differential

BioMed Research International 3

expression levels of circRNAs or miRNAs were analyzed byStudentrsquos 119905-test and FDR filtering was used for comparativeanalysis The 119875 value le 005 and absolute fold change ge20were considered statistically significant

3 Results

31 Screening of Differentially Expressed circRNAs and miR-NAs Expression profiling data of 2070 circRNAs wereobtained by using circRNAmicroarray analysisThe circRNAexpression levels were normalized to the same order ofmagnitude prior to the statistical analysis As shown ina box plot (Figure 1(a)) the median of different sampleswas almost on the same line after normalization whichshowed a great degree of standardization The scatter plotwas used to assess the circRNA expression variation betweenthe two compared groups of samples (Figure 1(b)) Witha threshold of 119875 value le 005 and absolute value of foldchange ge20 a total of 440 differentially expressed circRNAs(176 significantly upregulated circRNAs and 264 significantlydownregulated circRNAs) were screened (Table S1) Volcanoplot was used to visualize differential expression betweentumor group and adjacent nontumor group (Figure 1(c))Hierarchical clustering was performed based on differentiallyexpressed circRNAs to hypothesize the relationships betweensamples and the result of hierarchical clustering showed adistinguishable circRNA expression profiling among samples(Figure 1(d)) The miRNA expression profile of GSE23739was analyzed by using the online tool GEO2R The box plotshowed a great degree of standardization (Figure 2) With athreshold of 119875 value le 005 and absolute value of fold changege20 a total of 111 differentially expressed miRNAs including20 upregulatedmiRNAs and 91 downregulatedmiRNAs wereidentified (Table S2)

32 Prediction of circRNA-miRNA and miRNA-mRNA Inter-action Differentially expressed circRNAs contain corre-spondingmiRNAbinding sites To facilitate the investigationthe interactions between miRNAs and circRNAs were pre-dicted by Arraystarrsquos home-made miRNA target predictionsoftwareThe circRNAs with an absolute value of fold changege50 were selected for further analysis and 260 interactionsbetween 53 circRNAs and 187 miRNAs were screened 23miRNAs were selected after differentially expressed miRNAsand circRNA-related miRNAs were intersected (Figure 3)The target genes of the 23 miRNAs were predicted by usingTarBase v70 and 206 interactions between the 23 miRNAsand 150 mRNAs were obtained

33 e GO and KEGG Enrichment Analysis of the TargetGenes GO and KEGG enrichment analysis were performedfor the selected 150 mRNAs to investigate the biologicalfunction of the circRNAs In GO analysis all the results wereranked by enrichment score (minus log(119875 value)) and top 10 ofevery category were displayed in Figure 4 In the biologi-cal process analysis anteriorposterior pattern specificationliver development and transcription and DNA-templatedwere the top 3 enriched terms In the cellular componentanalysis cytoplasmic stress granule cytosol andnucleoplasm

were the top 3 enriched terms In themolecular function anal-ysis protein binding protein kinase binding and sequence-specific DNA binding were the top 3 enriched terms Resultsof KEGG pathway analysis were also ranked by enrichmentscore and the top 10 pathways associated with the mRNAswere listed in Figure 5(a) The network composed of themost enriched pathways and their related genes (Figure 5(b))showed that PARD6B GSK3B CCND2 CCNE1 PPP2CAand CDC27 were cross-talk genes associated with at least twopathways

34 Construction of the circRNA-miRNA-mRNA Regula-tion Network A circRNA-miRNA-mRNA network was con-structed to reveal the interactions in circRNA miRNA andmRNA As shown in Figure 6 hsa-miR-27a-3p had the mostdegrees and has circRNA 101504 had the most interactionswith miRNAs indicating that they were hub genes in theregulation network Dramatically hsa-miR-93-5p and hsa-miR-20b-5p and hsa-miR-454-3p and hsa-miR-301a-3p werecoupled miRNAs which had almost the same target genesThese coupled miRNAs might coregulate the target genesin the network In graph theory betweenness centrality isa measure of centrality in a graph based on shortest pathsand devised as a general measure of centrality A node withhigher betweenness centrality would have more control overthe network becausemore informationwill pass through thatnodeThe DEGs involved in the PPI network (betweenness gt4000) were listed in Table 1

4 Discussion

Gastric cancer is one of the deadliest solid tumors character-ized by complex molecular and cellular heterogeneity Overthe past few decades great efforts have been made to providenovel insights into the molecular mechanisms underlyinggastric cancer but the focus has been on protein-coding genesor miRNAs [28 29] Recently circRNAs has been widelyreported to participate in a wide range of biological processesand their dysregulated expression is associated with manycomplicated human disease phenotypes including cancers[30 31]

In this study microarray analysis was performed toobtain the expression profiles of circRNAs in gastric cancersamples and nonmalignant pancreas samplesThe expressionprofiles of miRNAs were obtained from GEO databases andanalyzed by using GEO2R With a threshold of 119875 value lt005 and absolute fold change ge20 dysregulated circRNAsand miRNAs were identified separately After the circRNA-related miRNAs dysregulated miRNAs were intersected 23miRNAs were selected for further study Gene functionanalysis including GO analysis and KEGG pathway analysiswas conducted for the targetmRNAs of the selectedmiRNAsThe results of KEGG pathway analysis indicated that p53signaling pathway and hippo signaling pathway were signifi-cantly enriched P53 is a well-known tumor suppressor geneand the p53 mutations have been reported in many cancers[32 33] In gastric cancer He et al [34] found that Fra-1 wasupregulated in gastric cancer tissues and played its functionby affecting the PI3KAkt and p53 signaling pathway Hippo

4 BioMed Research International

Samples

16

14

12

10

8

6

4

2

Nor

mal

ized

inte

nsity

val

ues

Nor

mal1

Nor

mal2

Nor

mal3

Nor

mal4

Nor

mal5

Nor

mal6

Tum

or1

Tum

or2

Tum

or3

Tum

or4

Tum

or5

Tum

or6

(a)

16

14

12

10

8

6

4

2161412108642

Gro

up-tu

mor

(nor

mal

ized

)

Group-normal (normalized)

(b)

0

2

4

6

8

10

12

minuslog10(P

val

ue)

minus5 0 5

log 2(fold change)Tumor versus normal

(c)

Tum

or1

Tum

or2

Tum

or3

Tum

or5

Tum

or4

Tum

or6

00 75 150

Nor

mal1

Nor

mal2

Nor

mal3

Nor

mal5

Nor

mal4

Nor

mal6

(d)

Figure 1 Differentially expressed circRNAs in tumor tissues and adjacent nontumor tissues from gastric cancer patients The box plot showsthe variations in circRNA expression (a) The scatter plot (b) and the volcano plot (c) illustrate the distributions of the data in the circRNAprofilesThe result from hierarchical clustering shows a distinguishable circRNA expression profiling among samplesThe heatmap shows thedifferentially expressed circRNAs in tumor and adjacent nontumor tissues (d) Each group consists of six samples Gene expression profilesare shown in rows ldquoRedrdquo indicates high relative expression and ldquobluerdquo indicates low relative expression

BioMed Research International 5

GSM

2452859

GSM

2452861

GSM

2452862

GSM

2452864

GSM

2452866

GSM

2452868

GSM

2452870

GSM

2452873

GSM

2452874

GSM

2452876

GSM

2452879

GSM

2452880

GSM

2452882

GSM

2452885

GSM

2452886

GSM

2452889

GSM

2452890

GSM

2452893

GSM

2452894

GSM

2452897

GSM

2452858

GSM

2452860

GSM

2452863

GSM

2452865

GSM

2452867

GSM

2452869

GSM

2452871

GSM

2452872

GSM

2452875

GSM

2452877

GSM

2452878

GSM

2452881

GSM

2452883

GSM

2452884

GSM

2452887

GSM

2452888

GSM

2452891

GSM

2452892

GSM

2452895

GSM

2452896

NormalCancer

3

2

1

0

minus1

minus2

minus3

Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples

Differentially expressed miRNAs circRNA-related miRNAs

Selected miRNAs

88(32)

23(84)

164(596)

Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing

signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported

thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed

5 Conclusion

In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their

6 BioMed Research International

0 1 2 3 4 5 6Anteriorposterior pattern specification

Liver developmentTranscription DNA-templated

Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter

Regulation of cell adhesionMuscle cell differentiation

G1S transition of mitotic cell cycleRegulation of mRNA stability

Negative regulation of myeloid cell differentiationCytoplasmic stress granule

CytosolNucleoplasm

NucleusSpindle poleCentrosome

Cytoplasmic mRNA processing bodyMembrane

Microtubule associated complexCytoplasm

Protein bindingProtein kinase binding

Sequence-specific DNA bindingPoly(A) RNA binding

Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding

Kinase activityProtein phosphatase binding

Nuclear localization sequence bindingPhosphoprotein binding

minuslog(P value)

Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms

0 05 1 15 2 25 3 35 4 45

Cell cycle

Oocyte meiosis

p53 signaling pathway

Axon guidance

Measles

MicroRNAs in cancer

Hepatitis B

Prostate cancer

Hippo signaling pathway

Neurotrophin signaling pathway

minuslog(P value)

(a)

PPP2CA

CCND2

CALM3

Oocyte meiosise

ZMAT3

ITPR3

p53 signaling pathway

CDC27

CDC25B

SESN3

WEE1

E2F3

CCNE1

CDKN1B

Cell cyclecyl

SEMA4C

Axon guidancei

LATS2

EPHA7

GSK3BPATJPARD6B

SLIT2

ROCK2

Hippo signaling nalipathway

(b)

Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown

function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work

Conflicts of Interest

The authors declare that there are no conflicts of interest

Authorsrsquo Contributions

Wei Gu and Ying Sun are equal contributors to this work

Acknowledgments

The authors thank CloudSeq Inc for the bioinformatic sup-port

BioMed Research International 7

Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)

Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011

hsa_circRNA_104575

hsa-miR-145-5p

CCNJ

hsa-miR-181b-5p

BIRC6

ANGPT2

hsa_circRNA_101222

PKD2

DYNC1LI2

SQSTM1

hsa_circRNA_104697hsa-miR-28-5p

FOXJ3

hsa-miR-193a-3p

PUM1

CDKN1B

hsa_circRNA_104168 AGPAT3

PTPRG

HOXC8

hsa-miR-196a-5p

HAND1

PSMD11

hsa_circRNA_105049

IGF2BP1

PRTG

HOXA5

KHSRP

GSK3BHOXB8

MSI2

ACLY

SLC9A6

GATAD2B

HOXA9

hsa_circRNA_103552

hsa-miR-21-5p

YOD1

BTG2

CCDC126EPHA7GATA6

HOXB6

KLHL15 FRS2 PARD6B

ADNP

CUL5

HBP1

hsa_circRNA_104634

hsa-miR-145-3p

CDC25Bhsa_circRNA_100383

KIF1BRAB14

QKI

MTPN

hsa_circRNA_103349

hsa-miR-214-3phsa_circRNA_102064

hsa_circRNA_101858

CNIH1

GALNT7

hsa_circRNA_101017

ASB1

ITPR3

BHLHE41

ADAMTS5

hsa-miR-148a-3p

NPTX1 KIAA0226

NRARP

SPIRE1

ARL6IP1 FAM178A

hsa_circRNA_101504

PGM2L1

HPRT1

hsa-miR-301a-3p

B4GALT5

CNOT4

MLLT10

PPP6R1

SESTD1ATP11A

C7orf60

hsa-miR-17-5p

ROCK2

hsa-miR-93-5p

CAMTA1

EFCAB14

hsa_circRNA_104651

RGMB

ZNF148

PSAP

RRAGDPTPN4

ZMAT3hsa_circRNA_103840

TSHZ1

STEAP4ANKRD52

TBCEL

NPAT

ARAP2

BRWD1

hsa-miR-454-3p

SMARCD2

LDLR

PPP2CAhsa-miR-652-5p

RC3H2

hsa_circRNA_104682

MAP1B

hsa_circRNA_100583

LATS2

CEP85L

hsa-miR-135b-5p

SLC6A5

CENPB

MAN2A1

ZBTB18 ZNF800

LONP2LONRF2

ABL2 PEG10

PRDM4

SLIT2

DLL1

CBX4

hsa-miR-15a-5pTMEM245

CCNE1

DDX3XWEE1

CHAC1

ARL2

PAFAH1B1RB1CC1

ATP13A3

DCP2

NABP1

SPTY2D1

hsa-miR-27a-3p

SESN3

CDK16

RORA

JARID2

CHAMP1

NUFIP2

FAM118A

MFHAS1

SEMA4C

KPNA6

hsa_circRNA_104533

hsa-miR-125b-5pZC3H7B

hsa_circRNA_100319

KPNB1

hsa_circRNA_102700

TNPO1

hsa-miR-27b-3p

CALM3

RASL11B

hsa-miR-20b-5p

hsa_circRNA_105039

PFN2

TBC1D9TMEM167A

hsa-miR-18b-5p

hsa_circRNA_102062

INADL

TAOK1

GIGYF1

CAMK2N1

CADhsa-miR-18a-5p

RNF187

E2F3

CACUL1

CDC27ZNF367

SPRTN

CCND2

ARNTL2STAT3

MAP3K9

SUV420H1

ANKRD13C

PRR15

hsa-miR-660-3p

APH1A

hsa_circRNA_104374FAM98A

hsa_circRNA_100013

NOP9

mRNAmiRNAcircRNA

minus85 85

Fold change

0

Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression

8 BioMed Research International

Supplementary Materials

Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)

References

[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015

[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015

[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014

[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014

[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016

[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013

[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013

[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013

[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016

[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015

[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016

[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015

[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016

[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016

[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016

[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like

structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976

[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017

[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012

[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015

[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014

[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016

[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003

[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012

[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015

[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009

[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013

[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009

[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015

[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014

[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017

[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017

[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012

[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993

4 BioMed Research International

Samples

16

14

12

10

8

6

4

2

Nor

mal

ized

inte

nsity

val

ues

Nor

mal1

Nor

mal2

Nor

mal3

Nor

mal4

Nor

mal5

Nor

mal6

Tum

or1

Tum

or2

Tum

or3

Tum

or4

Tum

or5

Tum

or6

(a)

16

14

12

10

8

6

4

2161412108642

Gro

up-tu

mor

(nor

mal

ized

)

Group-normal (normalized)

(b)

0

2

4

6

8

10

12

minuslog10(P

val

ue)

minus5 0 5

log 2(fold change)Tumor versus normal

(c)

Tum

or1

Tum

or2

Tum

or3

Tum

or5

Tum

or4

Tum

or6

00 75 150

Nor

mal1

Nor

mal2

Nor

mal3

Nor

mal5

Nor

mal4

Nor

mal6

(d)

Figure 1 Differentially expressed circRNAs in tumor tissues and adjacent nontumor tissues from gastric cancer patients The box plot showsthe variations in circRNA expression (a) The scatter plot (b) and the volcano plot (c) illustrate the distributions of the data in the circRNAprofilesThe result from hierarchical clustering shows a distinguishable circRNA expression profiling among samplesThe heatmap shows thedifferentially expressed circRNAs in tumor and adjacent nontumor tissues (d) Each group consists of six samples Gene expression profilesare shown in rows ldquoRedrdquo indicates high relative expression and ldquobluerdquo indicates low relative expression

BioMed Research International 5

GSM

2452859

GSM

2452861

GSM

2452862

GSM

2452864

GSM

2452866

GSM

2452868

GSM

2452870

GSM

2452873

GSM

2452874

GSM

2452876

GSM

2452879

GSM

2452880

GSM

2452882

GSM

2452885

GSM

2452886

GSM

2452889

GSM

2452890

GSM

2452893

GSM

2452894

GSM

2452897

GSM

2452858

GSM

2452860

GSM

2452863

GSM

2452865

GSM

2452867

GSM

2452869

GSM

2452871

GSM

2452872

GSM

2452875

GSM

2452877

GSM

2452878

GSM

2452881

GSM

2452883

GSM

2452884

GSM

2452887

GSM

2452888

GSM

2452891

GSM

2452892

GSM

2452895

GSM

2452896

NormalCancer

3

2

1

0

minus1

minus2

minus3

Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples

Differentially expressed miRNAs circRNA-related miRNAs

Selected miRNAs

88(32)

23(84)

164(596)

Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing

signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported

thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed

5 Conclusion

In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their

6 BioMed Research International

0 1 2 3 4 5 6Anteriorposterior pattern specification

Liver developmentTranscription DNA-templated

Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter

Regulation of cell adhesionMuscle cell differentiation

G1S transition of mitotic cell cycleRegulation of mRNA stability

Negative regulation of myeloid cell differentiationCytoplasmic stress granule

CytosolNucleoplasm

NucleusSpindle poleCentrosome

Cytoplasmic mRNA processing bodyMembrane

Microtubule associated complexCytoplasm

Protein bindingProtein kinase binding

Sequence-specific DNA bindingPoly(A) RNA binding

Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding

Kinase activityProtein phosphatase binding

Nuclear localization sequence bindingPhosphoprotein binding

minuslog(P value)

Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms

0 05 1 15 2 25 3 35 4 45

Cell cycle

Oocyte meiosis

p53 signaling pathway

Axon guidance

Measles

MicroRNAs in cancer

Hepatitis B

Prostate cancer

Hippo signaling pathway

Neurotrophin signaling pathway

minuslog(P value)

(a)

PPP2CA

CCND2

CALM3

Oocyte meiosise

ZMAT3

ITPR3

p53 signaling pathway

CDC27

CDC25B

SESN3

WEE1

E2F3

CCNE1

CDKN1B

Cell cyclecyl

SEMA4C

Axon guidancei

LATS2

EPHA7

GSK3BPATJPARD6B

SLIT2

ROCK2

Hippo signaling nalipathway

(b)

Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown

function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work

Conflicts of Interest

The authors declare that there are no conflicts of interest

Authorsrsquo Contributions

Wei Gu and Ying Sun are equal contributors to this work

Acknowledgments

The authors thank CloudSeq Inc for the bioinformatic sup-port

BioMed Research International 7

Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)

Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011

hsa_circRNA_104575

hsa-miR-145-5p

CCNJ

hsa-miR-181b-5p

BIRC6

ANGPT2

hsa_circRNA_101222

PKD2

DYNC1LI2

SQSTM1

hsa_circRNA_104697hsa-miR-28-5p

FOXJ3

hsa-miR-193a-3p

PUM1

CDKN1B

hsa_circRNA_104168 AGPAT3

PTPRG

HOXC8

hsa-miR-196a-5p

HAND1

PSMD11

hsa_circRNA_105049

IGF2BP1

PRTG

HOXA5

KHSRP

GSK3BHOXB8

MSI2

ACLY

SLC9A6

GATAD2B

HOXA9

hsa_circRNA_103552

hsa-miR-21-5p

YOD1

BTG2

CCDC126EPHA7GATA6

HOXB6

KLHL15 FRS2 PARD6B

ADNP

CUL5

HBP1

hsa_circRNA_104634

hsa-miR-145-3p

CDC25Bhsa_circRNA_100383

KIF1BRAB14

QKI

MTPN

hsa_circRNA_103349

hsa-miR-214-3phsa_circRNA_102064

hsa_circRNA_101858

CNIH1

GALNT7

hsa_circRNA_101017

ASB1

ITPR3

BHLHE41

ADAMTS5

hsa-miR-148a-3p

NPTX1 KIAA0226

NRARP

SPIRE1

ARL6IP1 FAM178A

hsa_circRNA_101504

PGM2L1

HPRT1

hsa-miR-301a-3p

B4GALT5

CNOT4

MLLT10

PPP6R1

SESTD1ATP11A

C7orf60

hsa-miR-17-5p

ROCK2

hsa-miR-93-5p

CAMTA1

EFCAB14

hsa_circRNA_104651

RGMB

ZNF148

PSAP

RRAGDPTPN4

ZMAT3hsa_circRNA_103840

TSHZ1

STEAP4ANKRD52

TBCEL

NPAT

ARAP2

BRWD1

hsa-miR-454-3p

SMARCD2

LDLR

PPP2CAhsa-miR-652-5p

RC3H2

hsa_circRNA_104682

MAP1B

hsa_circRNA_100583

LATS2

CEP85L

hsa-miR-135b-5p

SLC6A5

CENPB

MAN2A1

ZBTB18 ZNF800

LONP2LONRF2

ABL2 PEG10

PRDM4

SLIT2

DLL1

CBX4

hsa-miR-15a-5pTMEM245

CCNE1

DDX3XWEE1

CHAC1

ARL2

PAFAH1B1RB1CC1

ATP13A3

DCP2

NABP1

SPTY2D1

hsa-miR-27a-3p

SESN3

CDK16

RORA

JARID2

CHAMP1

NUFIP2

FAM118A

MFHAS1

SEMA4C

KPNA6

hsa_circRNA_104533

hsa-miR-125b-5pZC3H7B

hsa_circRNA_100319

KPNB1

hsa_circRNA_102700

TNPO1

hsa-miR-27b-3p

CALM3

RASL11B

hsa-miR-20b-5p

hsa_circRNA_105039

PFN2

TBC1D9TMEM167A

hsa-miR-18b-5p

hsa_circRNA_102062

INADL

TAOK1

GIGYF1

CAMK2N1

CADhsa-miR-18a-5p

RNF187

E2F3

CACUL1

CDC27ZNF367

SPRTN

CCND2

ARNTL2STAT3

MAP3K9

SUV420H1

ANKRD13C

PRR15

hsa-miR-660-3p

APH1A

hsa_circRNA_104374FAM98A

hsa_circRNA_100013

NOP9

mRNAmiRNAcircRNA

minus85 85

Fold change

0

Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression

8 BioMed Research International

Supplementary Materials

Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)

References

[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015

[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015

[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014

[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014

[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016

[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013

[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013

[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013

[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016

[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015

[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016

[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015

[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016

[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016

[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016

[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like

structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976

[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017

[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012

[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015

[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014

[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016

[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003

[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012

[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015

[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009

[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013

[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009

[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015

[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014

[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017

[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017

[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012

[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993

BioMed Research International 5

GSM

2452859

GSM

2452861

GSM

2452862

GSM

2452864

GSM

2452866

GSM

2452868

GSM

2452870

GSM

2452873

GSM

2452874

GSM

2452876

GSM

2452879

GSM

2452880

GSM

2452882

GSM

2452885

GSM

2452886

GSM

2452889

GSM

2452890

GSM

2452893

GSM

2452894

GSM

2452897

GSM

2452858

GSM

2452860

GSM

2452863

GSM

2452865

GSM

2452867

GSM

2452869

GSM

2452871

GSM

2452872

GSM

2452875

GSM

2452877

GSM

2452878

GSM

2452881

GSM

2452883

GSM

2452884

GSM

2452887

GSM

2452888

GSM

2452891

GSM

2452892

GSM

2452895

GSM

2452896

NormalCancer

3

2

1

0

minus1

minus2

minus3

Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples

Differentially expressed miRNAs circRNA-related miRNAs

Selected miRNAs

88(32)

23(84)

164(596)

Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing

signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported

thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed

5 Conclusion

In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their

6 BioMed Research International

0 1 2 3 4 5 6Anteriorposterior pattern specification

Liver developmentTranscription DNA-templated

Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter

Regulation of cell adhesionMuscle cell differentiation

G1S transition of mitotic cell cycleRegulation of mRNA stability

Negative regulation of myeloid cell differentiationCytoplasmic stress granule

CytosolNucleoplasm

NucleusSpindle poleCentrosome

Cytoplasmic mRNA processing bodyMembrane

Microtubule associated complexCytoplasm

Protein bindingProtein kinase binding

Sequence-specific DNA bindingPoly(A) RNA binding

Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding

Kinase activityProtein phosphatase binding

Nuclear localization sequence bindingPhosphoprotein binding

minuslog(P value)

Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms

0 05 1 15 2 25 3 35 4 45

Cell cycle

Oocyte meiosis

p53 signaling pathway

Axon guidance

Measles

MicroRNAs in cancer

Hepatitis B

Prostate cancer

Hippo signaling pathway

Neurotrophin signaling pathway

minuslog(P value)

(a)

PPP2CA

CCND2

CALM3

Oocyte meiosise

ZMAT3

ITPR3

p53 signaling pathway

CDC27

CDC25B

SESN3

WEE1

E2F3

CCNE1

CDKN1B

Cell cyclecyl

SEMA4C

Axon guidancei

LATS2

EPHA7

GSK3BPATJPARD6B

SLIT2

ROCK2

Hippo signaling nalipathway

(b)

Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown

function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work

Conflicts of Interest

The authors declare that there are no conflicts of interest

Authorsrsquo Contributions

Wei Gu and Ying Sun are equal contributors to this work

Acknowledgments

The authors thank CloudSeq Inc for the bioinformatic sup-port

BioMed Research International 7

Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)

Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011

hsa_circRNA_104575

hsa-miR-145-5p

CCNJ

hsa-miR-181b-5p

BIRC6

ANGPT2

hsa_circRNA_101222

PKD2

DYNC1LI2

SQSTM1

hsa_circRNA_104697hsa-miR-28-5p

FOXJ3

hsa-miR-193a-3p

PUM1

CDKN1B

hsa_circRNA_104168 AGPAT3

PTPRG

HOXC8

hsa-miR-196a-5p

HAND1

PSMD11

hsa_circRNA_105049

IGF2BP1

PRTG

HOXA5

KHSRP

GSK3BHOXB8

MSI2

ACLY

SLC9A6

GATAD2B

HOXA9

hsa_circRNA_103552

hsa-miR-21-5p

YOD1

BTG2

CCDC126EPHA7GATA6

HOXB6

KLHL15 FRS2 PARD6B

ADNP

CUL5

HBP1

hsa_circRNA_104634

hsa-miR-145-3p

CDC25Bhsa_circRNA_100383

KIF1BRAB14

QKI

MTPN

hsa_circRNA_103349

hsa-miR-214-3phsa_circRNA_102064

hsa_circRNA_101858

CNIH1

GALNT7

hsa_circRNA_101017

ASB1

ITPR3

BHLHE41

ADAMTS5

hsa-miR-148a-3p

NPTX1 KIAA0226

NRARP

SPIRE1

ARL6IP1 FAM178A

hsa_circRNA_101504

PGM2L1

HPRT1

hsa-miR-301a-3p

B4GALT5

CNOT4

MLLT10

PPP6R1

SESTD1ATP11A

C7orf60

hsa-miR-17-5p

ROCK2

hsa-miR-93-5p

CAMTA1

EFCAB14

hsa_circRNA_104651

RGMB

ZNF148

PSAP

RRAGDPTPN4

ZMAT3hsa_circRNA_103840

TSHZ1

STEAP4ANKRD52

TBCEL

NPAT

ARAP2

BRWD1

hsa-miR-454-3p

SMARCD2

LDLR

PPP2CAhsa-miR-652-5p

RC3H2

hsa_circRNA_104682

MAP1B

hsa_circRNA_100583

LATS2

CEP85L

hsa-miR-135b-5p

SLC6A5

CENPB

MAN2A1

ZBTB18 ZNF800

LONP2LONRF2

ABL2 PEG10

PRDM4

SLIT2

DLL1

CBX4

hsa-miR-15a-5pTMEM245

CCNE1

DDX3XWEE1

CHAC1

ARL2

PAFAH1B1RB1CC1

ATP13A3

DCP2

NABP1

SPTY2D1

hsa-miR-27a-3p

SESN3

CDK16

RORA

JARID2

CHAMP1

NUFIP2

FAM118A

MFHAS1

SEMA4C

KPNA6

hsa_circRNA_104533

hsa-miR-125b-5pZC3H7B

hsa_circRNA_100319

KPNB1

hsa_circRNA_102700

TNPO1

hsa-miR-27b-3p

CALM3

RASL11B

hsa-miR-20b-5p

hsa_circRNA_105039

PFN2

TBC1D9TMEM167A

hsa-miR-18b-5p

hsa_circRNA_102062

INADL

TAOK1

GIGYF1

CAMK2N1

CADhsa-miR-18a-5p

RNF187

E2F3

CACUL1

CDC27ZNF367

SPRTN

CCND2

ARNTL2STAT3

MAP3K9

SUV420H1

ANKRD13C

PRR15

hsa-miR-660-3p

APH1A

hsa_circRNA_104374FAM98A

hsa_circRNA_100013

NOP9

mRNAmiRNAcircRNA

minus85 85

Fold change

0

Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression

8 BioMed Research International

Supplementary Materials

Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)

References

[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015

[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015

[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014

[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014

[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016

[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013

[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013

[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013

[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016

[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015

[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016

[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015

[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016

[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016

[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016

[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like

structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976

[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017

[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012

[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015

[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014

[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016

[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003

[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012

[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015

[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009

[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013

[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009

[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015

[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014

[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017

[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017

[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012

[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993

6 BioMed Research International

0 1 2 3 4 5 6Anteriorposterior pattern specification

Liver developmentTranscription DNA-templated

Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter

Regulation of cell adhesionMuscle cell differentiation

G1S transition of mitotic cell cycleRegulation of mRNA stability

Negative regulation of myeloid cell differentiationCytoplasmic stress granule

CytosolNucleoplasm

NucleusSpindle poleCentrosome

Cytoplasmic mRNA processing bodyMembrane

Microtubule associated complexCytoplasm

Protein bindingProtein kinase binding

Sequence-specific DNA bindingPoly(A) RNA binding

Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding

Kinase activityProtein phosphatase binding

Nuclear localization sequence bindingPhosphoprotein binding

minuslog(P value)

Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms

0 05 1 15 2 25 3 35 4 45

Cell cycle

Oocyte meiosis

p53 signaling pathway

Axon guidance

Measles

MicroRNAs in cancer

Hepatitis B

Prostate cancer

Hippo signaling pathway

Neurotrophin signaling pathway

minuslog(P value)

(a)

PPP2CA

CCND2

CALM3

Oocyte meiosise

ZMAT3

ITPR3

p53 signaling pathway

CDC27

CDC25B

SESN3

WEE1

E2F3

CCNE1

CDKN1B

Cell cyclecyl

SEMA4C

Axon guidancei

LATS2

EPHA7

GSK3BPATJPARD6B

SLIT2

ROCK2

Hippo signaling nalipathway

(b)

Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown

function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work

Conflicts of Interest

The authors declare that there are no conflicts of interest

Authorsrsquo Contributions

Wei Gu and Ying Sun are equal contributors to this work

Acknowledgments

The authors thank CloudSeq Inc for the bioinformatic sup-port

BioMed Research International 7

Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)

Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011

hsa_circRNA_104575

hsa-miR-145-5p

CCNJ

hsa-miR-181b-5p

BIRC6

ANGPT2

hsa_circRNA_101222

PKD2

DYNC1LI2

SQSTM1

hsa_circRNA_104697hsa-miR-28-5p

FOXJ3

hsa-miR-193a-3p

PUM1

CDKN1B

hsa_circRNA_104168 AGPAT3

PTPRG

HOXC8

hsa-miR-196a-5p

HAND1

PSMD11

hsa_circRNA_105049

IGF2BP1

PRTG

HOXA5

KHSRP

GSK3BHOXB8

MSI2

ACLY

SLC9A6

GATAD2B

HOXA9

hsa_circRNA_103552

hsa-miR-21-5p

YOD1

BTG2

CCDC126EPHA7GATA6

HOXB6

KLHL15 FRS2 PARD6B

ADNP

CUL5

HBP1

hsa_circRNA_104634

hsa-miR-145-3p

CDC25Bhsa_circRNA_100383

KIF1BRAB14

QKI

MTPN

hsa_circRNA_103349

hsa-miR-214-3phsa_circRNA_102064

hsa_circRNA_101858

CNIH1

GALNT7

hsa_circRNA_101017

ASB1

ITPR3

BHLHE41

ADAMTS5

hsa-miR-148a-3p

NPTX1 KIAA0226

NRARP

SPIRE1

ARL6IP1 FAM178A

hsa_circRNA_101504

PGM2L1

HPRT1

hsa-miR-301a-3p

B4GALT5

CNOT4

MLLT10

PPP6R1

SESTD1ATP11A

C7orf60

hsa-miR-17-5p

ROCK2

hsa-miR-93-5p

CAMTA1

EFCAB14

hsa_circRNA_104651

RGMB

ZNF148

PSAP

RRAGDPTPN4

ZMAT3hsa_circRNA_103840

TSHZ1

STEAP4ANKRD52

TBCEL

NPAT

ARAP2

BRWD1

hsa-miR-454-3p

SMARCD2

LDLR

PPP2CAhsa-miR-652-5p

RC3H2

hsa_circRNA_104682

MAP1B

hsa_circRNA_100583

LATS2

CEP85L

hsa-miR-135b-5p

SLC6A5

CENPB

MAN2A1

ZBTB18 ZNF800

LONP2LONRF2

ABL2 PEG10

PRDM4

SLIT2

DLL1

CBX4

hsa-miR-15a-5pTMEM245

CCNE1

DDX3XWEE1

CHAC1

ARL2

PAFAH1B1RB1CC1

ATP13A3

DCP2

NABP1

SPTY2D1

hsa-miR-27a-3p

SESN3

CDK16

RORA

JARID2

CHAMP1

NUFIP2

FAM118A

MFHAS1

SEMA4C

KPNA6

hsa_circRNA_104533

hsa-miR-125b-5pZC3H7B

hsa_circRNA_100319

KPNB1

hsa_circRNA_102700

TNPO1

hsa-miR-27b-3p

CALM3

RASL11B

hsa-miR-20b-5p

hsa_circRNA_105039

PFN2

TBC1D9TMEM167A

hsa-miR-18b-5p

hsa_circRNA_102062

INADL

TAOK1

GIGYF1

CAMK2N1

CADhsa-miR-18a-5p

RNF187

E2F3

CACUL1

CDC27ZNF367

SPRTN

CCND2

ARNTL2STAT3

MAP3K9

SUV420H1

ANKRD13C

PRR15

hsa-miR-660-3p

APH1A

hsa_circRNA_104374FAM98A

hsa_circRNA_100013

NOP9

mRNAmiRNAcircRNA

minus85 85

Fold change

0

Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression

8 BioMed Research International

Supplementary Materials

Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)

References

[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015

[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015

[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014

[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014

[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016

[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013

[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013

[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013

[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016

[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015

[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016

[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015

[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016

[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016

[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016

[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like

structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976

[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017

[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012

[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015

[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014

[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016

[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003

[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012

[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015

[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009

[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013

[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009

[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015

[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014

[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017

[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017

[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012

[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993

BioMed Research International 7

Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)

Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011

hsa_circRNA_104575

hsa-miR-145-5p

CCNJ

hsa-miR-181b-5p

BIRC6

ANGPT2

hsa_circRNA_101222

PKD2

DYNC1LI2

SQSTM1

hsa_circRNA_104697hsa-miR-28-5p

FOXJ3

hsa-miR-193a-3p

PUM1

CDKN1B

hsa_circRNA_104168 AGPAT3

PTPRG

HOXC8

hsa-miR-196a-5p

HAND1

PSMD11

hsa_circRNA_105049

IGF2BP1

PRTG

HOXA5

KHSRP

GSK3BHOXB8

MSI2

ACLY

SLC9A6

GATAD2B

HOXA9

hsa_circRNA_103552

hsa-miR-21-5p

YOD1

BTG2

CCDC126EPHA7GATA6

HOXB6

KLHL15 FRS2 PARD6B

ADNP

CUL5

HBP1

hsa_circRNA_104634

hsa-miR-145-3p

CDC25Bhsa_circRNA_100383

KIF1BRAB14

QKI

MTPN

hsa_circRNA_103349

hsa-miR-214-3phsa_circRNA_102064

hsa_circRNA_101858

CNIH1

GALNT7

hsa_circRNA_101017

ASB1

ITPR3

BHLHE41

ADAMTS5

hsa-miR-148a-3p

NPTX1 KIAA0226

NRARP

SPIRE1

ARL6IP1 FAM178A

hsa_circRNA_101504

PGM2L1

HPRT1

hsa-miR-301a-3p

B4GALT5

CNOT4

MLLT10

PPP6R1

SESTD1ATP11A

C7orf60

hsa-miR-17-5p

ROCK2

hsa-miR-93-5p

CAMTA1

EFCAB14

hsa_circRNA_104651

RGMB

ZNF148

PSAP

RRAGDPTPN4

ZMAT3hsa_circRNA_103840

TSHZ1

STEAP4ANKRD52

TBCEL

NPAT

ARAP2

BRWD1

hsa-miR-454-3p

SMARCD2

LDLR

PPP2CAhsa-miR-652-5p

RC3H2

hsa_circRNA_104682

MAP1B

hsa_circRNA_100583

LATS2

CEP85L

hsa-miR-135b-5p

SLC6A5

CENPB

MAN2A1

ZBTB18 ZNF800

LONP2LONRF2

ABL2 PEG10

PRDM4

SLIT2

DLL1

CBX4

hsa-miR-15a-5pTMEM245

CCNE1

DDX3XWEE1

CHAC1

ARL2

PAFAH1B1RB1CC1

ATP13A3

DCP2

NABP1

SPTY2D1

hsa-miR-27a-3p

SESN3

CDK16

RORA

JARID2

CHAMP1

NUFIP2

FAM118A

MFHAS1

SEMA4C

KPNA6

hsa_circRNA_104533

hsa-miR-125b-5pZC3H7B

hsa_circRNA_100319

KPNB1

hsa_circRNA_102700

TNPO1

hsa-miR-27b-3p

CALM3

RASL11B

hsa-miR-20b-5p

hsa_circRNA_105039

PFN2

TBC1D9TMEM167A

hsa-miR-18b-5p

hsa_circRNA_102062

INADL

TAOK1

GIGYF1

CAMK2N1

CADhsa-miR-18a-5p

RNF187

E2F3

CACUL1

CDC27ZNF367

SPRTN

CCND2

ARNTL2STAT3

MAP3K9

SUV420H1

ANKRD13C

PRR15

hsa-miR-660-3p

APH1A

hsa_circRNA_104374FAM98A

hsa_circRNA_100013

NOP9

mRNAmiRNAcircRNA

minus85 85

Fold change

0

Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression

8 BioMed Research International

Supplementary Materials

Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)

References

[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015

[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015

[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014

[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014

[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016

[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013

[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013

[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013

[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016

[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015

[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016

[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015

[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016

[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016

[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016

[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like

structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976

[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017

[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012

[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015

[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014

[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016

[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003

[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012

[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015

[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009

[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013

[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009

[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015

[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014

[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017

[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017

[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012

[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993

8 BioMed Research International

Supplementary Materials

Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)

References

[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015

[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015

[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014

[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014

[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016

[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013

[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013

[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013

[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016

[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015

[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016

[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015

[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016

[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016

[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016

[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like

structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976

[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017

[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012

[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015

[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014

[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016

[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003

[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012

[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015

[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009

[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013

[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009

[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015

[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014

[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017

[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017

[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012

[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993

BioMed Research International 9

[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015

[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011

[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013

[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015

[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016

[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011

[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017

[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015

Stem Cells International

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

MEDIATORSINFLAMMATION

of

EndocrinologyInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Disease Markers

Hindawiwwwhindawicom Volume 2018

BioMed Research International

OncologyJournal of

Hindawiwwwhindawicom Volume 2013

Hindawiwwwhindawicom Volume 2018

Oxidative Medicine and Cellular Longevity

Hindawiwwwhindawicom Volume 2018

PPAR Research

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Immunology ResearchHindawiwwwhindawicom Volume 2018

Journal of

ObesityJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Computational and Mathematical Methods in Medicine

Hindawiwwwhindawicom Volume 2018

Behavioural Neurology

OphthalmologyJournal of

Hindawiwwwhindawicom Volume 2018

Diabetes ResearchJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Research and TreatmentAIDS

Hindawiwwwhindawicom Volume 2018

Gastroenterology Research and Practice

Hindawiwwwhindawicom Volume 2018

Parkinsonrsquos Disease

Evidence-Based Complementary andAlternative Medicine

Volume 2018Hindawiwwwhindawicom

Submit your manuscripts atwwwhindawicom

Stem Cells International

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

MEDIATORSINFLAMMATION

of

EndocrinologyInternational Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Disease Markers

Hindawiwwwhindawicom Volume 2018

BioMed Research International

OncologyJournal of

Hindawiwwwhindawicom Volume 2013

Hindawiwwwhindawicom Volume 2018

Oxidative Medicine and Cellular Longevity

Hindawiwwwhindawicom Volume 2018

PPAR Research

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Immunology ResearchHindawiwwwhindawicom Volume 2018

Journal of

ObesityJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Computational and Mathematical Methods in Medicine

Hindawiwwwhindawicom Volume 2018

Behavioural Neurology

OphthalmologyJournal of

Hindawiwwwhindawicom Volume 2018

Diabetes ResearchJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Research and TreatmentAIDS

Hindawiwwwhindawicom Volume 2018

Gastroenterology Research and Practice

Hindawiwwwhindawicom Volume 2018

Parkinsonrsquos Disease

Evidence-Based Complementary andAlternative Medicine

Volume 2018Hindawiwwwhindawicom

Submit your manuscripts atwwwhindawicom