RESEARCH ARTICLE
Characterization of Differentially Expresd
Genes Involved in Pathways Associated with
Gastric Cancer
Hao Li 1,2,Beiqin Yu 1,Jianfang Li 1,Liping Su 1,Min Yan 1,3,Jun Zhang 1,2,Chen Li 1,3,
Zhenggang Zhu 1,3,Bingya Liu 1*梦见有人追求我
1Shanghai Key Laboratory of Gastric Neoplasms,Shanghai Institute of Digestive Surgery,Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine,Shanghai 200025,People ’s Republic of China,
2Department of Oncology,Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai
200025,People ’s Republic of China,3Department of Surgery,Ruijin Hospital,Shanghai Jiao Tong
University School of Medicine,Shanghai 200025,People ’s Republic of China
*byliu@sjtu.edu
Abstract To explore the patterns of gene expression in gastric cancer,a total of 26paired gastric can-cer and noncancerous tissues from patients were enrolled for gene expression microarray analys.Limma methods were applied to analyze the data,and genes were considered to be significantly differentially expresd if the Fal Discovery Rate (FDR)value was <0.01,P -value was <0.01and the fold change (FC)was >2.Subquently,Gene Ontology (GO)categories were ud to analyze the main functions of the differentially expresd genes.According to the Kyoto Encyclopedia of Genes and Genomes (KEGG)databa,we found pathways significantly associated
with the differential genes.Gene-Act network and co-expression network were built respectively bad on the relationships among the genes,proteins and compounds in the databa.2371mRNAs and 350lncRNAs considered as significantly differentially expresd genes were lected for the further analysis.The GO categories,pathway analys and the Gene-Act network showed a consistent result that up-regulated genes were responsible for tumorigenesis,migration,angiogenesis and mi-croenvironment formation,while down-regulated genes were involved in metabolism.The results of this study provide some novel findings on coding RNAs,lncRNAs,path-ways and the co-expression network in gastric cancer which will be uful to guide further investigation and target therapy for this dia.Introduction Gastric cancer (GC)is one of the most common cancers worldwide,and its incidence is partic-ularly high in Eastern Asia,especially in China.Approximately 952,000new cas of stomach cancer were diagnod worldwide in 2012,and half of them occurred in Eastern Asia (mainly in China)[1].In China,the majority of patients with GC are diagnod at a late stage
with
句号的作用OPEN ACCESS
Citation:Li H,Yu B,Li J,Su L,Yan M,Zhang J,et al.
(2015)Characterization of Differentially Expresd
Genes Involved in Pathways Associated with Gastric
Cancer.PLoS ONE 10(4):e0125013.doi:10.1371/
journal.pone.0125013
Academic Editor:Francisco J.Esteban,University
of Jaén,SPAIN
Received:November 9,2014
感慨人生Accepted:March 6,2015
Published:April 30,2015
Copyright:©2015Li et al.This is an open access
article distributed under the terms of the Creative
Commons Attribution Licen ,which permits
unrestricted u,distribution,and reproduction in any
medium,provided the original author and source are
credited.
Data Availability Statement:All relevant data are
within the paper and its Supporting Information files.
All microarray files are available from the NCBI Gene
Expression Omnibus (GEO)databa (accession
number “GSE65801”;bi.v/
geo/i?acc=GSE65801).
Funding:This work was supported by grants for
analysis from the National Natural Science
Foundation of China [No.81172324,No.91229106,
No.81272749,and No.81372231],Science and
Technology Commission of Shanghai Municipality
[No.13ZR1425600],and Key Projects in the National
Science &Technology Pillar Program of China (No.
poor prognosis.Therefore,elucidating the molecular mechanisms underlying GC progression is esntial to identifying key biomarkers and developing effective targeted therapies.Over the last decade,gene expression microarrays have become a common tool for examining gene transcript levels in cancer rearch.Microarray data is ud for a wide variety of analys,such as unsupervid clustering,classification,differential expression analysis,and expression
mapping of quantitative trait loci [2].It not only helps to identify key dysfunctional genes in can-
cer but provides genome-wide information on gene expression at one time as well[3,4].In this
study,we performed a genome-wide survey of the expression of lncRNAs and mRNAs from
paired samples of primary gastric cancer tissues and noncancerous tissues,to profile the differen-
tially expresd lncRNAs and coding RNAs.Study of the data will provide valuable informa-
tion on the mechanism of carcinogenesis and allow discovery of key genes that may act as future
targets of anti-cancer therapy.
Methods and Materials
Ethical statement
Written informed connt was obtained from all participants.The study was approved by the
Human Rearch Ethics Committee of Ruijin Hospital,Shanghai Jiao Tong University,School
of Medicine.
Tissue samples
Tissues were taken from primary gastric carcinomas from untreated patients who underwent
D2radical gastrectomy in Shanghai Ruijin Hospital.For each cancer tissue,a paired noncan-
cerous tissue sample was collected from the adjacent region at the same time.The size of each
sample was around 0.1cm 3.All the samples were placed in RNALater within 15minutes after
excision and stored in liquid nitrogen until RNA extraction.In this study,32paired tissues
were collected for the microarray and 26paired samples were enrolled for the next-step analy-
sis of GO,pathway and network after quality control using 3D Principal component analysis
(3D-PCA)and Cluster analysis.
Microarray experiments
青皮的功效与作用
Agilent SurePrint G3Human GE 8x60K Microarray (Design ID:028004)was employed in this
study.Total RNA was isolated and amplified using a Low Input Quick Amp Labeling Kit,One-
Color (Cat#5190–2305,Agilent technologies,US).Then,the labeled cRNAs were purified by a
腰组词RNeasy mini kit (Cat#74106,QIAGEN,Germany).
Bad on the manufacturer ’s instructions,each slide was hybridized with 600ng Cy3-labeled
cRNA using a Gene Expression Hybridization Kit (Cat#5188–5242,Agilent technologies,US)
and washed by the Gene Expression Wash Buffer Kit (Cat#5188–5327,Agilent technologies,US).梦到坟地
An Agilent Microarray Scanner (Cat#G2565CA,Agilent technologies,US)and Feature Ex-
traction software 10.7(Agilent technologies,US)were applied to scan each slide with the same
ttings shown as follow,Dye channel:Green,Scan resolution =3μm,20bit.The raw data were
normalized by the Quantile algorithm,Gene Spring Software 11.0(Agilent technologies,US)
(detailed in S5Table ).
Limma
Linear models and empirical Bayes methods were applied to analyze the data in this study.The
resulting P -values were adjusted using the BH FDR algorithm.There were three standards
for
2014BAI09B03).The funders had no role in study
design,data collection and analysis,decision to
publish,or preparation of the manuscript.
Competing Interests:The authors have declared
that no competing interests exist.
us to consider that a gene was significantly differentially expresd,the FDR value was <0.01,
P -value was <0.01and the fold change was >2.(detailed in S5Table )
复读生GO category
We performed Gene Ontology (GO)analys to analyze the functions of the differentially ex-
presd genes in our microarray according to the key functional classification of The National
Center for Biotechnology Information (NCBI).Generally,Fisher ’s exact test and the χ2test
were applied to classify the GO category,and the fal discovery rate (FDR,FDR ¼1ÀNk )was calculated to correct the P -value (N k refers to the number of Fisher ’s test P -values less than the
χ2test P -values).The enrichment Re was given by:Re =(n f /n )/(N f /N )in the significant catego-
ries (N f is the number of differential genes within the particular category,n is the total number
of genes within the same category,n f is the number of differential genes in the entire microar-
ray,and N is the total number of genes in the microarray.)(detailed in S5Table ).
Pathway analys
Pathway annotations of the differential exresd genes were obtained from KEGG (www.
genome.jp/kegg/).Pathway categories with a FDR <0.01were marked.The enrichment of sig-nificant pathways was given by:enrichment =ng
na
ÀÁ/Ng Na ÀÁ,which helped us to locate more signifi-cant pathways in our study (n g is the number of differential genes within the particular
pathway,n a is the total number of genes within the same pathway,N g is the number of differ-
ential genes which have at least one pathway annotation,and N a is the number of genes which
have at least one pathway annotation in the entire microarray.)(detailed in S5Table ).
Gene-Act network
According to the KEGG databa,one gene may be involved in veral pathways or interact
with veral other genes.All the gene —gene interactions were pooled together to build the
Gene-Act network bad on the differential pathways,which helped us to reveal the signaling
pathways and key regulatory genes in GC.
Co-expression network
Gene co-expression Network was built according to the normalized signal intensity of specific
expression genes.Degree centrality is defined as the number of links one node has to another,
which determines the relative importance of genes.What ’s more,k-cores were applied as a
method of simplifying the graph topology analys.Core regulatory factors (genes)which have
the highest degrees connect most adjacent genes and build the structure of the network (de-
tailed in S5Table ).
Real-time quantitative PCR
Total RNA was extracted from tissues using the Trizol reagent (Invitrogen)according to the
manufacturer ’s instructions.The quantitative real-time polymera chain reaction (PCR)was
performed by using SYBR-green PCR Master Mix in a Fast Real-time PCR 7500System (Ap-
plied Biosystems).The primers of the 10genes were showed in S4Table .PCR reactions were
performed at 50°C for 2min,followed by 40cycles of 95°C for 15s and 60°C for 1min.ΔCt
was calculated by subtracting the Ct of β-actin RNA (control)from the Ct of the RNA of sam-
ple,respectively.ΔΔCt was then calculated by subtracting the ΔCt of the control from the ΔCt
of the sample.Fold change was calculated by the equation 2-ΔΔCt.
Statistical analysis
SPSS software19and Microsoft Excel2010was ud to analyze the data.Expression levels be-tween cancer tissues and adjacent noncancerous tissues were analyzed by paired-sample t-tests. P-values below0.05were regarded as statistically significant.
Results
Microarray analys
In total,42,405human genes were profiled in our study by using an Agilent G3Human GE家装装修合同
8x60K microarray.We have submitted our datat in the repository of“Gene Expression Om-nibus”and the accession number was“GSE65801”(bi.v/geo/query/ i?acc=GSE65801).We ud linear models and empirical Bayes methods to analyze the data(e Methods).There were2371mRNAs and350lncRNAs considered as the differentially expresd genes by limma for the next-step analysis(Fig1A).
Among all2371differential mRNAs,there are1142mRNAs down-regulated and1229 mRNAs up-regulated in our obrvation on alterations of gene expression between gastric can-cer and control tissues(Fig1C).Most of the differential mRNAs have been proven to be corre-lated with carcinogenesis and metastasis in most types of cancer(Table1).The genes such as GKN2,PGC,MUC6,CHIA,PSCA and FBP2were among the top20down-regulated genes, while KLK8,SFRP4,INHBA,CLDN1,CST1,FAP,SPP1,OLFM4,and KRT17were among the top20up-regulated genes(Table1).However,some genes such as HOXC9,FNDC1,STRA6, KCNE2,PGA3and
KCNJ16haven’t been reported in gastric cancer and their roles remain un-known(Table1).
In addition,we found193down-regulated lncRNAs and156up-regulated lncRNAs among a total of350differential lncRNAs bad on the profiling(Fig1B).Most of the lncRNAs have not been given an official names and their functions remain unknown.However,some have been reported playing critical roles in cancer,such as H19,GUCY1B2,MEG3and AKR7L (Table2).
In our previous report[36],the fold change(FC)of H19in74gastric cancer versus paired noncancerous tissues was6.015,with a P-value of0.017.This result was consistent with the data of H19(Absolute FC=6.06)in this microarray analys.Furthermore,over-expression of H19contributes to the proliferation,migration,invasion and metastasis of gastric cancer. Gene Ontology categories
All the differentially expresd genes were classified into different functional categories accord-ing to the Gene Ontology(GO)project for biological process.Bad on our microarray data, GO analys indicated that208GO terms were enriched(P<0.01,FDR<0.01)(S1Table).The primary GO categories for170up-regulated GO terms were focud on cell adhesion,angio-genesis,multicellular organism development,axon guidance,skeletal system development,col-lagen fibril organization,positive regulation of angiogenesis,wounding and negative regulation of cell proliferation(Fig2A).The main G
O categories for down-regulated genes were digestion, xenobiotic metabolic process,transmembrane transport,ion transport,small molecule meta-bolic process,negative regulation of growth,glutathione metabolic process,cellular respon to cadmium ion and metabolic process(Fig2B).
According to the differential genes and functions,we built a GO Tree to explore the interac-tions among all the differential GO categories.The diversity in the categories when compar-ing cancerous and control tissues suggested that gastric cancer may be associated with significantly up-regulated cell migration,cell proliferation,angiogenesis,cell—cell adhesion
Fig1.Differentially expresd genes in a gene expression microarray of26pairs of gastric cancer and noncancerous tissues.A)Volcano plot showing the differential genes(red dots)in the expression microarray(P-value<0.01,FDR<0.01).B)Clustering heatmap showing the differential lncRNAs. Each column reprents one sample and each row reprents one differential lncRNA.C)Clustering heatmap showing the differential mRNAs.Each column reprents one sample and each row reprents one differential mRNA.
doi:10.1371/journal.pone.0125013.g001
