Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2014.2300 mmr-10-02-0713 Articles Differential co-expression analysis of a microarray gene expression profiles of pulmonary adenocarcinoma FUSHIJIE PANXUFENG FANGWENTAO Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, P.R. China Correspondence to: Dr Wentao Fang, Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, No. 241 Huaihai East Road, Xuhui, Shanghai 200030, P.R. China, E-mail: chest2013@126.com 8 2014 05 06 2014 10 2 713 718 02 09 2013 17 03 2014 Copyright © 2014, Spandidos Publications 2014 This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. The article may be redistributed, reproduced, and reused for non-commercial purposes, provided the original source is properly cited.

Lung cancer severely reduces the quality of life worldwide and causes high socioeconomic burdens. However, key genes leading to the generation of pulmonary adenocarcinoma remain elusive despite intensive research efforts. The present study aimed to identify the potential associations between transcription factors (TFs) and differentially co-expressed genes (DCGs) in the regulation of transcription in pulmonary adenocarcinoma. Gene expression profiles of pulmonary adenocarcinoma were downloaded from the Gene Expression Omnibus, and gene expression was analyzed using a computational method. A total of 37,094 differentially co-expressed links (DCLs) and 251 DCGs were identified, which were significantly enriched in 10 pathways. The construction of the regulatory network and the analysis of the regulatory impact factors revealed eight crucial TFs in the regulatory network. These TFs regulated the expression of DCGs by promoting or inhibiting their expression. In addition, certain TFs and target genes associated with DCGs did not appear in the DCLs, which indicated that those TFs could be synergistic with other factors. This is likely to provide novel insights for research into pulmonary adenocarcinoma. In conclusion, the present study may enhance the understanding of disease mechanisms and lead to an improved diagnosis of lung cancer. However, further studies are required to confirm these observations.

 pulmonary adenocarcinoma differentially co-expressed genes pathway Introduction

Globally, lung cancer is the leading cause of cancer mortality in males and the second leading cause of cancer mortality in females with ~1.6 million new lung cancer cases and 1.4 million mortalities expected to occur in one year (1). In China, the most frequently diagnosed cancer is lung cancer (2), and Chinese females have higher lung cancer rates than females in several European countries (3).

Almost 40% of lung cancer cases are adenocarcinoma, which usually originates in the peripheral lung tissue (4). Although several studies have assessed gene expression (5–7) or provided novel diagnostic aids (8) for pulmonary adenocarcinoma, key genes leading to the deterioration of pulmonary adenocarcinoma remain to be fully evaluated. Extensive efforts have been made to uncover the basic mechanisms underlying the initiation and progression of pulmonary adenocarcinoma, as well as to target these processes for diagnostics at molecular and genetic levels. Key genes and transcription factors (TFs) have an important role in the study of the disease. For example, the expression of the receptor of activated C kinase, which is an important 36-kDa cytosolic protein (9), was reported to be a useful biomarker for pulmonary adenocarcinoma (10). Furthermore, thyroid transcription factor-1 gene amplification has been discovered in certain types of lung adenocarcinoma, and this finding has been useful to inhibit transforming growth factor-β-mediated epithelial-to-mesenchymal transition in pulmonary adenocarcinoma cells (11). Compared with traditional research methods, DNA microarrays are one of the most popular technologies for studying the expression of genes at a large scale and ultimately associating them with diseases (12). The molecular mechanisms of pulmonary adenocarcinoma have yet to be fully understood, and a large-scale study of genes associated with this disease is necessary.

The aim of the present study was to explore the biochemical pathways leading to the deterioration of patients with pulmonary adenocarcinoma at the gene transcription level using a computational bioinformatics analysis of gene expression. Furthermore, the study aimed to identify the potential association between TFs and differentially co-expressed genes (DCGs) in the regulation of transcription. The present study may provide the groundwork to enable the exploration of the most variable genes leading to pulmonary adenocarcinoma.

 Materials and methods

All patients provided informed consent prior to their inclusion in the present study, and all human studies were approved by the Ethics Committee of Shanghai Jiaotong University (Shanghai, China) and performed in accordance with the ethical standards.

Affymetrix microarray data and differential expression analysis

The gene expression profile of GSE 2514 (7) was downloaded from a public functional genomics data repository, the Gene Expression Omnibus (GEO), which was based on the Affymetrix GPL8300 platform (Affymetrix Human Genome U95, Version 2 Array; Affymetrix, Inc., Santa Clara, CA, USA). These expression data were deposited by Stearman et al (7). A total of 39 samples, including 20 human lung cancer tissue samples and 19 human normal lung tissue samples, were analyzed with one replicate each.

The R package was used to analyze the gene expression profile (http://r-project.org/). The CEL source files were processed into expression estimates, and background correction and quartile data normalization were performed using the Robust Multi-array Average algorithm (13). The probability of genes being differentially expressed between pulmonary adenocarcinoma samples and normal samples was computed using the limma package (14). The t-test method was used to identify DEGs (15,16). P-values <0.05 and |logFC|>0.5 were considered to be statistically significant. The DCsum, DCp and DCe functions in the Differential Co-expression Analysis and Differential Regulation Analysis of Gene Expression Microarray Data (DCGL) (17,18) (part of the R package) were used to evaluate DCGs and differentially co-expressed links (DCLs). A Q-value <0.05 was defined as the cut-off criterion.

 Pathway enrichment analysis of DCGs

The Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY database is a comprehensive database containing various biochemical pathways (19). This database records networks of molecular interactions in cells, as well as variants of these networks specific to particular organisms. To explore the dysfunctional pathways in pulmonary adenocarcinoma samples, each group of genes was assessed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (20) for pathway enrichment analysis. The DAVID is a program that detects an enrichment of genes with specific gene ontology, KEGG and SwissProt terms.

 Construction of a transcriptional regulatory network

TRANSFAC® (21) is a database of TFs, their genomic binding sites and DNA-binding profiles. TRANSFAC® comprises numerous data sheets, including SITE, GENE, FACTOR, CLASS, MATRIX, CELLS, METHOD and REFERENCE. The association between downloaded human TFs and target genes in the database was analyzed. A total of 298 TFs and 6,495 TF-target pairs were selected. The DCLs were mapped to the TF-target pairs and the results were associated with the known target genes to obtain transcriptional regulation interrelations. Cytoscape (22) software was used for the construction of a transcriptional regulatory network.

 Results Differential gene expression in cancer tissue samples compared with normal samples

The gene expression profile of GSE 2514 was downloaded from the GEO database. Considering the scale of the calculations in the DCGL, pulmonary adenocarcinoma microarray data were analyzed and filtered using the limma and Affy packages to obtain the DEGs. A total of 1,379 DEGs were obtained with a P-value <0.05 and |logFC|>0.5. The DCGL in the R package was used to screen DCLs and DCGs. When Q<0.05 was used as the cut-off criterion, a total of 251 DCGs and 37,094 DCLs were obtained.

 Identification of dysregulated pathways

In order to identify the dysregulated pathways in pulmonary adenocarcinoma samples, pathway enrichment analysis was performed using the online biological classification tool DAVID. A total of 10 pathways with a P-value <0.1 were enriched (Fig. 1). The most significant pathway was glycolysis/gluconeogenesis (P=5.49×10−6). The other significant pathways included the cell cycle (P=8.84×10−5), complement and coagulation cascades (P=0.018029) and DNA replication (P=0.044864). Ten significant pathways are listed in Table I.

 TF regulatory network

All DCLs discovered in the present study were compared with the TRANSFAC database, and 10 TF-target gene pairs associated with pulmonary adenocarcinoma were observed, including eight TFs. The correlation difference (cor. diff.)-value (the sum of absolute values of the maximum absolute correlation for TF and target gene) indicated the degree of correlation between the TF and target gene (Table II). By mapping eight TFs to the TRANSFAC database again, 366 regulation associations that were linked with those TFs were obtained. By processing these associations with Cytoscape software, eight TFs and 308 target genes were identified in the transcriptional regulatory network. Furthermore, several TFs and target genes belonging to DCGs did not appear among the DCLs in the present study, which indicated that those TFs may be synergistic with other factors. The results are illustrated in Fig. 2.

 Discussion

The present study investigated gene expression profiles in patients with lung cancer and healthy controls to explore the biochemical pathways leading to the deterioration associated with pulmonary adenocarcinoma at the gene transcription level using a computational bioinformatics method. Furthermore, the study identified the most variable genes leading to pulmonary adenocarcinoma according to the associations between TFs and target genes. A total of 251 DCGs and 37,094 DCLs were obtained following DCGL analysis. These 251 DCGs were significantly enriched in 10 pathways. In addition, certain DCGs belonging to TF-target pairs did not appear among the DCLs. These TF-target gene pairs may have a synergistic effect with other TFs, which may provide novel insights for research into pulmonary adenocarcinoma.

In the present study, KEGG PATHWAY analysis was used to identify the dysregulated pathways in pulmonary adenocarcinoma samples; 10 pathways, including glycolysis/gluconeogenesis, the cell cycle and the complement and coagulation cascades, were highlighted. These pathways were associated with energy conversion, cell replication and the immune response. The most significant pathway was glycolysis/gluconeogenesis. Glycolysis involves the conversion of glucose into pyruvate and the generation of small amounts of adenosine triphosphate and nicotinamide adenine dinucleotide, and gluconeogenesis is a pathway leading to the synthesis of glucose from noncarbohydrate precursors. Gluconeogenesis is the reverse of glycolysis, but involves minor variations or alternative paths. A series of DCGs were included in the glycolysis/gluconeogenesis pathway, including phosphoglycerate kinase 1 (PGK1). Chen et al (23) suggested that four proteins, including the protein encoded by PGK1, that are involved in the glycolysis pathway are overexpressed in pulmonary adenocarcinoma; this overexpression was suggested to be associated with low survival rates in patients with pulmonary adenocarcinoma. A study on breast cancer demonstrated that tumors had abnormal bioenergetics, and patients with cancer showed a systematic loss of energy involving the interaction of tumor glycolysis and gluconeogenesis (24). The results of the differential co-expression analysis in the present study are in accordance with previous studies and indicated that the genes in the glycolysis/gluconeogenesis pathway have an important role in the development and progression of pulmonary adenocarcinoma.

A transcriptional regulatory network with 308 target genes and eight TFs was obtained using Cytoscape software. In this network, 19 target genes and all of the eight TFs [Fli-1 proto-oncogene, ETS transcription factor (FLI1), v-ets erythroblastosis virus E26 oncogene homolog 2 (ETS2), hypoxia-inducible factor 1α (HIF1A), peroxisome proliferator-activated receptor γ (PPARG), lymphoid enhancer-binding factor 1 (LEF1), ets variant 4 (ETV4), jun D proto-oncogene (JUND) and vitamin D receptor (VDR)] were DCGs. The most significant correlation of TF-target gene pairs was FLI1-cyclin-dependent kinase inhibitor 1A (CDKN1A) with a cor. diff.-value of 1.294162235. Other significant correlations included ETS2-urokinase-type plasminogen activator (PLAU) (cor. diff.-value, 1.225511357) and HIF1A-nuclear receptor subfamily 4, group A, member 1 (NR4A1) (cor. diff.-value, 1.222672148). Correlations of TF-target genes are listed in Table II.

FLI1 is a member of the ETS family of TFs characterized by the presence of the evolutionary conserved DNA-binding (ETS) domain, which recognizes the purine-rich GGA (A/T) core sequence (25). Sankar et al (26) suggested that the EWS RNA-binding protein 1 gene fuses with FLI1 to produce the EWS/FLI fusion protein, which is the abnormal TF that drives tumor development in Ewing sarcoma. CDKN1A is a type of CDK inhibitor (CKI), and is a cell cycle inhibitor gene regulated by VDR. The expression or stability of CKIs is reduced in tumors and leads to organ hyperplasia and increased tumor susceptibility (27). CDKN1A plays essential roles in the DNA damage response by inducing cell cycle arrest and directly inhibiting DNA replication, as well as by regulating fundamental processes, including apoptosis and transcription (28). The deletion of CDKN1A improves stem cell function and increases the lifespan of mice with dysfunctional telomeres without accelerating cancer formation (29). To date, genes whose expression has been reported to be repressed by EWS/FLI include CDKN1A (30,31).

It was also observed in the present study that DNA-binding transcription factors have three target genes. The expression of ETS2 has been shown to be elevated in certain cancer tissue samples and have a significant role in cancer progression (32). The genes regulated by ETS2 are those encoding enzymes that degrade the extracellular matrix, including stromelysin and collagen (33). The most significant relevant target gene to ETS2 is PLAU, which is currently used as a diagnostic marker (34). PLAU is closely correlated with the expression of a series of genes in lung cancer cell lines (35). However, in renal cancer cells, PLAU showed cancer cell-specific methylation that did not correlate well with expression status (36) and it was not specifically associated with colon cancer (37). The results of the cotransfection of the PLAU enhancer-CAT construct in the presence of increasing amounts of the ETS2-β-galactosidase expression vector showed that the co-expression of the ETS dominant-negative protein resulted in the almost complete inhibition of the PLAU enhancer induction (38). The second relevant gene to ETS2 is JUNB, which represents an important target in diseases associated with cancer and fibrosis (39). The expression of JUNB is inactivated by methylation in chronic myeloid leukemia (40). The third relevant gene to ETS2 is matrix metalloproteinase-9 (MMP9). The expression of MMP9 in non-small cell lung cancer contributes to tumor cell invasiveness (41). ETS can regulate MMPs, and MMP9 expression was shown to be suppressed by ETS blockage through overexpression of a dominant-negative form of ETS1 (42).

HIF1A encodes a pivotal TF that regulates angiogenesis by inducing the expression of vascular endothelial growth factor, interleukin-8 and a basic fibroblast growth factor (43). HIF1A is considered to be one of the key regulators of tumor angiogenesis. NR4A1 is the target gene of HIF1A. NR4A1 was demonstrated to reduce the migration of normal and breast cancer cell lines (44). Results regarding the knockdown or overexpression of NR4A1 in lung cancer cells suggest that this receptor exhibits pro-oncogenic activity and enhances cell survival and/or proliferation (45).

The other TFs examined in the present study showed less correlation with their target genes, but these also had an important role in the molecular mechanism of the disease. PPARG is a member of the PPAR family, which has a pivotal role in adipogenesis and glucose homeostasis (46). LEF1 mRNA levels are important statistical metrics in cancer. A study on ovarian, fallopian tube and peritoneal cancer indicated that LEF1 overexpression may be predictive of poor overall survival (47). Furthermore, ETV4 was demonstrated to be a useful marker in study on prostate cancer (48), and JUND and its target gene PLAU were shown to have an important role in gene expression in cancer cells (49). The TF with the least correlation was VDR, which only had five target genes on the map. This indicates that VDR is an important site for carcinogenesisP.

In conclusion, a total of 10 pathways were enriched in pulmonary adenocarcinoma tissue samples, and the most significant pathway of DCGs in the present study was glycolysis/gluconeogenesis. Ten TF-target gene pairs that were associated with pulmonary adenocarcinoma were discovered in the transcriptional regulatory network. Co-expression analysis of these TFs and target genes may provide novel ideas for cancer research, and the results of the present study may provide groundwork enabling the investigation of the most variable genes leading to the development and progression of pulmonary adenocarcinoma. However, further experiments are required to confirm these observations.

 References FerlayJShinHRBrayFFormanDMathersCParkinDMEstimates of worldwide burden of cancer in 2008: GLOBOCAN 2008Int J Cancer127289329172010 JemalACenterMMDeSantisCWardEMGlobal patterns of cancer incidence and mortality rates and trendsCancer Epidemiol Biomarkers Prev19189319072010 MackayJEriksenMShafeyOThe Tobacco Atlas2nd editionAmerican Cancer SocietyAtlanta, GA2006 HongWKBastRCHaitWNKufeDWPollockREWeichselbaumRRHollandHFFreiEIIICancer Medicine8th editionPMPHNew York, NY2010 GarberMETroyanskayaOGSchluensKPetersenSThaeslerZPacyna-GengelbachMvan de RijnMRosenGDPerouCMWhyteRIDiversity of gene expression in adenocarcinoma of the lungProc Natl Acad Sci USA9813784137892001 Director’s Challenge Consortium for the Molecular Classification of Lung AdenocarcinomaSheddenKTaylorJMEnkemannSATsaoMSYeatmanTJGeraldWLEschrichSJurisicaIGiordanoTJMisekDEGene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation studyNat Med200814822827 StearmanRSDwyer-NieldLZerbeLBlaineSAChanZBunnPAJrJohnsonGLHirschFRMerrickDTFranklinWAAnalysis of orthologous gene expression between human pulmonary adenocarcinoma and a carcinogen-induced murine modelAm J Pathol167176317752005 JiangHDengYChenHSTaoLShaQChenJTsaiCJZhangSJoint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genesBMC Bioinformatics5812004 RonDChenCHCaldwellJJamiesonLOrrEMochly-RosenDCloning of an intracellular receptor for protein kinase C: a homolog of the beta subunit of G proteinsProc Natl Acad Sci USA918398431994 NagashioRSatoYMatsumotoTKageyamaTSatohYShinichiroRMasudaNGoshimaNJiangSXOkayasuIExpression of RACK1 is a novel biomarker in pulmonary adenocarcinomasLung Cancer6954592010 SaitoRAWatabeTHoriguchiKKohyamaTSaitohMNagaseTMiyazonoKThyroid transcription factor-1 inhibits transforming growth factor-β-mediated epithelial-to-mesenchymal transition in lung adenocarcinoma cellsCancer Res69278327912009 FangGKuangRPandeyGSteinbachMMyersCLKumarVSubspace differential coexpression analysis: problem definition and a general approachPac Symp Biocomput1451562010 IrizarryRAHobbsBCollinFBeazer-BarclayYDAntonellisKJScherfUSpeedTPExploration, normalization, and summaries of high density oligonucleotide array probe level dataBiostatistics42492642003 DibounIWernischLOrengoCAKoltzenburgMMicroarray analysis after RNA amplification can detect pronounced differences in gene expression using limmaBMC Genomics72522006 LeeHKHsuAKSajdakJQinJPavlidisPCoexpression analysis of human genes across many microarray data setsGenome Res14108510942004 StuartJMSegalEKollerDKimSKA gene-coexpression network for global discovery of conserved genetic modulesScience3022492552003 LiuBHYuHTuKLiCLiYXLiYYDCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray dataBioinformatics26263726382010 YuHLiuBHYeZQLiCLiYXLiYYLink-based quantitative methods to identify differentially coexpressed genes and gene pairsBMC Bioinformatics123152011 KanehisaMGotoSKEGG: kyoto encyclopedia of genes and genomesNucleic Acids Res2827302000 Huang daWShermanBTTanQCollinsJRAlvordGRoayaeiJStephensRBaselerMWLaneHCLempickiRAThe DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene listsGenome Biol8R1832007 WingenderEChenXHehlRKarasHLiebichIMatysVMeinhardtTPrüssMReuterISchachererFTRANSFAC: an integrated system for gene expression regulationNucleic Acids Res283163192000 SmootMEOnoKRuscheinskiJWangPLIdekerTCytoscape 2.8: new features for data integration and network visualizationBioinformatics274314322011 ChenGGharibTGWangHHuangCCKuickRThomasDGSheddenKAMisekDETaylorJMGiordanoTJProtein profiles associated with survival in lung adenocarcinomaProc Natl Acad Sci USA10013537135422003 PerumalSSShanthiPSachdanandamPTherapeutic effect of tamoxifen and energy-modulating vitamins on carbohydrate-metabolizing enzymes in breast cancerCancer Chemother Pharmacol561051142005 OikawaTYamadaTMolecular biology of the Ets family of transcription factorsGene30311342003 SankarSGomezNCBellRPatelMDavisIJLessnickSLLuoWEWS and RE1-silencing transcription factor inhibit neuronal phenotype development and oncogenic transformation in Ewing sarcomaGenes Cancer42132232013 LüdtkeTHFarinHFRudatCSchuster-GosslerKPetryMBarnettPChristoffelsVMKispertATbx2 controls lung growth by direct repression of the cell cycle inhibitor genes Cdkn1a and Cdkn1bPLoS Genet9e10031892013 CazzaliniOScovassiAISavioMStivalaLAProsperiEMultiple roles of the cell cycle inhibitor p21(CDKN1A) in the DNA damage responseMutat Res70412202010 ChoudhuryARJuZDjojosubrotoMWSchienkeALechelASchaetzleinSJiangHStepczynskaAWangCBuerJCdkn1a deletion improves stem cell function and lifespan of mice with dysfunctional telomeres without accelerating cancer formationNat Genet39991052007 SchwentnerRMolecular mechanisms of transcriptional repression by the EWS-FLI1 oncogene in Ewing’s sarcomaPhD dissertationUniversity of ViennaPublication no. AC078843812008 RiggiNCironiLProveroPSuvàMLKaloulisKGarcia-EcheverriaCHoffmannFTrumppAStamenkovicIDevelopment of Ewing’s sarcoma from primary bone marrow-derived mesenchymal progenitor cellsCancer Res6511459114682005 LiuAYCoreyEVessellaRLLangePHTrueLDHuangGMNelsonPSHoodLIdentification of differentially expressed prostate genes: increased expression of transcription factor ETS-2 in prostate cancerProstate301451531997 ButticéGKurkinenMOncogenes control stromelysin and collagenase gene expressionContrib Nephrol1071011071994 SudolMFrom Rous sarcoma virus to plasminogen activator, src oncogene and cancer managementOncogene30300330102011 WatanabeTMiuraTDegawaYFujitaYInoueMKawaguchiMFurihataCComparison of lung cancer cell lines representing four histopathological subtypes with gene expression profiling using quantitative real-time PCRCancer Cell Int1022010 Ibanez de CaceresIDulaimiEHoffmanAMAl-SaleemTUzzoRGCairnsPIdentification of novel target genes by an epigenetic reactivation screen of renal cancerCancer Res66502150282006 ManessLGoktepeIChenHAhmednaMSangSImpact of Phytolacca americana extracts on gene expression of colon cancer cellsPhytother ResApr42013(Epub ahead of print)10.1002/ptr.4979 CirilloGCasalinoLValloneDCaraccioloADe CesareDVerdePRole of distinct mitogen-activated protein kinase pathways and cooperation between Ets-2, ATF-2, and Jun family members in human urokinase-type plasminogen activator gene induction by interleukin-1 and tetradecanoyl phorbol acetateMol Cell Biol19624062521999 GervasiMBianchi-SmiragliaACummingsMZhengQWangDLiuSBakinAVJunB contributes to Id2 repression and the epithelial-mesenchymal transition in response to transforming growth factor-βJ Cell Biol1965896032012 YangMYLiuTCChangJGLinPMLinSFJunB gene expression is inactivated by methylation in chronic myeloid leukemiaBlood101320532112003 LiuPLTsaiJRHwangJJChouSHChengYJLinFYChenYLHungCYChenWCChenYHChongIWHigh-mobility group box 1-mediated matrix metalloproteinase-9 expression in non-small cell lung cancer contributes to tumor cell invasivenessAm J Resp Cell Mol Biol435305382010 SahinAVercamerCKaminskiAFuchsTFlorinAHahneJCMattotVPourtier-ManzanedoAPietschTFafeurVWernertNDominant-negative inhibition of Ets 1 suppresses tumor growth, invasion and migration in rat C6 glioma cells and reveals differentially expressed Ets 1 target genesInt J Oncol343772009 ChaSTChenPSJohanssonGChuCYWangMYJengYMYuSLChenJSChangKJJeeSHMicroRNA-519c suppresses hypoxia-inducible factor-1 alpha expression and tumor angiogenesisCancer Res70267526852010 AlexopoulouANLeaoMCaballeroOLDa SilvaLReidLLakhaniSRSimpsonAJMarshallJFNevilleAMJatPSDissecting the transcriptional networks underlying breast cancer: NR4A1 reduces the migration of normal and breast cancer cell linesBreast Cancer Res12R512010 LeeSOLiXKhanSSafeSTargeting NR4A1 (TR3) in cancer cells and tumorsExpert Opin Ther Targets151952062011 RobbinsGTNieDPPARgamma, bioactive lipids, and cancer progressionFront Biosci (Landmark Ed)17181618342012 ClementsAEngleDSheltonDNeffTParkSBenderDAhmedADe GeestKButtonAEngelhardtJFGoodheartMLymphoid Enhancing Factor 1 (Lef-1) overexpression in epithelial ovarian, fallopian tube and peritoneal cancer and associations with clinical factorsProc Obstet Gynecol2122011 TomlinsSAMehraRRhodesDRSmithLRRoulstonDHelgesonBECaoXWeiJTRubinMAShahRBChinnaiyanAMTMPRSS2: ETV4 gene fusions define a third molecular subtype of prostate cancerCancer Res66339634002006 DarnellJEJrTranscription factors as targets for cancer therapyNat Rev Cancer27407492002

Significant metabolic pathways of differentially co-expressed genes: The abscissa represent the number of genes; the ordinate represents the name of the metabolic pathways. KEGG, Kyoto Encyclopedia of Genes and Genomes.

Transcriptional regulation network: Triangles in the figure represent TFs; circles represent the target genes; yellow dots represent DCGs; pink dots represent non-DCGs; red lines represent 10 correlations; black lines represent correlations of TF-target regulation. DCG, differentially co-expressed gene; TF, transcription factor.

Pathway enrichment of differentially co-expressed genes in patients with cancer compared with normal controls with P<0.1.

Term P-value Differentially co-expressed genes
Hsa00010: Glycolysis/gluconeogenesis 5.49×10−6 ALDOA, GPI, TPI1, PKM2, ALDH2, FBP1, ADH1B, ADH1A, PGK1, GAPDH, PCK1
Hsa04110: Cell cycle 8.84×10−5 CCNB1, CDC6, CCNB2, CCND2, BUB1, PCNA, BUB1BPRKDC, MCM2, GADD45B, MCM4, MCM6
Hsa04610: Complement and coagulation cascades 0.018029 VWF, C7, THBD, CD59, SERPING1, CFD
Hsa03030: DNA replication 0.044864 PCNA, MCM2, MCM4, MCM6
Hsa00230: Purine metabolism 0.052964 POLR2H, ITPA, PKM2, PDE4B, RRM1, NPR1, ENTPD6HPRT1
Hsa00620: Pyruvate metabolism 0.058282 ME1, PKM2, ALDH2, PCK1
Hsa00970: Aminoacyl-tRNA biosynthesis 0.061901 TARS, AARS, EPRS, IARS2
Hsa05416: Viral myocarditis 0.072253 LAMA2, ICAM1, CAV1, DMD, MYH11
Hsa00350: Tyrosine metabolism 0.073368 GOT2, MAOB, ADH1B, ADH1A, MIF
Hsa00360: Phenylalanine metabolism 0.084752 GOT2, MAOB, MIF

Transcription regulation correlations in differentially co-expressed links.

Transcription factor Target gene Cor. 1 Cor. 2 Cor. diff.
FLI1 CDKN1A −0.593101612 0.701060623 1.294162235
ETS2 PLAU 7.12×10−1 −0.513413211 1.225511357
HIF1A NR4A1 0.79000137 −0.432670777 1.222672148
PPARG ATP2A2 0.448097377 −0.721308657 1.169406033
LEF1 VIM 0.484138641 −0.605684298 1.089822939
ETS2 JUNB 0.735807964 −0.337406532 1.073214496
ETV4 VIM 0.485632699 −0.491208815 0.976841514
JUND PLAU 6.27×10−1 0.038142972 0.588792142
ETS2 MMP9 0.619596343 0.05290016 0.566696183
VDR CDKN1A 0.497762462 0.034328193 0.463434268

Cor. 1 and 2 represent the maximum absolute correlation of the transcription factor and the target gene. Cor. diff. represents the sum of the absolute value of correlations 1 and 2. FLI1, Fli-1 proto-oncogene, ETS transcription factor; ETS2, v-ets erythroblastosis virus E26 oncogene homolog 2; HIF1A, hypoxia-inducing factor 1α; PPARG, peroxisome proliferator-activated receptor γ; LEF1, lymphoid enhancer-binding factor 1; ETV4, ets variant 4; JUND, jun D proto-oncogene; VDR, vitamin D receptor; CDKN1A, cyclin-dependent kinase inhibitor 1A; PLAU, plasminogen activator, urokinase; NR4A1, nuclear receptor subfamily 4, group A, member 1; ATP2A2, ATPase, Ca++ transporting, cardiac muscle, slow twitch 2; VIM, vimentin; JUNB, jun B proto-oncogene; MMP9, matrix metallopeptidase 9; Cor. 1, correlation 1; Cor. 2, correlation 2; Cor. diff., correlation difference.