OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2018.8842 OL-0-0-8842 Articles Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma LuoTing 1 * ChenXiaoyi 1 * ZengShufei 2 GuanBaozhang 1 HuBo 1 MengYu 1 LiuFanna 1 WongTaksui 1 LuYongpin 2 YunChen 2 HocherBerthold 3 YinLianghong 1 Department of Nephrology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, Guangdong 510632, P.R. China Department of Nephrology, Charité-Universitätsmedizin Berlin, Campus Mitte, D-10117 Berlin, Germany Institute of Nutritional Sciences, University of Potsdam, D-14558 Potsdam, Germany Correspondence to: Professor Lianghong Yin, Department of Nephrology, The First Affiliated Hospital of Jinan University, Jinan University, 613 West Huangpu Avenue, Guangzhou, Guangdong 510632, P.R. China, E-mail: jnufu09@126.com Dr Berthold Hocher, Institute of Nutritional Sciences, University of Potsdam, 114-116 Arthur-Scheunert-Allee, D-14558 Potsdam, Germany, E-mail: hocher@uni-potsdam.de

Contributed equally

 08 2018 30 05 2018 16 2 1747 1757 14122017 22052018 Copyright: © Luo et al. 2018 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

The present study aimed to identify new key genes as potential biomarkers for the diagnosis, prognosis or targeted therapy of clear cell renal cell carcinoma (ccRCC). Three expression profiles (GSE36895, GSE46699 and GSE71963) were collected from Gene Expression Omnibus. GEO2R was used to identify differentially expressed genes (DEGs) in ccRCC tissues and normal samples. The Database for Annotation, Visualization and Integrated Discovery was utilized for functional and pathway enrichment analysis. STRING v10.5 and Molecular Complex Detection were used for protein-protein interaction (PPI) network construction and module analysis, respectively. Regulation network analyses were performed with the WebGestal tool. UALCAN web-portal was used for expression validation and survival analysis of hub genes in ccRCC patients from The Cancer Genome Atlas (TCGA). A total of 65 up- and 164 downregulated genes were identified as DEGs. DEGs were enriched with functional terms and pathways compactly related to ccRCC pathogenesis. Seventeen hub genes and one significant module were filtered out and selected from the PPI network. The differential expression of hub genes was verified in TCGA patients. Kaplan-Meier plot showed that high mRNA expression of enolase 2 (ENO2) was associated with short overall survival in ccRCC patients (P=0.023). High mRNA expression of cyclin D1 (CCND1) (P<0.001), fms related tyrosine kinase 1 (FLT1) (P=0.004), plasminogen (PLG) (P<0.001) and von Willebrand factor (VWF) (P=0.008) appeared to serve as favorable factors in survival. These findings indicate that the DEGs may be key genes in ccRCC pathogenesis and five genes, including ENO2, CCND1, PLT1, PLG and VWF, may serve as potential prognostic biomarkers in ccRCC.

 clear cell renal cell carcinoma bioinformatics differentially expressed genes biomarkers Kaplan-Meier plot Introduction

Renal cell carcinoma (RCC) accounts for 2–3% of all human malignancies (1). It is estimated that more than 338,000 people are diagnosed with RCC each year, with a 22% increase projected by 2020; there are more than 140,000 RCC-related deaths per year (2). Clear cell renal cell carcinoma (ccRCC) is the most common (~75%), lethal subtype of RCC (3). Over the past decade, with improved surgical procedures and the application of specific targeted drugs, the survival of RCC patient has markedly improved (4). However, early accurate diagnosis of ccRCC is still a great challenge and chemotherapeutic or radiotherapeutic resistance remains (4).

A comprehensive understanding of ccRCC initiation, progression and metastasis contributes to early diagnosis and precise treatment. Previous studies have demonstrated that mutations of VHL are significant drivers of ccRCC by regulating various biological processes, and VHL alterations are considered as prognostic markers in ccRCC (5). Moreover, targeted therapies associated with the pVHL/HIF pathway have been tested in phase 3 trials (4). VHL alterations alone are insufficient to cause the cancer, as ccRCC is a systemic biological disease. Sequencing studies have identified some other specific molecular genetic alterations of ccRCC, such as mutations of TCEB1 (6), PBRM1 (7) and abnormal expression of miR-92 (8), miR-210 (9). Further insights into the molecular biology of ccRCC could help us find some novel molecular biomarkers and potential targets for early diagnosis and precise treatment.

Gene expression profiling arrays make it possible to identify numerous differentially expressed genes in tumor samples compared to non-tumor samples at the same time. In this study, we performed an integrated bioinformatics analysis of three gene expression profiles and identified several differentially expressed genes (DEGs) in ccRCC tissues compared with normal controls. We executed functional and pathway enrichment analysis, protein-protein interaction (PPI) network analysis of DEGs and employed the Kaplan-Meier method to analyze survival associated with hub genes. We intended to provide further insights into the complex molecular biology of ccRCC pathogenesis and to identify new key genes that may be candidates for diagnostic biomarkers, prognostic indicators or potential targets of precise therapy.

 Materials and methods <sec> <title>Data collection

Three gene expression profiles (GSE36895, GSE46699 and GSE71963) were acquired from Gene Expression Omnibus (GEO) database, a free public genomics data repository for array- and sequence-based data.

The array data of GSE36895 included 29 ccRCC tumor samples and 23 matched adjacent normal kidney cortices (10). GSE46699 was comprised of 126 samples including 65 ccRCC tumors and 61 patient-matched adjacent-normal tissues (11). GSE71963 contained 32 ccRCC tumor samples and 16 normal kidney samples (12).

 Data processing

GEO2R, a tool for online analysis of GEO series based on the R programming language (13), was used to screen DEGs between the normal kidneys and ccRCC samples. Adjusted P-value (adj. P) and |log Fold Change| (|log FC|) were used to select significant DEGs. adj. P<0.05 and |log FC| >2 were chosen as the cutoff criteria.

 Functional and pathway enrichment analysis

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs was carried out using The Database for Annotation, Visualization and Integrated Discovery (DAVID) online (14,15). P<0.05 was selected as the cutoff value.

 PPI network construction and significant module analysis

STRING v10.5 was utilized for functional interaction analysis to construct a PPI network (16). Confidence scores >0.7 were considered significant. Genes with degrees >10 were selected as hub genes. The PPI network was visualized by Cytoscape software, and module of PPI network was screened by the Molecular Complex Detection (MCODE) in Cytoscape. The parameters were set as follows: Degree cutoff: 2, node score cutoff: 0.2, k-core: 2, and max. depth: 100 (17). The functional and pathway enrichment analysis of the significant module was carried out by DAVID.

 Regulation network analyses

The miRNAs and transcription factors (TFs) that potentially regulated the DEGs were predicted using Overrepresentation Enrichment Analysis (ORA) in WebGestal software (18). Then miRNA-target network and TF-target network were also visualized using Cytoscape software.

 TCGA verification and survival analysis of hub genes

UALCAN, a tool for in-depth analyses of The Cancer Genome Atlas (TCGA) data, was utilized to verify the differences in expression levels of hub genes (19). The correlation of hub genes with overall survival (OS) of ccRCC patients was examined by recruiting UALCAN as well. Patient data were categorized into two groups based on transcripts per million (TPM) value. The data with TPM greater than upper quartile were assigned to a high expression group and the others with TPM below upper quartile belonged to low/medium expression group. Survival analysis was performed by Kaplan-Meier method, and the log-rank test was carried out. P<0.05 was selected as the cutoff value.

 Results <sec> <title>Identification of DEGs in ccRCC

A total of 591, 325 and 1118 genes were extracted from the GSE36895, GSE46699 and GSE71963 datasets, respectively. There were 229 genes consistently differentially expressed in all three datasets (Fig. 1), including 65 upregulated DEGs and 164 downregulated DEGs in ccRCC tissues compared with normal kidney tissues (Table I).

 GO analysis of DEGs in ccRCC

After performing GO analysis of DEGs with DAVID online, the DEGs were classified into three groups: biological process group, molecular function group and cellular component group. We found that the upregulated genes were mainly enriched in biological processes related to hypoxia, blood vessel morphogenesis and angiogenesis. The downregulated genes were commonly involved in functional terms associated with cellular components, metabolism and homeostasis.

 Pathway enrichment analysis of DEGs in ccRCC

KEGG pathway enrichment analysis of DEGs was also conducted with DAVID online. KEGG results of the up- and downregulated genes were displayed in Tables II and III, respectively. The upregulated genes were mostly enriched in HIF-1 signaling pathway, PPAR signaling pathway, focal adhesion, coagulation cascades and AMPK signaling pathway. The downregulated genes were mainly enriched in metabolic pathways, collecting duct acid secretion, aldosterone-regulated sodium reabsorption, carbon metabolism and biosynthesis of antibiotics.

 PPI network construction and significant module analysis

A total of 169 genes of the 229 DEGs in all three datasets were filtered into the PPI network complex, containing 169 nodes and 432 edges (Fig. 2A). There were 44 upregulated genes and 125 downregulated genes among the 169 DEGs. Seventeen nodes with a degree >10 were identified as hub genes, such as ALB, VEGFA, EGF, AQP2, ENO2, PLG, FLT1,etc. (bold in Table I). The characteristic properties of the hub nodes based on analysis of the PPI network were tabulated in Table IV. These properties included degree, betweenness, closeness, stress and average shortest path length. After performing module analysis by MCODE, the most significant module was screened out from the PPI network of DEGs, composed of 15 nodes and 54 edges (Fig. 2B). Functional and pathway enrichment analysis of nodes in the module was displayed in Table V. Most of these nodes were enriched in the functional terms related to substance transport and the pathways associated with cancer.

 TF-DEG regulatory network

The DEG-associated transcriptional regulatory network was shown in Fig. 3A. A total of 90 nodes with 135 edges were contained in this regulation network, including 61 downregulated genes, 19 upregulated genes and 10 TFs.

 miRNA-DEG regulatory network

In total, 6 miRNAs were filtered out (miR-144, miR-96, miR-503, miR-150, miR-383 and miR-338) (Fig. 3B). A total of 31 nodes and 28 edges were included in this regulatory network.

 TCGA validation and the Kaplan-Meier plot

TCGA data of ccRCC patients were used via the UALCAN data portal. The hub genes identified from the PPI network were differentially expressed between ccRCC tissues and normal tissues (Fig. 4). The expression trends were identical within the three GEO datasets. Kaplan-Meier curve for overall survival of TCGA patients with ccRCC was obtained according to the low and high expression of each gene. The results showed that patients in the high mRNA expression group for ENO2 had significantly worse OS than those in the low/medium expression group (P=0.023) (Fig. 5A). While high mRNA expression level of CCND1 was associated with longer OS for ccRCC patients (P=0.000), as well as FLT1 (P=0.004), PLG (P=0.000), and VWF (P=0.008) (Fig. 5B-E).

 Discussion

The prognosis remains uncertain in ccRCC patients. Identifying novel potential biomarkers for early diagnosis, prognostic evaluation or targeted therapy may improve patient outcomes. Here we performed an in-depth analysis of three expression profiles (with 126 ccRCC tissues and 100 normal controls) using bioinformatics method and identified 65 up- and 164 downregulated genes. Then we constructed a PPI network of DEGs and extracted 17 hub genes and one significant module from the PPI network. GO and KEGG pathway analysis revealed that the DEGs were commonly involved in functional terms and pathways related to the progression and prognosis of ccRCC. For example, hypoxia and HIF-1 pathway alterations are critical for the initiation and metastasis of ccRCC (20). Hypoxia could induce a series of tumor-related aberrations within cellular metabolism, apoptosis, migration and angiogenesis through dysregulation of HIF target genes (20). Drugs targeting the HIF-1 pathway have proven to be effective in treating ccRCC patients (21). In addition, metabolic pathways play a critical role in ccRCC progression according to previous studies, as well as glycolysis/gluconeogenesis, AMPK signaling pathway, and PI3K-Akt signaling pathway (22).

Interestingly, the Staphylococcus aureus infection pathway was found to be significant in our study. Growing evidence has indicated that bacterial infection is highly associated with certain human malignancies (23). It has been reported that lipoteichoic acids from S. aureus induce proliferation of two human non-small-cell lung cancer cell lines, A549 and H226 (24). However, the role of S. aureus infection in ccRCC still remains to be detected.

Using a Kaplan-Meier plot for survival analysis, the mRNA expression levels of ENO2, CCND1, PLT1, PLG and VWF were found to be significantly correlated with OS in ccRCC.

Enolase 2 (ENO2) encodes an enolase isoenzyme which is considered as a sensitive and specific biomarker for small-cell lung cancer (25,26). According to our KEGG results, ENO2 was involved in several pathways compactly related to ccRCC pathogenesis such as glycolysis/gluconeogenesis, HIF-1 signaling pathway and metabolic pathways. In addition, ENO2 is found to be induced by HIF-2a although suppression of its mRNA expression alone does not significantly inhibit the growth of the ccRCC cell line 786-O (27). Combining our survival analysis, we infer that ENO2 may be an indicator in the diagnosis and prognosis rather than a potential target for therapy.

Cyclin D1 (CCND1) encodes an essential protein in the cell cycle which shows dual functions in cell growth. It is well-established that CCND1 regulates the cell cycle transition from G1 to S phase by binding to CK4 and CDK6 (28,29). Previous studies suggest that the overexpression of CCND1 promotes cell growth in many malignancies (30–34). Other studies have shown an apoptotic induction effect of CCND1. Consistent expression of an exogenous CCND1 significantly inhibits cell proliferation (35) and induces apoptosis in mammary epithelial cell lines (36). Upregulated CCND1 induces apoptosis of fibroblasts (37) and has a positive correlation with a high apoptotic index in squamous cell carcinomas (38). Our analysis and previous studies show that CCND1 is upregulated in ccRCC patients (39). Furthermore, it has been reported that reducing CCND1 expression leads to a suppression of tumor growth in ccRCC (27). CCND1 is considered as an oncogene in ccRCC. Interestingly, our results showed that high expression of CCND1 was associated with favorable prognosis in ccRCC. Similarly, CCND1 is elevated and has a favorable effect on disease-free survival in papillary superficial bladder cancer (40). Two independent studies have shown that colon cancer patients with higher CCND1 expression have better outcomes (41,42). The molecular mechanism of CCND1 in cancer awaits further investigation.

The importance of VEGF in RCC progression is well established and several VEGFR inhibitors such as sunitinib and sorafenib have proven to be significantly beneficial for progression-free survival (PFS) and OS in phase 3 trials (43,44). Recent research has demonstrated that FLT1 (also known as VEGFR-1) protein expression in the tumor epithelium of localized ccRCC patients has a negative effect on prognosis (45). Other studies have found that high mRNA expression level of FLT1 is significantly related to favorable PFS in metastatic ccRCC patients treated with sunitinib (46). In this study, we found that higher mRNA expression levels of FLT1 in ccRCC tissue were associated with longer OS. The implication of FLT1 in ccRCC remains unclear. It should be noted that FLT1 can be generated as a transmembrane form and a soluble form. Soluble FLT1 (sFlt1) lacks transmembrane and intracellular domains in contrast to the primary form, a full-length transmembrane receptor (47). Additionally, sFlt1 is thought to be a natural antagonist of VEGF. Recent studies have found that sFlt1 has an antitumor effect on several cancer cells (48–50). Enhanced sFlt1 expression in the serum of breast cancer patients inhibits circulating tumor cells entering the peripheral blood, which may contribute to favorable outcomes (51). Herein we hypothesize that not transmembrane FLT1 but sFLT1 may have an antitumor effect on ccRCC and the value of sFlt1 in patient serum or urine may be worthy of further evaluation.

More and more evidence has demonstrated that plasminogen-plasmin system components are involved in tumor growth, invasion and metastasis by regulating angiogenesis and cell migration (52). The high levels of uPA, uPAR or PAI-1 expression have proven to be prognostic biomarkers of poor outcome in many cancers, such as ovary cancer, breast cancer and renal cancer (53). The mRNA expression level of PLG in ccRCC patients was found to be downregulated in our analysis and other studies (54). Our results revealed that the ccRCC patients with a higher PLG mRNA expression had longer OS. Similar results have been reported in advanced ovarian cancer recently, and PLG was identified to be a favorable prognostic biomarker in this disease (55).

Another favorable biomarker in our analysis is Von Willebrand Factor (VWF), which shows dual functions in angiogenesis and cancer metastasis according to previous data (56). VWF exhibits a pro-apoptotic effect on 769P, a ccRCC-derived cell line (57). While others have found that serum VWF levels are notably higher in progressive RCC patients compared with stable RCC patients (58). More studies should be done to clarify the link between VWF and ccRCC.

The main limitation of our study is that exploration is done at a bioinformatics level, in silico. Future studies, especially biological experiments in vitro and in vivo are needed to validate the function of these DEGs in ccRCC.

In conclusion, through an integrated bioinformatics analysis of three gene profiles, we identified 229 DEGs, which may contain key genes in ccRCC pathogenesis. Five of the 17 hub genes including ENO2, CCND1, PLT1, PLG and VWF were filtered out through our analysis and may be potential prognostic biomarkers in ccRCC.

 Acknowledgements

Not applicable.

 Funding

This study was supported by the Guangdong Obers Blood Purification Academician Work Station (grant no. 2013B090400004); the Guangzhou Entrepreneurial Leader Talent (grant no. LCY201215); and the Guangdong Provincial Center for Clinical Engineering of Blood Purification (grant no. 507204531040).

 Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

 Authors' contributions

BH, LY and TL designed the study; TL, XC, SZ, BG, BH, YL and CY performed data analysis; YM, FL and TW performed literature research and data acquisition and participated in the data analysis; TL and XC wrote the manuscript; BH and LY revised the manuscript; YL and CY edited the manuscript and approved the final version of the manuscript; LY obtained funding. All authors read and approved the final manuscript.

 Ethics approval and consent to participate

Not applicable.

 Consent for publication

Not applicable.

 Competing interests

The authors declare no competing interests.

 References LiPZnaorAHolcatovaIFabianovaEMatesDWozniakMBFerlayJSceloGRegional geographic variations in kidney cancer incidence rates in European countriesEur Urol6711341141201510.1016/j.eururo.2014.11.00125465966 FerlayJSoerjomataramIDikshitREserSMathersCRebeloMParkinDMFormanDBrayFCancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012Int J Cancer136E359E386201510.1002/ijc.2921025220842 SrinivasanRRickettsCJSourbierCLinehanWMNew strategies in renal cell carcinoma: Targeting the genetic and metabolic basis of diseaseClin Cancer Res211017201510.1158/1078-0432.CCR-13-299325564569 CapitanioUMontorsiFRenal cancerLancet387894906201610.1016/S0140-6736(15)00046-X26318520 GossageLEisenTAlterations in VHL as potential biomarkers in renal-cell carcinomaNat Rev Clin Oncol7277288201010.1038/nrclinonc.2010.4220368728 Cancer Genome Atlas Research NetworkComprehensive molecular characterization of clear cell renal cell carcinomaNature4994349201310.1038/nature1222223792563 IbragimovaIMaradeoMEDulaimiECairnsPAberrant promoter hypermethylation of PBRM1, BAP1, SETD2, KDM6A and other chromatin-modifying genes is absent or rare in clear cell RCCEpigenetics8486493201310.4161/epi.2455223644518 ValeraVAWalterBALinehanWMMerinoMJRegulatory effects of microRNA-92 (miR-92) on VHL gene expression and the hypoxic activation of miR-210 in clear cell renal cell carcinomaJ Cancer2515526201110.7150/jca.2.51522043236 McCormickRIBlickCRagoussisJSchoedelJMoleDRYoungACSelbyPJBanksREHarrisALmiR-210 is a target of hypoxia-inducible factors 1 and 2 in renal cancer, regulates ISCU and correlates with good prognosisBr J Cancer10811331142201310.1038/bjc.2013.5623449350 Peña-LlopisSVega-Rubín-de-CelisSLiaoALengNPavía-JiménezAWangSYamasakiTZhrebkerLSivanandSSpencePBAP1 loss defines a new class of renal cell carcinomaNat Genet44751759201210.1038/ng0912-1072b22683710 Eckel-PassowJESerieDJBotBMJosephRWChevilleJCParkerASANKS1B is a smoking-related molecular alteration in clear cell renal cell carcinomaBMC Urol1414201410.1186/1471-2490-14-1424479813 TakahashiMTsukamotoYKaiTTokunagaANakadaCHijiyaNUchidaTDaaTNomuraTSatoFDownregulation of WDR20 due to loss of 14q is involved in the malignant transformation of clear cell renal cell carcinomaCancer Sci107417423201610.1111/cas.1289226790128 BarrettTWilhiteSELedouxPEvangelistaCKimIFTomashevskyMMarshallKAPhillippyKHShermanPMHolkoMNCBI GEO: Archive for functional genomics data sets-updateNucleic Acids Res41(Database Issue):D991D995201310.1093/nar/gks119323193258 Huang daWShermanBTLempickiRASystematic and integrative analysis of large gene lists using DAVID bioinformatics resourcesNat Protoc44457200910.1038/nprot.2008.21119131956 Huang daWShermanBTLempickiRABioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene listsNucleic Acids Res37113200910.1093/nar/gkn92319033363 SzklarczykDMorrisJHCookHKuhnMWyderSSimonovicMSantosADonchevaNTRothABorkPThe STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessibleNucleic Acids Res45D362D368201710.1093/nar/gkw93727924014 BaderGDHogueCWAn automated method for finding molecular complexes in large protein interaction networksBMC Bioinformatics42200310.1186/1471-2105-4-212525261 WangJDuncanDShiZZhangBWEB-based GEne SeT AnaLysis Toolkit (WebGestalt): Update 2013Nucleic Acids Res41W77W83201310.1093/nar/gkt43923703215 ChandrashekarDSBashelBBalasubramanyaSAHCreightonCJPonce-RodriguezIChakravarthiBVSKVaramballySUALCAN: A portal for facilitating tumor subgroup gene expression and survival analysesNeoplasia19649658201710.1016/j.neo.2017.05.00228732212 SchödelJGramppSMaherERMochHRatcliffePJRussoPMoleDRHypoxia, hypoxia-inducible transcription factors, and renal cancerEur Urol69646657201610.1016/j.eururo.2015.08.00726298207 EscudierBSzczylikCPortaCGoreMTreatment selection in metastatic renal cell carcinoma: Expert consensusNat Rev Clin Oncol9327337201210.1038/nrclinonc.2012.5922473096 CiccareseCBrunelliMMontironiRFiorentinoMIacovelliRHengDTortoraGMassariFThe prospect of precision therapy for renal cell carcinomaCancer Treat Rev493744201610.1016/j.ctrv.2016.07.00327453294 ZhuCWangYCaiCCaiQBacterial infection and associated cancersAdv Exp Med Biol1018181191201710.1007/978-981-10-5765-6_1129052138 HattarKReinertCPSibeliusUGökyildirimMYSubtilFSBWilhelmJEulBDahlemGGrimmingerFSeegerWGrandelULipoteichoic acids from Staphylococcus aureus stimulate proliferation of human non-small-cell lung cancer cells in vitroCancer Immunol Immunother66799809201710.1007/s00262-017-1980-428314957 FizaziKCojeanIPignonJPRixeOGatineauMHadefSArriagadaRBaldeyrouPComoyELe ChevalierTNormal serum neuron specific enolase (NSE) value after the first cycle of chemotherapy: An early predictor of complete response and survival in patients with small cell lung carcinomaCancer8210491055199810.1002/(SICI)1097-0142(19980315)82:6<1049::AID-CNCR6>3.0.CO;2-99506348 OremekGMSauer-EppelHBruzdziakTHValue of tumour and inflammatory markers in lung cancerAnticancer Res2719111915200717649794 ZhangTNiuXLiaoLChoEAYangHThe contributions of HIF-target genes to tumor growth in RCCPLoS One8e80544201310.1371/journal.pone.008054424260413 MacLachlanTKSangNGiordanoACyclins, cyclin-dependent kinases and cdk inhibitors: Implications in cell cycle control and cancerCrit Rev Eukaryot Gene Expr5127156199510.1615/CritRevEukarGeneExpr.v5.i2.208845581 AlaoJPThe regulation of cyclin D1 degradation: Roles in cancer development and the potential for therapeutic inventionMol Cancer624200710.1186/1476-4598-6-2417407548 ArberNHibshooshHMossSFSutterTZhangYBeggMWangSWeinsteinIBHoltPRIncreased expression of cyclin D1 is an early event in multistage colorectal carcinogenesisGastroenterology110669674199610.1053/gast.1996.v110.pm86088748608874 GautschiORatschillerDGuggerMBetticherDCHeighwayJCyclin D1 in non-small cell lung cancer: a key driver of malignant transformationLung Cancer55114200710.1016/j.lungcan.2006.09.02417070615 BoschFJaresPCampoELopez-GuillermoAPirisMAVillamorNTassiesDJaffeESMontserratERozmanCPRAD-1/cyclin D1 gene overexpression in chronic lymphoproliferative disorders: A highly specific marker of mantle cell lymphomaBlood842726273219947919385 FaustJBMeekerTCAmplification and expression of the bcl-1 gene in human solid tumor cell linesCancer Res522460246319921568216 BuckleyMFSweeneyKJHamiltonJASiniRLManningDLNicholsonRIdeFazioAWattsCKMusgroveEASutherlandRLExpression and amplification of cyclin genes in human breast cancerOncogene82127213319938336939 HanEKSgambatoAJiangWZhangYJSantellaRMDokiYCacaceAMSchierenIWeinsteinIBStable overexpression of cyclin D1 in a human mammary epithelial cell line prolongs the S-phase and inhibits growthOncogene1095396119957898937 HanEKBegemannMSgambatoASohJWDokiYXingWQLiuWWeinsteinIBIncreased expression of cyclin D1 in a murine mammary epithelial cell line induces p27kip1, inhibits growth, and enhances apoptosisCell Growth Differ769971019968780883 Sofer-LeviYResnitzkyDApoptosis induced by ectopic expression of cyclin D1 but not cyclin EOncogene132431243719968957085 KotelnikovVMCoonJSIVMundleSKelanicSLaFolletteSTaylorSIVHutchinsonJPanjeWCaldarelliDDPreislerHDCyclin D1 expression in squamous cell carcinomas of the head and neck and in oral mucosa in relation to proliferation and apoptosisClin Cancer Res39510119979815543 TangSWChangWHSuYCChenYCLaiYHWuPTHsuCILinWCLaiMKLinJYMYC pathway is activated in clear cell renal cell carcinoma and essential for proliferation of clear cell renal cell carcinoma cellsCancer Lett2733543200910.1016/j.canlet.2008.07.03818809243 SgambatoAMigaldiMFaragliaBde AloysioGFerrariPArditoRde GaetaniCCapelliGCittadiniATrentiniGPCyclin D1 expression in papillary superficial bladder cancer: Its association with other cell cycle-associated proteins, cell proliferation and clinical outcomeInt J Cancer97671678200210.1002/ijc.1005511807796 HollandTAElderJMcCloudJMHallCDeakinMFryerAAElderJBHobanPRSubcellular localisation of cyclin D1 protein in colorectal tumours is associated with p21(WAF1/CIP1) expression and correlates with patient survivalInt J Cancer95302306200110.1002/1097-0215(20010920)95:5<302::AID-IJC1052>3.0.CO;2-#11494229 OginoSNoshoKIraharaNKureSShimaKBabaYToyodaSChenLGiovannucciELMeyerhardtJAFuchsCSA cohort study of cyclin D1 expression and prognosis in 602 colon cancer casesClin Cancer Res1544314438200910.1158/1078-0432.CCR-08-333019549773 MotzerRJHutsonTETomczakPMichaelsonMDBukowskiRMRixeOOudardSNegrierSSzczylikCKimSTSunitinib versus interferon alfa in metastatic renal-cell carcinomaN Engl J Med356115124200710.1056/NEJMoa06504417215529 EscudierBEisenTStadlerWMSzczylikCOudardSStaehlerMNegrierSChevreauCDesaiAARollandFSorafenib for treatment of renal cell carcinoma: Final efficacy and safety results of the phase III treatment approaches in renal cancer global evaluation trialJ Clin Oncol2733123318200910.1200/JCO.2008.19.551119451442 KlatteTSeligsonDBLaRochelleJShuchBSaidJWRiggsSBZomorodianNKabbinavarFFPantuckAJBelldegrunASMolecular signatures of localized clear cell renal cell carcinoma to predict disease-free survival after nephrectomyCancer Epidemiol Biomarkers Prev18894900200910.1158/1055-9965.EPI-08-078619240241 BeuselinckBJean-BaptisteJSchöffskiPCouchyGMeillerCRollandFAlloryYJoniauSVerkarreVElaidiRValidation of VEGFR1 rs9582036 as predictive biomarker in metastatic clear-cell renal cell carcinoma patients treated with sunitinibBJU Int118890901201610.1111/bju.1358527417418 KendallRLThomasKAInhibition of vascular endothelial cell growth factor activity by an endogenously encoded soluble receptorProc Natl Acad Sci USA901070510709199310.1073/pnas.90.22.107058248162 MiyakeTKumasawaKSatoNTakiuchiTNakamuraHKimuraTSoluble VEGF receptor 1 (sFLT1) induces non-apoptotic death in ovarian and colorectal cancer cellsSci Rep624853201610.1038/srep2485327103202 TakanoSIshikawaEMatsudaMSakamotoNAkutsuHYamamotoTMatsumuraAThe anti-angiogenic role of soluble-form VEGF receptor in malignant gliomasInt J Oncol50515524201710.3892/ijo.2016.381028000842 NiuJWangYWangJBinLHuXDelivery of sFIT-1 engineered MSCs in combination with a continuous low-dose doxorubicin treatment prevents growth of liver cancerAging (Albany NY)835203534201610.18632/aging.10114628039440 VilsmaierTRackBJanniWJeschkeUWeissenbacherTSUCCESS Study GroupAngiogenic cytokines and their influence on circulating tumour cells in sera of patients with the primary diagnosis of breast cancer before treatmentBMC Cancer16547201610.1186/s12885-016-2612-727464822 KwaanHCMcMahonBThe role of plasminogen-plasmin system in cancerCancer Treat Res1484366200910.1007/978-0-387-79962-9_419377918 McMahonBJKwaanHCComponents of the plasminogen-plasmin system as biologic markers for cancerAdv Exp Med Biol867145156201510.1007/978-94-017-7215-0_1026530365 SchrödterSBraunMSyringIKlümperNDengMSchmidtDPernerSMüllerSCEllingerJIdentification of the dopamine transporter SLC6A3 as a biomarker for patients with renal cell carcinomaMol Cancer1510201610.1186/s12943-016-0495-526831905 ZhaoSDornJNapieralskiRWalchADierschSKotzschMAhmedNHooperJDKiechleMSchmittMMagdolenVPlasmin(ogen) serves as a favorable biomarker for prediction of survival in advanced high-grade serous ovarian cancerBiol Chem398765773201710.1515/hsz-2016-028227935848 MojiriAStoletovKCarrilloMAWillettsLJainSGodboutRJuraszPSergiCMEisenstatDDLewisJDJahroudiNFunctional assessment of von Willebrand factor expression by cancer cells of non-endothelial originOncotarget81301513029201710.18632/oncotarget.1427328035064 MochizukiSSoejimaKShimodaMAbeHSasakiAOkanoHJOkanoHOkadaYEffect of ADAM28 on carcinoma cell metastasis by cleavage of von Willebrand factorJ Natl Cancer Inst104906922201210.1093/jnci/djs23222636800 BraybrookeJPO'ByrneKJPropperDJBlannASaundersMDobbsNHanCWoodhullJMitchellKCrewJA phase II study of razoxane, an antiangiogenic topoisomerase II inhibitor, in renal cell cancer with assessment of potential surrogate markers of angiogenesisClin Cancer Res646974704200011156222

Identification of DEGs in three mRNA expression profiles (GSE36895, GSE46699 and GSE71963). DEGs, differentially expressed genes.

DEGs protein-protein interaction (PPI) network complex and one significant module obtained from PPI network. (A) DEGs PPI network containing 169 nodes and 432 edges. (B) One significant module composed of 15 nodes and 54 edges. Red nodes and green nodes stand for upregulated genes and downregulated genes, respectively. Lines represent the interaction between nodes. DEG, differentially expressed genes.

Regulatory network complex. (A) TF-DEG regulatory network containing 61 downregulated genes, 19 upregulated genes and 10 TFs. (B) miRNA-DEG regulatory network containing 31 nodes and 28 edges. Red nodes, green nodes, blue nodes and yellow nodes stand for upregulated genes, downregulated genes, TFs and miRNAs respectively. TF, transcription factor; miR, miRNA; DEG, differentially expressed genes.

Boxplots showing the expression of the 17 hub genes in healthy controls (n=72) and ccRCC tissues (n=533) of TCGA samples. The t-test was performed on the relevant results (*P<0.05 and ***P<0.001). ccRCC, clear cell renal cell carcinoma; TCGA, The Cancer Genome Atlas.

Kaplan-Meier survival curve for (A) ENO2, (B) CCND1, (C) FLT1, (D) PLG and (E) VWF expression levels in TCGA patients with ccRCC. The log-rank test was carried out on the relevant results. ccRCC, clear cell renal cell carcinoma; TCGA, The Cancer Genome Atlas.

DEGs in ccRCC tissues compared with normal controls.

DEGs Gene name
Upregulated TNFAIP6, PFKP, NDUFA4L2, CXCR4, NPTX2, C1QC, FLT1, LOX, PDK1, COL23A1, CDCA2, GAS2L3, KCNK3, NETO2, FABP7, RNASET2, ANGPTL4, GJC1, SCD, HILPDA, LOXL2, DGCR5, CA9, EGLN3, ENO2, TMEM45A, PPP1R3C, CAV2, VWF, CCND1, ST8SIA4, C3, DIRAS2, IGFBP3, FABP5, LAMA4, SAP30, CD36, CTHRC1, GAL3ST1, HK2, VEGFA, SCARB1, AHNAK2, CAV1, TGFBI, INHBB, ZNF395, PLOD2, TMCC1, PLXDC1, BHLHE41, CYP2J2, SPAG4, LPCAT1, CP, C1QB, FAM26F, APOC1, ENPP3, SLC6A3, ACKR3, ANGPT2, NOL3, ESM1
Downregulated PTGER3, ERBB4, RALYL, L1CAM, XPNPEP2, SLC4A1, MPPED2, EHF, HMGCS2, HPD, GGACT, SLC7A13, HRG, UGT3A1, GATA3, TMEM174, SLC13A1, PROM2, CALB1, SUSD2, KCNJ1, SLC12A3, CRYAA, HSD11B2, DEFB1, GPC5, CYP27B1, UCHL1, FABP1, TMEM30B, CYP4F2, NELL1, MTURN, FGF9, NPHS2, PSAT1, SLC4A9, TFCP2L1, ALDH4A1, SLC12A1, ERP27, ALDH8A1, SCIN, TSPAN8, KL, AZGP1, SLC22A6, EFHD1, LOC100505985, CRHBP, AQP2, ASS1, TACSTD2, PVALB, FOXI1, ABAT, TMEM52B, IRX2, MIOX, PIGR, ATP6V1G3, SEMA6D, S100A2, SCD5, MAL, FGF1, SORD, DMRT2, TFAP2B, GLDC, FBP1, RASD1, PLPPR1, CYP4F3, GSTM3, ESRRG, SLC47A2, KNG1, SLC34A1, MUC15, PTPRO, DPEP1, MECOM, ACSF2, CYP17A1, MT1G, PLG, UPP2, MFSD4A, SLC22A8, HAO2, ALDH6A1, MT1F, TMEM213, CHL1, EGF, DCXR, UMOD, ATP6V0D2, ANK2, HOGA1, DIO1, ELF5, SCNN1A, HSPA2, SOSTDC1, TYRP1, ENPP6, PCP4, GPC3, HS6ST2, CLDN8, PCK1, SLC5A2, NOX4, BMPR1B, G6PC, WNK4, ADH6, HEPA, CAM2, SOST, SH3GL2, SCNN1B, ALB, ALDOB, DCN, SCNN1G, KCNJ10, SLC13A3, SUCNR1, AFM, RAB25, ACPP, HPGD, FXYD4, DNER, RHCG, CYP4A11, CTXN3, KCNJ15, GRB14, PTH1R, GGT6, SLC26A7, C7, TMEM178A, OGDHL, ATP6V1B1, DUSP9, SERPINA5, SFRP1, CLCNKB, SLC7A8, SLC7A8, PIPOX, MAL2, PDE1A, TMPRSS2, GPAT3, PRODH2, FAM151A, EPCAM, MRO, ATP6V0A4

A total of 65 upregulated DEGs and 164 downregulated DEGs were identified in ccRCC tissues, compared with normal kidney tissues. The hub genes were shown in boldface. DEGs, differentially expressed genes; ccRCC, clear cell renal cell carcinoma.

KEGG pathway enrichment analysis of 65 upregulated DEGs.

Pathway Name P-value Genes
hsa04066 HIF-1 signaling pathway 1.14×10−5 PDK1, FLT1, VEGFA, EGLN3, ENO2, HK2, ANGPT2
hsa03320 PPAR signaling pathway 4.19×10−4 CD36, SCD, FABP7, FABP5, ANGPTL4
hsa04510 Focal adhesion 7.01×10−4 CAV2, VWF, LAMA4, CAV1, CCND1, FLT1, VEGFA
hsa04610 Complement and coagulation cascades 5.81×10−3 C1QB, VWF, C3, C1QC
hsa04152 AMPK signaling pathway 2.70×10−2 CCND1, CD36, SCD, PFKP
hsa05150 Staphylococcus aureus infection 3.35×10−2 C1QB, C3, C1QC
hsa04151 PI3K-Akt signaling pathway 3.53×10−2 VWF, LAMA4, CCND1, FLT1, VEGFA, ANGPT2
hsa05230 Central carbon metabolism in cancer 4.57×10−2 PDK1, PFKP, HK2
hsa00010 Glycolysis/Gluconeogenesis 4.96×10−2 ENO2, PFKP, HK2

The pathways were ranked by P-value. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

KEGG pathway enrichment analysis of 164 downregulated DEGs.

Pathway Name P-value Genes
hsa01100 Metabolic pathways 2.40×10−5 TYRP1, SORD, ASS1, OGDHL, ALDOB, UPP2, ADH6, ATP6V1B1, GPAT3, PIPOX, GLDC, CYP27B1, ALDH4A1, ATP6V0D2, HPD, ALDH6A1, KL, HOGA1, FBP1, PCK1, CYP4A11, CYP17A1, GGT6, G6PC, HMGCS2, HAO2, ABAT, PRODH2, CYP4F3, CYP4F2, ATP6V1G3, PSAT1, ATP6V0A4, DCXR
hsa04966 Collecting duct acid secretion 2.40×10−5 CLCNKB, SLC4A1, ATP6V1G3, ATP6V1B1, ATP6V0A4, ATP6V0D2
hsa04960 Aldosterone-regulated sodium reabsorption 1.51×10−4 FXYD4, HSD11B2, SCNN1G, SCNN1B, SCNN1A, KCNJ1
hsa01200 Carbon metabolism 3.81×10−3 ALDH6A1, OGDHL, ALDOB, HAO2, FBP1, PSAT1, GLDC
hsa01130 Biosynthesis of antibiotics 7.03×10−3 HMGCS2, ASS1, OGDHL, ALDOB, HAO2, FBP1, PSAT1, PCK1, GLDC
hsa00010 Glycolysis/gluconeogenesis 1.19×10−2 G6PC, ALDOB, FBP1, ADH6, PCK1
hsa04742 Taste transduction 2.17×10−2 PDE1A, SCNN1G, SCNN1B, SCNN1A
hsa05110 Vibrio cholerae infection 3.32×10−2 ATP6V1G3, ATP6V1B1, ATP6V0A4, ATP6V0D2
hsa00630 Glyoxylate and dicarboxylate metabolism 4.96×10−2 HAO2, HOGA1, GLDC

The pathways were ranked by P-value. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

Topology properties of 17 hub genes.

Genes name Degree Betweenness centrality Closeness centrality Clustering coefficient Stress Average shortest path length
ALB 50 0.42 0.50 0.10 30,746 2.00
VEGFA 35 0.14 0.42 0.15 11,030 2.40
EGF 26 0.14 0.45 0.25 11,990 2.23
AQP2 19 0.20 0.41 0.23 15,956 2.44
ENO2 17 0.08 0.39 0.13 6,610 2.60
PLG 16 0.01 0.38 0.45 1,644 2.62
CAV1 15 0.05 0.39 0.29 4,140 2.57
KNG1 15 0.04 0.38 0.45 3,414 2.62
CXCR4 15 0.02 0.38 0.45 3,020 2.62
FLT1 15 0.01 0.39 0.51 1,474 2.58
VWF 14 0.00 0.37 0.52 582 2.67
GLDC 13 0.06 0.34 0.15 5,708 2.96
DCN 12 0.09 0.37 0.26 6,442 2.69
CCND1 12 0.04 0.38 0.47 2,944 2.65
SLC12A1 12 0.03 0.38 0.42 3,942 2.62
ALDH4A1 12 0.03 0.31 0.21 3,776 3.20
FGF1 11 0.02 0.37 0.53 1,592 2.67

The genes were ranked by degree.

Functional and pathway enrichment analyses of nodes in the significant module.

Term Description Count P-value
GO:0006811 Ion transport 12 6.36×10−10
GO:0034220 Ion transmembrane transport 10 1.07 ×10−08
GO:0007588 Excretion 5 1.97 ×10−08
GO:0016324 Apical plasma membrane 7 4.25 ×10−08
GO:0015672 Monovalent inorganic cation transport 8 4.97 ×10−08
GO:0050878 Regulation of body fluid levels 8 6.29 ×10−08
GO:0030001 Metal ion transport 9 7.29 ×10−08
GO:0016324 Apical plasma membrane 7 1.68 ×10−07
GO:0055085 Transmembrane transport 10 1.70 ×10−07
GO:0006812 Cation transport 9 2.94 ×10−07
KEGG:hsa04960 Aldosterone-regulated sodium reabsorption 4 1.94×10−05
KEGG:hsa04510 Focal adhesion 5 1.40×10−04
KEGG:hsa05219 Bladder cancer 3 1.50×10−03
KEGG:hsa04742 Taste transduction 3 1.81×10−03
KEGG:hsa05212 Pancreatic cancer 3 3.73×10−03
KEGG:hsa04066 HIF-1 signaling pathway 3 8.32×10−03
KEGG:hsa04151 PI3K-Akt signaling pathway 4 1.14×10−02
KEGG:hsa05205 Proteoglycans in cancer 3 3.22×10−02
KEGG:hsa04015 Rap1 signaling pathway 3 3.52×10−02
KEGG:hsa04014 Ras signaling pathway 3 4.03×10−02
KEGG:hsa04060 Cytokine-cytokine receptor interaction 3 4.16×10−02

Two GO categories including GO FAT and GO Direct was used for GO analysis. The top 10 GO terms were selected by P-value. If the term was filtered out by GO DIRECT and GO FAT at the same time, the more significant one would be selected. The GO terms and pathways were ranked by P-value. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.