Open Access

Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma

  • Authors:
    • Ting Luo
    • Xiaoyi Chen
    • Shufei Zeng
    • Baozhang Guan
    • Bo Hu
    • Yu Meng
    • Fanna Liu
    • Taksui Wong
    • Yongpin Lu
    • Chen Yun
    • Berthold Hocher
    • Lianghong Yin
  • View Affiliations

  • Published online on: May 30, 2018     https://doi.org/10.3892/ol.2018.8842
  • Pages: 1747-1757
  • Copyright: © Luo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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.

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

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

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).

Table I.

DEGs in ccRCC tissues compared with normal controls.

Table I.

DEGs in ccRCC tissues compared with normal controls.

DEGsGene name
UpregulatedTNFAIP6, 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
DownregulatedPTGER3, 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

[i] 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.

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.

Table II.

KEGG pathway enrichment analysis of 65 upregulated DEGs.

Table II.

KEGG pathway enrichment analysis of 65 upregulated DEGs.

PathwayNameP-valueGenes
hsa04066HIF-1 signaling pathway 1.14×10−5PDK1, FLT1, VEGFA, EGLN3, ENO2, HK2, ANGPT2
hsa03320PPAR signaling pathway 4.19×10−4CD36, SCD, FABP7, FABP5, ANGPTL4
hsa04510Focal adhesion 7.01×10−4CAV2, VWF, LAMA4, CAV1, CCND1, FLT1, VEGFA
hsa04610Complement and coagulation cascades 5.81×10−3C1QB, VWF, C3, C1QC
hsa04152AMPK signaling pathway 2.70×10−2CCND1, CD36, SCD, PFKP
hsa05150Staphylococcus aureus infection 3.35×10−2C1QB, C3, C1QC
hsa04151PI3K-Akt signaling pathway 3.53×10−2VWF, LAMA4, CCND1, FLT1, VEGFA, ANGPT2
hsa05230Central carbon metabolism in cancer 4.57×10−2PDK1, PFKP, HK2
hsa00010 Glycolysis/Gluconeogenesis 4.96×10−2ENO2, PFKP, HK2

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

Table III.

KEGG pathway enrichment analysis of 164 downregulated DEGs.

Table III.

KEGG pathway enrichment analysis of 164 downregulated DEGs.

PathwayNameP-valueGenes
hsa01100Metabolic pathways 2.40×10−5TYRP1, 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
hsa04966Collecting duct acid secretion 2.40×10−5CLCNKB, SLC4A1, ATP6V1G3, ATP6V1B1, ATP6V0A4, ATP6V0D2
hsa04960 Aldosterone-regulated sodium reabsorption 1.51×10−4FXYD4, HSD11B2, SCNN1G, SCNN1B, SCNN1A, KCNJ1
hsa01200Carbon metabolism 3.81×10−3ALDH6A1, OGDHL, ALDOB, HAO2, FBP1, PSAT1, GLDC
hsa01130Biosynthesis of antibiotics 7.03×10−3HMGCS2, ASS1, OGDHL, ALDOB, HAO2, FBP1, PSAT1, PCK1, GLDC
hsa00010 Glycolysis/gluconeogenesis 1.19×10−2G6PC, ALDOB, FBP1, ADH6, PCK1
hsa04742Taste transduction 2.17×10−2PDE1A, SCNN1G, SCNN1B, SCNN1A
hsa05110Vibrio cholerae infection 3.32×10−2ATP6V1G3, ATP6V1B1, ATP6V0A4, ATP6V0D2
hsa00630Glyoxylate and dicarboxylate metabolism 4.96×10−2HAO2, HOGA1, GLDC

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

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.

Table IV.

Topology properties of 17 hub genes.

Table IV.

Topology properties of 17 hub genes.

Genes nameDegreeBetweenness centralityCloseness centralityClustering coefficientStressAverage shortest path length
ALB500.420.500.1030,7462.00
VEGFA350.140.420.1511,0302.40
EGF260.140.450.2511,9902.23
AQP2190.200.410.2315,9562.44
ENO2170.080.390.136,6102.60
PLG160.010.380.451,6442.62
CAV1150.050.390.294,1402.57
KNG1150.040.380.453,4142.62
CXCR4150.020.380.453,0202.62
FLT1150.010.390.511,4742.58
VWF140.000.370.525822.67
GLDC130.060.340.155,7082.96
DCN120.090.370.266,4422.69
CCND1120.040.380.472,9442.65
SLC12A1120.030.380.423,9422.62
ALDH4A1120.030.310.213,7763.20
FGF1110.020.370.531,5922.67

[i] The genes were ranked by degree.

Table V.

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

Table V.

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

TermDescriptionCountP-value
GO:0006811Ion transport12 6.36×10−10
GO:0034220Ion transmembrane transport101.07 ×10−08
GO:0007588Excretion51.97 ×10−08
GO:0016324Apical plasma membrane74.25 ×10−08
GO:0015672Monovalent inorganic cation transport84.97 ×10−08
GO:0050878Regulation of body fluid levels86.29 ×10−08
GO:0030001Metal ion transport97.29 ×10−08
GO:0016324Apical plasma membrane71.68 ×10−07
GO:0055085Transmembrane transport101.70 ×10−07
GO:0006812Cation transport92.94 ×10−07
KEGG:hsa04960 Aldosterone-regulated sodium reabsorption4 1.94×10−05
KEGG:hsa04510Focal adhesion5 1.40×10−04
KEGG:hsa05219Bladder cancer3 1.50×10−03
KEGG:hsa04742Taste transduction3 1.81×10−03
KEGG:hsa05212Pancreatic cancer3 3.73×10−03
KEGG:hsa04066HIF-1 signaling pathway3 8.32×10−03
KEGG:hsa04151PI3K-Akt signaling pathway4 1.14×10−02
KEGG:hsa05205Proteoglycans in cancer3 3.22×10−02
KEGG:hsa04015Rap1 signaling pathway3 3.52×10−02
KEGG:hsa04014Ras signaling pathway3 4.03×10−02
KEGG:hsa04060Cytokine-cytokine receptor interaction3 4.16×10−02

[i] 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.

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 (3034). 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 (4850). 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

1 

Li P, Znaor A, Holcatova I, Fabianova E, Mates D, Wozniak MB, Ferlay J and Scelo G: Regional geographic variations in kidney cancer incidence rates in European countries. Eur Urol. 67:1134–1141. 2015. View Article : Google Scholar : PubMed/NCBI

2 

Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 136:E359–E386. 2015. View Article : Google Scholar : PubMed/NCBI

3 

Srinivasan R, Ricketts CJ, Sourbier C and Linehan WM: New strategies in renal cell carcinoma: Targeting the genetic and metabolic basis of disease. Clin Cancer Res. 21:10–17. 2015. View Article : Google Scholar : PubMed/NCBI

4 

Capitanio U and Montorsi F: Renal cancer. Lancet. 387:894–906. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Gossage L and Eisen T: Alterations in VHL as potential biomarkers in renal-cell carcinoma. Nat Rev Clin Oncol. 7:277–288. 2010. View Article : Google Scholar : PubMed/NCBI

6 

Cancer Genome Atlas Research Network: Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 499:43–49. 2013. View Article : Google Scholar : PubMed/NCBI

7 

Ibragimova I, Maradeo ME, Dulaimi E and Cairns P: Aberrant promoter hypermethylation of PBRM1, BAP1, SETD2, KDM6A and other chromatin-modifying genes is absent or rare in clear cell RCC. Epigenetics. 8:486–493. 2013. View Article : Google Scholar : PubMed/NCBI

8 

Valera VA, Walter BA, Linehan WM and Merino MJ: Regulatory effects of microRNA-92 (miR-92) on VHL gene expression and the hypoxic activation of miR-210 in clear cell renal cell carcinoma. J Cancer. 2:515–526. 2011. View Article : Google Scholar : PubMed/NCBI

9 

McCormick RI, Blick C, Ragoussis J, Schoedel J, Mole DR, Young AC, Selby PJ, Banks RE and Harris AL: miR-210 is a target of hypoxia-inducible factors 1 and 2 in renal cancer, regulates ISCU and correlates with good prognosis. Br J Cancer. 108:1133–1142. 2013. View Article : Google Scholar : PubMed/NCBI

10 

Peña-Llopis S, Vega-Rubín-de-Celis S, Liao A, Leng N, Pavía-Jiménez A, Wang S, Yamasaki T, Zhrebker L, Sivanand S, Spence P, et al: BAP1 loss defines a new class of renal cell carcinoma. Nat Genet. 44:751–759. 2012. View Article : Google Scholar : PubMed/NCBI

11 

Eckel-Passow JE, Serie DJ, Bot BM, Joseph RW, Cheville JC and Parker AS: ANKS1B is a smoking-related molecular alteration in clear cell renal cell carcinoma. BMC Urol. 14:142014. View Article : Google Scholar : PubMed/NCBI

12 

Takahashi M, Tsukamoto Y, Kai T, Tokunaga A, Nakada C, Hijiya N, Uchida T, Daa T, Nomura T, Sato F, et al: Downregulation of WDR20 due to loss of 14q is involved in the malignant transformation of clear cell renal cell carcinoma. Cancer Sci. 107:417–423. 2016. View Article : Google Scholar : PubMed/NCBI

13 

Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:(Database Issue):. D991–D995. 2013. View Article : Google Scholar : PubMed/NCBI

14 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

15 

Huang da W, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI

16 

Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI

17 

Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI

18 

Wang J, Duncan D, Shi Z and Zhang B: WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): Update 2013. Nucleic Acids Res. 41:W77–W83. 2013. View Article : Google Scholar : PubMed/NCBI

19 

Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI

20 

Schödel J, Grampp S, Maher ER, Moch H, Ratcliffe PJ, Russo P and Mole DR: Hypoxia, hypoxia-inducible transcription factors, and renal cancer. Eur Urol. 69:646–657. 2016. View Article : Google Scholar : PubMed/NCBI

21 

Escudier B, Szczylik C, Porta C and Gore M: Treatment selection in metastatic renal cell carcinoma: Expert consensus. Nat Rev Clin Oncol. 9:327–337. 2012. View Article : Google Scholar : PubMed/NCBI

22 

Ciccarese C, Brunelli M, Montironi R, Fiorentino M, Iacovelli R, Heng D, Tortora G and Massari F: The prospect of precision therapy for renal cell carcinoma. Cancer Treat Rev. 49:37–44. 2016. View Article : Google Scholar : PubMed/NCBI

23 

Zhu C, Wang Y, Cai C and Cai Q: Bacterial infection and associated cancers. Adv Exp Med Biol. 1018:181–191. 2017. View Article : Google Scholar : PubMed/NCBI

24 

Hattar K, Reinert CP, Sibelius U, Gökyildirim MY, Subtil FSB, Wilhelm J, Eul B, Dahlem G, Grimminger F, Seeger W and Grandel U: Lipoteichoic acids from Staphylococcus aureus stimulate proliferation of human non-small-cell lung cancer cells in vitro. Cancer Immunol Immunother. 66:799–809. 2017. View Article : Google Scholar : PubMed/NCBI

25 

Fizazi K, Cojean I, Pignon JP, Rixe O, Gatineau M, Hadef S, Arriagada R, Baldeyrou P, Comoy E and Le Chevalier T: Normal 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 carcinoma. Cancer. 82:1049–1055. 1998. View Article : Google Scholar : PubMed/NCBI

26 

Oremek GM, Sauer-Eppel H and Bruzdziak TH: Value of tumour and inflammatory markers in lung cancer. Anticancer Res. 27:1911–1915. 2007.PubMed/NCBI

27 

Zhang T, Niu X, Liao L, Cho EA and Yang H: The contributions of HIF-target genes to tumor growth in RCC. PLoS One. 8:e805442013. View Article : Google Scholar : PubMed/NCBI

28 

MacLachlan TK, Sang N and Giordano A: Cyclins, cyclin-dependent kinases and cdk inhibitors: Implications in cell cycle control and cancer. Crit Rev Eukaryot Gene Expr. 5:127–156. 1995. View Article : Google Scholar : PubMed/NCBI

29 

Alao JP: The regulation of cyclin D1 degradation: Roles in cancer development and the potential for therapeutic invention. Mol Cancer. 6:242007. View Article : Google Scholar : PubMed/NCBI

30 

Arber N, Hibshoosh H, Moss SF, Sutter T, Zhang Y, Begg M, Wang S, Weinstein IB and Holt PR: Increased expression of cyclin D1 is an early event in multistage colorectal carcinogenesis. Gastroenterology. 110:669–674. 1996. View Article : Google Scholar : PubMed/NCBI

31 

Gautschi O, Ratschiller D, Gugger M, Betticher DC and Heighway J: Cyclin D1 in non-small cell lung cancer: a key driver of malignant transformation. Lung Cancer. 55:1–14. 2007. View Article : Google Scholar : PubMed/NCBI

32 

Bosch F, Jares P, Campo E, Lopez-Guillermo A, Piris MA, Villamor N, Tassies D, Jaffe ES, Montserrat E, Rozman C, et al: PRAD-1/cyclin D1 gene overexpression in chronic lymphoproliferative disorders: A highly specific marker of mantle cell lymphoma. Blood. 84:2726–2732. 1994.PubMed/NCBI

33 

Faust JB and Meeker TC: Amplification and expression of the bcl-1 gene in human solid tumor cell lines. Cancer Res. 52:2460–2463. 1992.PubMed/NCBI

34 

Buckley MF, Sweeney KJ, Hamilton JA, Sini RL, Manning DL, Nicholson RI, deFazio A, Watts CK, Musgrove EA and Sutherland RL: Expression and amplification of cyclin genes in human breast cancer. Oncogene. 8:2127–2133. 1993.PubMed/NCBI

35 

Han EK, Sgambato A, Jiang W, Zhang YJ, Santella RM, Doki Y, Cacace AM, Schieren I and Weinstein IB: Stable overexpression of cyclin D1 in a human mammary epithelial cell line prolongs the S-phase and inhibits growth. Oncogene. 10:953–961. 1995.PubMed/NCBI

36 

Han EK, Begemann M, Sgambato A, Soh JW, Doki Y, Xing WQ, Liu W and Weinstein IB: Increased expression of cyclin D1 in a murine mammary epithelial cell line induces p27kip1, inhibits growth, and enhances apoptosis. Cell Growth Differ. 7:699–710. 1996.PubMed/NCBI

37 

Sofer-Levi Y and Resnitzky D: Apoptosis induced by ectopic expression of cyclin D1 but not cyclin E. Oncogene. 13:2431–2437. 1996.PubMed/NCBI

38 

Kotelnikov VM, Coon JS IV, Mundle S, Kelanic S, LaFollette S, Taylor S IV, Hutchinson J, Panje W, Caldarelli DD and Preisler HD: Cyclin D1 expression in squamous cell carcinomas of the head and neck and in oral mucosa in relation to proliferation and apoptosis. Clin Cancer Res. 3:95–101. 1997.PubMed/NCBI

39 

Tang SW, Chang WH, Su YC, Chen YC, Lai YH, Wu PT, Hsu CI, Lin WC, Lai MK and Lin JY: MYC pathway is activated in clear cell renal cell carcinoma and essential for proliferation of clear cell renal cell carcinoma cells. Cancer Lett. 273:35–43. 2009. View Article : Google Scholar : PubMed/NCBI

40 

Sgambato A, Migaldi M, Faraglia B, de Aloysio G, Ferrari P, Ardito R, de Gaetani C, Capelli G, Cittadini A and Trentini GP: Cyclin D1 expression in papillary superficial bladder cancer: Its association with other cell cycle-associated proteins, cell proliferation and clinical outcome. Int J Cancer. 97:671–678. 2002. View Article : Google Scholar : PubMed/NCBI

41 

Holland TA, Elder J, McCloud JM, Hall C, Deakin M, Fryer AA, Elder JB and Hoban PR: Subcellular localisation of cyclin D1 protein in colorectal tumours is associated with p21(WAF1/CIP1) expression and correlates with patient survival. Int J Cancer. 95:302–306. 2001. View Article : Google Scholar : PubMed/NCBI

42 

Ogino S, Nosho K, Irahara N, Kure S, Shima K, Baba Y, Toyoda S, Chen L, Giovannucci EL, Meyerhardt JA and Fuchs CS: A cohort study of cyclin D1 expression and prognosis in 602 colon cancer cases. Clin Cancer Res. 15:4431–4438. 2009. View Article : Google Scholar : PubMed/NCBI

43 

Motzer RJ, Hutson TE, Tomczak P, Michaelson MD, Bukowski RM, Rixe O, Oudard S, Negrier S, Szczylik C, Kim ST, et al: Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med. 356:115–124. 2007. View Article : Google Scholar : PubMed/NCBI

44 

Escudier B, Eisen T, Stadler WM, Szczylik C, Oudard S, Staehler M, Negrier S, Chevreau C, Desai AA, Rolland F, et al: Sorafenib for treatment of renal cell carcinoma: Final efficacy and safety results of the phase III treatment approaches in renal cancer global evaluation trial. J Clin Oncol. 27:3312–3318. 2009. View Article : Google Scholar : PubMed/NCBI

45 

Klatte T, Seligson DB, LaRochelle J, Shuch B, Said JW, Riggs SB, Zomorodian N, Kabbinavar FF, Pantuck AJ and Belldegrun AS: Molecular signatures of localized clear cell renal cell carcinoma to predict disease-free survival after nephrectomy. Cancer Epidemiol Biomarkers Prev. 18:894–900. 2009. View Article : Google Scholar : PubMed/NCBI

46 

Beuselinck B, Jean-Baptiste J, Schöffski P, Couchy G, Meiller C, Rolland F, Allory Y, Joniau S, Verkarre V, Elaidi R, et al: Validation of VEGFR1 rs9582036 as predictive biomarker in metastatic clear-cell renal cell carcinoma patients treated with sunitinib. BJU Int. 118:890–901. 2016. View Article : Google Scholar : PubMed/NCBI

47 

Kendall RL and Thomas KA: Inhibition of vascular endothelial cell growth factor activity by an endogenously encoded soluble receptor. Proc Natl Acad Sci USA. 90:10705–10709. 1993. View Article : Google Scholar : PubMed/NCBI

48 

Miyake T, Kumasawa K, Sato N, Takiuchi T, Nakamura H and Kimura T: Soluble VEGF receptor 1 (sFLT1) induces non-apoptotic death in ovarian and colorectal cancer cells. Sci Rep. 6:248532016. View Article : Google Scholar : PubMed/NCBI

49 

Takano S, Ishikawa E, Matsuda M, Sakamoto N, Akutsu H, Yamamoto T and Matsumura A: The anti-angiogenic role of soluble-form VEGF receptor in malignant gliomas. Int J Oncol. 50:515–524. 2017. View Article : Google Scholar : PubMed/NCBI

50 

Niu J, Wang Y, Wang J, Bin L and Hu X: Delivery of sFIT-1 engineered MSCs in combination with a continuous low-dose doxorubicin treatment prevents growth of liver cancer. Aging (Albany NY). 8:3520–3534. 2016. View Article : Google Scholar : PubMed/NCBI

51 

Vilsmaier T, Rack B, Janni W, Jeschke U and Weissenbacher T; SUCCESS Study Group: Angiogenic cytokines and their influence on circulating tumour cells in sera of patients with the primary diagnosis of breast cancer before treatment. BMC Cancer. 16:5472016. View Article : Google Scholar : PubMed/NCBI

52 

Kwaan HC and McMahon B: The role of plasminogen-plasmin system in cancer. Cancer Treat Res. 148:43–66. 2009. View Article : Google Scholar : PubMed/NCBI

53 

McMahon BJ and Kwaan HC: Components of the plasminogen-plasmin system as biologic markers for cancer. Adv Exp Med Biol. 867:145–156. 2015. View Article : Google Scholar : PubMed/NCBI

54 

Schrödter S, Braun M, Syring I, Klümper N, Deng M, Schmidt D, Perner S, Müller SC and Ellinger J: Identification of the dopamine transporter SLC6A3 as a biomarker for patients with renal cell carcinoma. Mol Cancer. 15:102016. View Article : Google Scholar : PubMed/NCBI

55 

Zhao S, Dorn J, Napieralski R, Walch A, Diersch S, Kotzsch M, Ahmed N, Hooper JD, Kiechle M, Schmitt M and Magdolen V: Plasmin(ogen) serves as a favorable biomarker for prediction of survival in advanced high-grade serous ovarian cancer. Biol Chem. 398:765–773. 2017. View Article : Google Scholar : PubMed/NCBI

56 

Mojiri A, Stoletov K, Carrillo MA, Willetts L, Jain S, Godbout R, Jurasz P, Sergi CM, Eisenstat DD, Lewis JD and Jahroudi N: Functional assessment of von Willebrand factor expression by cancer cells of non-endothelial origin. Oncotarget. 8:13015–13029. 2017. View Article : Google Scholar : PubMed/NCBI

57 

Mochizuki S, Soejima K, Shimoda M, Abe H, Sasaki A, Okano HJ, Okano H and Okada Y: Effect of ADAM28 on carcinoma cell metastasis by cleavage of von Willebrand factor. J Natl Cancer Inst. 104:906–922. 2012. View Article : Google Scholar : PubMed/NCBI

58 

Braybrooke JP, O'Byrne KJ, Propper DJ, Blann A, Saunders M, Dobbs N, Han C, Woodhull J, Mitchell K, Crew J, et al: A phase II study of razoxane, an antiangiogenic topoisomerase II inhibitor, in renal cell cancer with assessment of potential surrogate markers of angiogenesis. Clin Cancer Res. 6:4697–4704. 2000.PubMed/NCBI

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August-2018
Volume 16 Issue 2

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Spandidos Publications style
Luo T, Chen X, Zeng S, Guan B, Hu B, Meng Y, Liu F, Wong T, Lu Y, Yun C, Yun C, et al: Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma. Oncol Lett 16: 1747-1757, 2018.
APA
Luo, T., Chen, X., Zeng, S., Guan, B., Hu, B., Meng, Y. ... Yin, L. (2018). Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma. Oncology Letters, 16, 1747-1757. https://doi.org/10.3892/ol.2018.8842
MLA
Luo, T., Chen, X., Zeng, S., Guan, B., Hu, B., Meng, Y., Liu, F., Wong, T., Lu, Y., Yun, C., Hocher, B., Yin, L."Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma". Oncology Letters 16.2 (2018): 1747-1757.
Chicago
Luo, T., Chen, X., Zeng, S., Guan, B., Hu, B., Meng, Y., Liu, F., Wong, T., Lu, Y., Yun, C., Hocher, B., Yin, L."Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma". Oncology Letters 16, no. 2 (2018): 1747-1757. https://doi.org/10.3892/ol.2018.8842