Open Access

Identification of hub genes associated with outcome of clear cell renal cell carcinoma

  • Authors:
    • Rengui Li
    • Lei Wang
    • Xiao Wang
    • Rong‑Xin Geng
    • Ning Li
    • Xiu‑Heng Liu
  • View Affiliations

  • Published online on: February 12, 2020
  • Pages: 2846-2860
  • Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Clear cell renal cell carcinoma (ccRCC) is one of the most common tumor types of the urinary system. Bioinformatics tools have been used to identify new biomarkers of ccRCC and to explore the mechanisms underlying development and progression of ccRCC. The present study analyzed the differentially expressed genes (DEGs) associated with RCC using data obtained from Gene Expression Omnibus datasets and GEO2R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of these DEGs was performed and analyzed using the Database for Annotation, Visualization and Integrated Discovery. A protein‑protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes to identify the hub genes, defined as the genes with the highest degree of interrelation. Subsequently, differential expression and survival analyses of hub genes was performed using The Cancer Genome Atlas database and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Using GEO2R, 1,650 DEGs were identified, including 743 upregulated and 907 downregulated genes. GO and KEGG pathway analyses indicated that the upregulated DEGs were primarily involved in blood vessel and vasculature development, whereas downregulated DEGs were primarily involved in organic acid metabolic processes and carboxylic acid metabolic processes. Subsequently, important modules were identified in the PPI network using Cytoscape's Molecular Complex Detection. The 15 most connected hub genes were identified among DEGs, including glycine decarboxylase (GLDC), enolase 2 (ENO2) and topoisomerase II alpha. GEPIA revealed the association between expression levels of hub genes and survival. Specifically, GLDC and ENO2 were associated with the prognosis of patients with RCC and thus, the effects of GLDC and ENO2 involvement in renal cancer were investigated in vitro. GLDC and ENO2 affected the proliferation and apoptosis of renal cancer cells. These hub genes may reveal a new mechanism underlying development or progression of RCC and identify new markers for its diagnosis and prognosis.


Renal cell carcinoma (RCC) accounts for 2–3% of malignant tumors in adults (1). Notably, clear cell RCC (ccRCC) accounts for 70–85% of kidney cancer cases with an increasing incidence worldwide (2). Clinically, RCC can be divided into four stages according to the size of the tumor and extent of invasion and metastasis. The insidious onset of symptoms usually results in diagnosis in the first instance at an advanced stage of the disease (3). Owing to the resistance of ccRCC to radiotherapy and chemotherapy, the most effective treatment is radical or partial nephrectomy. However, the mortality rate of patients with metastatic RCC remains high (4). Targeted therapy with agents such as sorafenib and sunitinib have been used for metastatic RCC; however, its curative effects are limited (5). Thus, it is important to identify biomarkers to aid early diagnosis of RCC and to provide novel therapeutic targets.

Recently, a number of biomarkers for diagnosis and prognosis have been found and investigated. For example, the CXC chemokine receptor 4 (CXCR4), which is one of the most important markers of cancer stem cells, has been confirmed as a major chemokine receptor in solid tumors (6). A previous study demonstrated that CXCR4 may predict survival in patients with RCC. High-throughput sequencing is a common tool used in medical research in numerous types of cancer concerning early diagnosis, staging, grading and prognosis (7,8). The use of bioinformatics to screen high-throughput sequencing allows identification of differentially expressed genes (DEGs) that may be associated with occurrence and development of certain diseases. In the present study, a Gene Expression Omnibus (GEO) dataset was selected and bioinformatics analysis was performed to identify the DEGs in ccRCC. Subsequently, the Search Tool for the Retrieval of Interacting Genes (STRING) was used to construct a protein-protein interaction (PPI) network and identify the hub genes in ccRCC. DEGs were analyzed to determine the biological process (BP), molecular function (MF) or cellular component (CC) associated with the genes using Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 15 hub genes were selected and overall survival analysis was performed to determine the relationship between each gene and survival of patients with ccRCC. The aim of the present study was to identify novel biomarkers and targets for the diagnosis and prognosis of RCC.

Materials and methods


Data relevant to RCC was obtained from the GEO database and the GSE40435 ( dataset was selected (9). This dataset was established on a GPL10558 platform. The GSE40435 dataset contains 101 pairs of ccRCC tumors and healthy adjacent tissue samples (age range, 42–84 years). In this database the basic information regarding each patient, including age, sex, tumor grade and type of tissue, is complete.

Differential gene expression analysis

The online software tool GEO2R ( was used to analyze the samples in the GSE40435 dataset. Using the GEO2R software, samples can be divided into two or more groups and the DEGs can be selected (10). A total of 202 samples were divided into two groups; the ccRCC tumor group and the adjacent non-tumor group. The Benjamini-Hochberg method was used to determine the false discovery rate (11) and the adjusted P-value was used to reduce the likelihood of false positive errors. The selection criteria included an adjusted P-value of <0.05 and |log fold change (FC)|≥1 (12).

Gene Ontology (GO) and KEGG pathway analysis of DEGs

GO analysis is a valuable approach to annotate genes and gene products, and assign characteristic biological properties to high-throughput genomic or transcriptome data (13). KEGG is a database collection which can be used to analyze genomes, biological pathways, diseases, chemical substances and drugs (14). DEGs selected by GEO2R were stratified according to whether they were upregulated or downregulated. Database for Annotation, Visualization and Integrated Discovery (DAVID; was used for annotation of the GO and KEGG pathway results (15). P<0.05 was used as a selection criterion for major BPs, MF and CC.

Establishing the PPI network and analyzing modules

STRING is an online application for evaluating PPI networks (16). STRING was used to map DEGs and identify potential interaction between DEGs. The selection criteria were: Confidence score ≥0.4 and max number of interactors to show none/query proteins only (17,18). Furthermore, the Molecular Complex Detection (MCODE) application in Cytoscape (version 3.6.1) was applied to select the PPI network modules, with a cutoff=2, node score cutoff=0.2, k-core=2 and maximum depth=100 as the selection criteria (18). GO and KEGG pathway analyses of the selected modules were performed in DAVID to explore potential information. The network of 15 selected hub genes were visualized using STRING and the selection criteria were: Confidence score ≥0.4 and a maximum number of interactors ≤5. Fig. 1 shows a flow diagram of the methodology.

Comparing expression and survival

The differential gene expression in patients with RCC and patients without tumors was investigated using the standardized output of RNA sequencing data in The Cancer Genome Atlas and Genotype-Tissue Expression project databases (19). GEPIA was used to determine differences in the expression of hub genes between RCC tissues and non-tumorous tissues, and the results were visualized as box plots. Additionally, the Gene Expression Profiling Interactive Analysis (GEPIA; also detects changes in the survival curves caused by hub genes in RCC patients. University of California Santa Cruz Xena ( was used to perform Kaplan-Meier survival analyses (20). Immunohistochemical data of patients with RCC and healthy individuals were obtained from the Human Protein Atlas (HPA) (21) to confirm the expression levels of the hub genes. For GLDC, the normal tissue was from a 61-year old man and the tumor was from a 59-years old woman. For ENO2, the normal tissue was from a 52-years old woman and the tumor was from a 52-years old woman. The GLDC expression level, age, sex and tumor stage of the 877 patients (age range, 28–90 years; mean age, 62 years) with renal cancer was obtained from the HPA ( to explore the patient characteristics in the glycine decarboxylase (GLDC) high and GLDC low groups. After removal of incomplete data, complete information of 840 patients was gathered. Subsequently, these 840 patients were divided into two groups according to the level of GLDC expression (n=420 for each group). The relationship between the expression of GLDC and patient characteristics was then analyzed using χ2 test.

Gene set enrichment analysis (GSEA)

RCC samples (n=101) from the GSE40435 dataset were divided into a high and low expression groups according to the median expression levels of GLDC and enolase 2 (ENO2). GSEA (version 3.0; was used to examine the potential biological functions of GLDC and ENO2. The reference gene sets were annotated. Gene sets: c2.cp.kegg.v5.2.symbols.gmt, sets c2.cp.bp. v5.2.symbols.gmt, v5.2.symbols.gmt and sets v5.2.symbols.gmt. The cut-off criteria were P<0.05 P<0.05, enrichment score (ES)>0.5 and gene size ≥100 (22,23).

Cell culture and transfection of small interfering (si)RNA and small activating (sa)RNA

Human RCC cell line 786-O and normal renal tubular epithelial cell line HK-2 were obtained from The China Center for Type Culture Collection. The saRNA negative control and siRNA were purchased from Shanghai GenePharma Co., Ltd., and were used at the concentration of 40 nM. The siRNAs were transfected into cells using Lipofectamine® 2000 (Invitrogen, Thermo Fisher Scientific, Inc.) for 48 h prior to subsequent experimentation. Cells were cultured at the density of 2×106 cells in RPMI-1640 media supplemented with 10% FBS (Gibco; Thermo Fisher Scientific, Inc.), 1% antibiotic solution (penicillin 100 U/ml and streptomycin 100 g/ml; Beyotime Institute of Biotechnology) at 37°C with 5% CO2 in a humidified incubator. Before transfection, 5×104 cells were seeded into 6-well plate. Sequences for siRNA targeting ENO2 were as follows: 5′-UUCUCUAUGGACAUGAUGGCU-3′ guide, 5′-CCAUCAUGUCCAUAGAGAAGA-3′ passenger. Sequences for saRNA targeting GLDC were as follows: 5′-AGUGUCUUGGUUGAGCGCA-3′ guide; 5′-UGCGCUCAACCAAGACACU-3′ passenger.

Cell proliferation assay

Cell viability was quantitatively evaluated using a Cell Counting kit-8 (CCK-8; Beyotime Institute of Biotechnology). After transfection by siRNA, saRNA and negative control, a total of 7×103 RCC cells/well were seeded into 96-well plates and exposed to a combination of different conditions. CCK-8 solution (10 µl) was added to each well for 2 h at 37°C, after which the optical density was estimated by measuring the absorbance at 450 nm using a microplate reader (Victor 3 1420 Multilabel Counter; PerkinElmer, Inc.).

Western blotting

RCC cells were cultured in 6-well plates for 48 h and transfected with siRNA or shRNA. Cells were lysed with RIPA buffer containing protease inhibitors (Beyotime Institute of Biotechnology. A bicinchoninic acid protein assay kit (Beyotime Institute of Biotechnology) was used to determine the concentration of protein. Proteins (25 µg) were separated by 10% SDS-PAGE and subsequently transferred to PVDF membranes (EMD Millipore). The membranes were blocked in 5% non-fat dry milk in TBS. Subsequently, the membranes were incubated with a rabbit or mouse primary antibody against GLDC (1:1,000; Abcam; cat. no. ab232989), ENO2 (1:1,000; Abcam; cat. no. ab189891), Bax (1:1,000; Abcam; cat. no. ab32503) and Bcl-2 (1:1,000; Abcam; cat. no. ab32124) at 4°C overnight. After washing with TBS-Tween 20 three times (10 min/wash), the membranes were incubated with the relevant secondary antibody for 1 h at room temperature, followed by three washes with TBS-Tween 20 in the dark. The membranes were scanned using a two-color Odyssey infrared imaging system (LI-COR Biosciences). Protein expression levels were normalized to those of GAPDH from the same membrane. Densitometry analysis was performed using ImageJ software (version 1.52r; National Institutes of Health).

Annexin V-phycoerythrin/7-aminoactinomycin D (Annexin V-PE/7-AAD) double staining assay for apoptosis

The Annexin V-PE/7-AAD kit (MultiSciences) and Annexin-V-FITC/PI kit (BD Biosciences) were used to quantify the percentage of apoptotic cells using a flow cytometer (FACSCalibur; BD Biosciences). FlowJo v10.6.1 software (FlowJo, LLC) was used for analysis. Cells were seeded into 6-well plates after being transfected for 48 h with siRNA or saRNA, NC and control. Adherent cells were collected and co-stained with 5 µl Annexin V-PE and 5 µl 7-AAD for 15 min at room temperature in the dark prior to flow cytometry analysis. Live cells were cells fluorescing positively for both PE and 7-AAD negative, early apoptotic cells were cells fluorescing with PE alone, necrotic cells did not show fluorescence for either fluorophore and late apoptotic and dead cells showed 7-AAD fluorescence alone.

Hoechst 33258 apoptosis assay

Hoechst 33258 Staining kit (Beyotime Institute of Biotechnology) was used to detect apoptotic morphological features. A total of 1×105 cells/well in the exponential growth phase were seeded into a 6-well plate. Cells were cultured for 24 h and stained with Hoechst 33258 at room temperature for 15 min. Apoptotic morphological features (chromatin condensation, nuclear fragmentation) were observed and captured using a fluorescent microscope (BX51; Olympus Corporation; magnification, ×200).

Statistical analysis

Data analysis was performed in GraphPad prism version (GraphPad Software, Inc.). Data are presented as the mean ± standard error of mean of 3 repeats. An ANOVA with a post-hoc Tukey's test was used to compare the differences between multiple groups. P<0.05 was considered to indicate a statistically significant difference.


DEGs and hub genes

The GSE40435 dataset contains 202 samples, including 101 pairs of ccRCC tumors and adjacent healthy tissue. A total of 1,650 DEGs (743 upregulated and 907 downregulated) were selected. The volcano map (Fig. 2A) and the heat map (Fig. 2B) graphically visualize the selected DEGs with P<0.05 and |log FC|≥1. Subsequently, the 15 most relevant hub genes were selected according to the degree of connectivity in PPI networks (Table I).

Table I.

Top 15 hub genes with highest degree of connectivity.

Table I.

Top 15 hub genes with highest degree of connectivity.

GeneDegree of connectivityAdjusted P-value
ALB222 3.05×10−56
VEGFA150 1.39×10−58
TOP2A141 5.15×10−53
EGFR114 2.73×10−32
EGF111 5.01×10−60
EHHADH102 7.34×10−17
MYC100 2.62×10−49
CD44  97 6.41×10−30
GLDC  95 1.37×10−67
ALDH7A1  84 3.05×10−52
ENO2  83 7.62×10−73
CSF1R  81 1.43×10−43
ALDH3A2  81 8.34×10−29
ALDH1B1  79 1.89×10−49
ALDH4A1  79 4.13×10−56
GO and KEGG pathway enrichment analyses

GO and KEGG pathway enrichment analyses were performed by inputting upregulated and downregulated DEGs into DAVID to determine their relationship and study the functions of the DEGs (Fig. 3). Fig. 3A and B show the results of GO enrichment analysis. The upregulated DEGs were enriched in BPs, including ‘blood vessel development’, ‘vasculature development’, ‘angiogenesis’, ‘locomotion’ and ‘extracellular matrix organization’. The downregulated DEGs were enriched in BPs, including ‘organic acid metabolic process’, ‘carboxylic acid metabolic process’, ‘oxoacid metabolic process’, ‘organic acid catabolic process’ and ‘small molecule catabolic process’. For MF, the upregulated DEGs were primarily enriched in ‘receptor binding’, ‘identical protein binding’, ‘cargo receptor activity’, ‘protein homodimerization activity’ and ‘extracellular matrix binding’. The downregulated DEGs were enriched in ‘cofactor binding’, ‘anion binding’, ‘active transmembrane transporter activity’, ‘secondary active transmembrane transporter activity’ and ‘carboxylic acid binding’ (Table II). For CC, the upregulated DEGs were enriched in ‘extracellular space’, ‘collagen trimer’, ‘extracellular region’, ‘extracellular region part’ and ‘proteinaceous extracellular matrix’. Downregulated DEGs were enriched in ‘extracellular exosome’, ‘extracellular vesicle’, ‘extracellular organelle’, ‘membrane-bounded vesicle’ and ‘extracellular region part’ (Table II).

Table II.

Gene ontology analysis of differentially expressed genes associated with renal cell carcinoma.

Table II.

Gene ontology analysis of differentially expressed genes associated with renal cell carcinoma.

A, Upregulated genes

  GO:0001568-blood vessel development1922.89157 5.91×10−11 1.05×10−07
  GO:0001944-vasculature development1922.89157 1.50×10−10 2.68×10−07
  GO:0001525-angiogenesis1518.07229 3.79×10−09 6.76×10−06
  GO:0040011-locomotion2530.12048 2.81×10−08 5.02×10−05
  GO:0030198-extracellular matrix organization1315.66265 2.94×10−08 5.23×10−05
  GO:0005615-extracellular space3137.3494 2.51×10−12 3.19×10−09
  GO:0005581-collagen trimer1012.04819 1.94×10−09 2.47×10−06
  GO:0005576-extracellular region4959.03614 2.58×10−09 3.29×10−06
  GO:0044421-extracellular region part4453.01205 6.54×10−09 8.32×10−06
  GO:0005578-proteinaceous extracellular matrix1416.86747 2.49×10−08 3.17×10−05
  GO:0005102-receptor binding2327.71084 3.23×10−07 4.53×10−04
  GO:0042802-identical protein binding2024.09639 7.57×10−060.010638
  GO:0038024-cargo receptor activity56.024096 2.81×10−040.39348
  GO:0042803-protein homodimerization activity1113.253010.0017462.425067
  GO:0050840-extracellular matrix binding44.8192770.0018392.551809

B, Downregulated

  GO:0070062-extracellular exosome10754.87179 3.25×10−33 4.34×10−30
  GO:1903561-extracellular vesicle10754.87179 5.11×10−33 6.83×10−30
  GO:0043230-extracellular organelle10754.87179 5.27×10−33 7.06×10−30
  GO:0031988-membrane-bounded vesicle10855.38462 2.53×10−24 3.39×10−21
  GO:0044421-extracellular region part11056.41026 5.33×10−23 7.13×10−20
  GO:0048037-cofactor binding189.230769 1.71×10−08 2.57×10−05
  GO:0043168-anion binding189.230769 2.91×10−08 4.37×10−05
  GO:0022804-active transmembrane transporter activity2010.25641 4.68×10−08 7.04×10−05
  GO:0015291-secondary active transmembrane transporter activity168.205128 8.44×10−08 1.27×10−04
  GO:0031406-carboxylic acid binding126.153846 3.06×10−060.004593

[i] GO, Gene Ontology; BP, biological processes; MF, molecular function; CC, cell component; FDR, false discovery rate.

The upregulated DEGs were enriched in ‘Focal adhesion’, the ‘PI3K-Akt signaling pathway’, ‘ECM-receptor interaction’, the HIF-1 signaling pathway’ and ‘Staphylococcus aureus infection’. The downregulated DEGs were enriched in ‘Metabolic pathways’, ‘Biosynthesis of antibiotics’, ‘Glycine, serine and threonine metabolism’, ‘Mineral absorption’ and ‘Aldosterone-regulated sodium reabsorption’ (Fig. 3C; Table III).

Table III.

Kyoto Encyclopedia of Genes and Genomes pathway analysis of differentially expressed genes associated with renal cell carcinoma.

Table III.

Kyoto Encyclopedia of Genes and Genomes pathway analysis of differentially expressed genes associated with renal cell carcinoma.

A, Upregulated

TermCountRatioP-valueGenesFalse discovery rate
hsa04510: Focal adhesion910.84337 1.15×10−04CAV2, VWF, LAMA4, CAV1, PGF, VEGFA, COL1A2, COL1A1, BIRC30.133554
hsa04151: PI3K-Akt signaling pathway910.843370.003446VWF, LAMA4, PGF, VEGFA, COL1A2, COL1A1, ANGPT2, MYC, DDIT43.92945
hsa04512: ECM-receptor interaction56.0240960.003733VWF, LAMA4, CD36, COL1A2, COL1A14.250896
hsa04066: HIF-1 signaling pathway56.0240960.005709VEGFA, SLC2A1, ENO2, ANGPT2, TIMP16.433293
hsa05150: Staphylococcus aureus infection44.8192770.007211C1QB, C3, ITGB2, C1QC8.060809

B, Downregulated

TermCountRatio, %P-valueGenesFalse discovery rate

hsa01130: Biosynthesis of antibiotics168.205128 1.74×10−06ACY1, ASS1, SUCLG1, ALDOB, OGDHL, FBP1, ECHS1, PCK2, AGXT, PCK1, GLDC, HMGCS2, HAO2, PSAT1, HADH, ACAA10.002068
hsa00260: Glycine, serine and threonine metabolism  84.102564 2.75×10−06CHDH, GATM, BHMT, AGXT2, PSAT1, AGXT, PIPOX, GLDC0.003272
hsa04978: Mineral absorption  84.102564 8.70×10−06FXYD2, MT1A, MT1E, ATP1A1, MT1H, MT1X, MT1G, MT1F0.01035
hsa04960: Aldosterone-regulated sodium reabsorption  73.589744 3.70×10−05FXYD2, FXYD4, HSD11B2, ATP1A1, SCNN1G, SCNN1A, KCNJ10.044004
Screening hub genes and three modules from the PPI network

The PPI network constructed using hub genes revealed the correlation between DEGs, where hub genes with higher degrees of correlation were detected using Cytoscape. The following hub genes were selected: Albumin; vascular endothelial growth factor-A; topoisomerase II a; epidermal growth factor receptor (EGFR); EGF; enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase; MYC; CD44; GLDC; aldehyde dehydrogenase 7 family member A1; ENO2; colony stimulating factor 1 receptor; aldehyde dehydrogenase 3 family member A2; aldehyde dehydrogenase 1 family member B1; and aldehyde dehydrogenase 4 family member A1. Subsequently, a PPI network of the 15 most relevant hub genes was constructed (Fig. 4A) and the top four modules were identified using the MCODE application in Cytoscape (Fig. 4B-E). These four modules were associated with valine, leucine and isoleucine degradation, retinol metabolism, glycolysis and gluconeogenesis (Table IV).

Table IV.

Enriched pathways of the top 4 modules from the protein-protein interaction network.

Table IV.

Enriched pathways of the top 4 modules from the protein-protein interaction network.

A, Module 1

Valine, leucine and isoleucine degradation 6.66×10−13 7.95×10−10DBT, ACADSB, EHHADH, ECHS1, ACAD8, HIBCH, HADH, ACAT1, PCCB, ALDH3A2, ACAA1, AUH
Staphylococcus aureus infection 9.85×10−11 1.18×10−07FGG, C3, FCGR1A, C5, FPR1, FPR3, HLA-DPA1, HLA-DPB1, CFD, PLG, HLA-DRA
ECM-receptor interaction 7.80×10−10 9.32×10−07VWF, COL4A2, COL4A1, CD44, COL3A1, COL6A3, COL1A2, COL6A2, COL1A1, COL5A2, COL5A1, COL4A5
Graft-versus-host disease 8.35×10−10 9.98×10−07HLA-A, HLA-C, HLA-DPA1, HLA-B, HLA-DPB1, HLA-E, HLA-G, HLA-DRA, HLA-F
Allograft rejection 2.25×10−09 2.69×10−06HLA-A, HLA-C, HLA-DPA1, HLA-B, HLA-DPB1, HLA-E, HLA-G, HLA-DRA, HLA-F

B, Module 2


Valine, leucine and isoleucine degradation 1.75×10−11 1.45×10−08ALDH6A1, ALDH7A1, ACADM, ALDH1B1, HIBADH, HMGCL, PCCA
Metabolic pathways 3.41×10−08 2.83×10−05ACSM3, ACOX2, ALDH6A1, ALDH7A1, ACADM, ALDH1B1, ACSS2, HIBADH, HMGCL, PCCA, GLDC
Biosynthesis of antibiotics 6.42×10−060.005321ALDH7A1, ACADM, ALDH1B1, ACSS2, PCCA, GLDC
Propanoate metabolism 7.19×10−060.005954ALDH6A1, ACADM, ACSS2, PCCA
beta-Alanine metabolism 1.08×10−050.008986ALDH6A1, ALDH7A1, ACADM, ALDH1B1

C, Module 3


Retinol metabolism 4.18×10−060.004763RDH12, ALDH1A2, DHRS4, AOX1, ADH6, ADH1A
Glycolysis / Gluconeogenesis 5.31×10−040.602677LDHA, FBP1, ADH6, ADH1A, GAPDH
Metabolic pathways0.0017852.014139CHDH, LDHA, ALDH5A1, FBP1, ADH6, ADH1A, RDH12, ALDH1A2, DHRS4, AOX1, DMGDH, AGXT2, GAPDH, HSD17B8
PI3K-Akt signaling pathway0.0038754.325624EGFR, CCND1, PGF, FGF1, ANGPT2, ITGB1, MYC
Proteoglycans in cancer0.01251413.36304EGFR, CCND1, SDC4, ITGB1, MYC

D, Module 4


Glycolysis/Gluconeogenesis 5.96×10−08 6.07×10−05ALDOC, ALDOB, PGAM1, HK2, PFKP, ENO2, PDHB
Carbon metabolism 1.98×10−060.002013ALDOC, ALDOB, PGAM1, HK2, PFKP, ENO2, PDHB
Biosynthesis of antibiotics 7.50×10−050.076404ALDOC, ALDOB, PGAM1, HK2, PFKP, ENO2, PDHB
Biosynthesis of amino acids 1.18×10−040.119904ALDOC, ALDOB, PGAM1, PFKP, ENO2
Fructose and mannose metabolism 1.64×10−040.167132ALDOC, ALDOB, HK2, PFKP

[i] FDR, False Discovery Rate.

Differential expression and survival curves of hub genes

The GEPIA online tool revealed the upregulated and downregulated expression of genes between RCC and non-tumorous tissues (Fig. 5A and B). Expression levels of GLDC mRNA were significantly decreased in RCC compared with normal tissues (Fig. 5A), whereas ENO2 mRNA expression levels were significantly increased in renal carcinoma compared with normal tissues (Fig. 5B). In addition, immunohistochemical data of patients with RCC and normal patients were analyzed using HPA, and the results demonstrated that GLDC expression was low in tumor whereas ENO2 expression was high in tumor (Fig. 5C and D). KM survival curves indicated that GLDC may inhibit tumor development and ENO2 may promote tumor progression (Fig. 5E and F). GEPIA and UCSC Xena were performed, demonstrating that low expression levels of GLDC in RCC was associated with a less favorable prognosis (P=0.0082 and P=0.02541, respectively). Analyses of ENO2 revealed that high expression levels of ENO2 was associated with poor prognosis (GEPIA, P=0.02; UCSC Xena, P=0.01375). CIs and log-rank P-value were calculated as shown in the plot (Fig. 5E and F). The relationship between the expression levels of GLDC and patient characteristics were analyzed, demonstrating that GLDC expression levels are significantly associated with patient age and disease stage, whereas no significant difference in terms of sex were observed (Table V).

Table V.

Relationship between GLDC expression and characteristics of 840 patients with renal cell carcinoma from the Human Protein Atlas.

Table V.

Relationship between GLDC expression and characteristics of 840 patients with renal cell carcinoma from the Human Protein Atlas.

CharacteristicsnGLDC expressionχ2P-value
Age HighLow8.2230.00414
Sex 3.1080.07791
Stage 10.2290.00138

[i] GLDC, glycine decarboxylase.


GSEA was performed to relate the hub genes to the GO analysis and KEGG pathway database and explore the function of these genes. A total of four functional gene sets were enriched. The gene sets enriched in RCC with high expression of GLDC were ‘ribosomal subunit’ and ‘structural constituent of ribosome’. These two gene sets were primarily involved in cell proliferation and differentiation pathways (Fig. 5G). The gene sets enriched in RCC with high ENO2 expression levels were ‘establishment of protein localization to endoplasmic reticulum’ and ‘protein localization to endoplasmic reticulum’ (Fig. 5H). These gene sets were focused on pathways associated with cell apoptosis and endoplasmic reticulum stress.

GLDC acts as a tumor suppressor gene, whereas ENO2 is an oncogene in RCC

The expression levels of GLDC and ENO2 were examined in the normal renal tubular epithelial cell line HK-2 and the renal clear cell carcinoma cell line 786-O. It was found that GLDC had reduced expression levels in 786-O cells, whereas ENO2 was highly expressed in 786-O cells. saRNA was used to induce GLDC overexpression in 786-O cells and siRNA was used to knockdown ENO2 expression (Fig. 6A). There was a significant decrease in the proliferative capacity of the sa-GLDC-786-O and si-ENO2-786-O cells after 72 h compared to control group (P<0.0001; Fig. 6B). An Annexin V-PE/7-AAD double staining assay was performed detect cell apoptosis using flow cytometry. The number of apoptotic 786-O cells was increased in sa-GLDC-786-O cells and si-ENO2-786-O cells compared to control group (Fig. 6C) and the results of Hoechst 33258 staining assay were consistent with Annexin V-PE/7-AAD double staining assay (Fig. 6D and E). Finally, it was demonstrated that the expression of Bax increased and expression of Bcl-2 decreased (Fig. 6F). These data showed that GLDC promoted and ENO2 inhibited apoptosis of renal cancer cells, which suggests that GLDC acts as a tumor suppressor gene whereas ENO2 acts as an oncogene in RCC.


A tumor is a manifestation of a disease dependent on a number of genes and its formation is associated with a variety of factors, including metabolic capacity and immune regulation. The essential attribute of a tumor is the loss of cell cycle regulation control, resulting in uncontrollable cell proliferation (24). The reasons for this may be the activation of one or more proto-oncogenes and mutations of or deletions in tumor suppressor genes (25). Genetic deletion mutations can lead to the inability of the immune system to recognize tumors, leading to immune escape (26). Therefore, changes in gene expression levels in cancer are particularly important.

The insidious onset of symptoms usually results in delayed diagnosis at an advanced stage of RCC (27). Therefore, the identification of specific biomarkers to aid diagnosis of RCC and effective therapeutic targets is needed. In the present study, the GSE40435 dataset, containing 101 pairs of tumors and adjacent healthy tissue, was analyzed. Although the number of samples included in the present study is limited, the data mining performed may be valuable. In gastric cancer, Sun et al (28) utilized the gene expression profile of GSE54129 containing only 111 gastric cancer samples and 21 healthy gastric mucosa epithelium. Chen et al (29) used bioinformatic analysis to demonstrate that the COP9 signalosome subunit 7B may be a prognostic marker and therapeutic target in renal cancer. Among differentially expressed genes, Wan et al (30) identified Aurora kinase B as a marker for predicting the prognosis of renal cancer. In the present study, the prognostic value of GLDC and ENO2 in RCC were assessed and it was hypothesized that they may be associated with cancer and carcinogenicity. DEGs were identified using bioinformatics and the 15 most important genes among these were determined using PPI network analysis. In these 15 genes, altered expression levels of GLDC and ENO2 exerted a significant effect on the prognosis of patients with renal cancer. Finally, GSEA was used to identify the BPs which may have been affected by altered expression levels of GLDC and ENO2. An understanding of the biological mechanisms regulated by GLDC and ENO2 in RCC may have research value.

GLDC, also termed glycine cleavage system P protein and HYGN1, is involved in nonketotic hyperglycinemia (31,32). Diseases associated with GLDC include glycine encephalopathy and neonatal glycine encephalopathy (33). Metabolism, glyoxylate metabolism and glycine degradation are among the pathways related to GLDC. GLDC has been shown to serve an important role in certain types of cancer, such as gastric cancer (34), breast cancer (35) and lung cancer (36); however, to the best of our knowledge, there has been no research investigating the relationship between GLDC and RCC. In lung cancer, GLDC is an essential oncogene promoting tumorigenesis through its metabolic action (37). In gastric cancer, GLDC promoter hypermethylation regulates transcriptional silencing inhibiting tumor development, whereas GLDC was reported as a putative tumor suppressor gene involved in tumor progression (34). The present study revealed that GLDC was downregulated in RCC and associated with poor prognosis, consistent with the results observed in gastric cancer. The result of the present study suggested that GLDC may be a tumor suppressor gene in RCC, supported by the fact that high GLDC expression levels inhibited proliferation and promoted apoptosis in RCC. Therefore, GLDC may serve an important role in the diagnosis and therapy of RCC.

To date, three enolase isoenzymes have been identified in mammals, including ENO2. Pathways related to ENO2 are metabolism and hypoxia-inducible factor-1 signaling pathway (38). ENO2 participates in glycolysis and promotes the conversion of b-glycerophosphate to dihydroxyacetone phosphate (39,40). In a previous study, ENO2 was shown to be a pro-survival factor in renal cancer (41). However, to the best of our knowledge, the underlying mechanism of this action has not been clarified. In acute lymphoblastic leukemia, ENO2 was reported to promote cell proliferation, glycolysis and glucocorticoid resistance (42). The mechanism of ENO2 was initially demonstrated in the present study, also demonstrating that high ENO2 expression may be a promising prognostic and diagnostic marker for patients with RCC.

DEGs are candidate diagnostic markers of RCC, with some well-established genes, such as EGFR and VEGF already associated with this disease. The EGFR signaling pathway serves an important role in physiological processes, such as cell growth, proliferation and differentiation. Regulation of the EGFR signaling pathway controls the growth and progression of renal cancer (43). VEGF promotes vascular permeability, extracellular matrix degeneration, vascular endothelial cell migration, proliferation and angiogenesis. Studies have shown that VEGF has a significant impact on the epithelial-mesenchymal transition process in kidney cancer (44,45). UMOD gene is associated with more aggressive clinical and pathological characteristics in RCC (46). Carbonic anhydrase 9 (CA9) is a gene from a family of carbonic anhydrases shown to be a biomarker of ccRCC (47,48), with previous studies showing that CA9 is overexpressed in RCC (49) and its value as a diagnostic or prognostic marker of ccRCC (50). Pregnancy upregulated nonubiquitous CaM kinase (PNCK) belongs to the calmodulin kinase I family and participates in cytoplasmic and nuclear signal transduction (51). A previous study indicated that overexpression of PNCK could be used for the prognostic stratification of patients with primary ccRCC (52). Collectively, further exploration of the clinical value of these DEGs is warranted.

In the present study, 1,650 DEGs and 15 hub genes were identified as potential diagnostic or prognostic biomarkers of RCC. The present study identified several genes which had not been previously linked with RCC and provided evidence that these genes were associated with this disease. Further experiments are required to verify these results and to more accurately analyze the associations between these genes and RCC. Overall, the present study highlights potentially novel targets for more individualized treatment of patients with RCC.


The authors would like to thank Dr Huan Deng (Department of Gastroenterology, Renmin Hospital of Wuhan University) for his assistance in statistical analysis.


This study was supported by The Application and Basic Research Project of Wuhan City (grant no. 2018060401011321), The Wuhan Morning Light Plan of Youth Science and Technology (grant no. 2017050304010281), The Hubei Province Health and Family Planning Scientific Research Project (grant nos. WJ2017M025 and WJ2017Z005), The Natural Science Foundation of Hubei Province (grant no. 2016CFB114, 2017CFB181) and The Research Project of Wuhan University (grant no. 2042017kf0097).

Availability of data and materials

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

Authors' contributions

RL and LW performed the biochemical experiments and contributed to the preparation of the draft. XL collected the data and wrote the first draft of the paper. XW perforemd analysis of PPI network. RXG and NL performed the GO and KEGG analysis of hub genes. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.



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Li R, Wang L, Wang X, Geng RX, Li N and Liu XH: Identification of hub genes associated with outcome of clear cell renal cell carcinoma. Oncol Lett 19: 2846-2860, 2020
Li, R., Wang, L., Wang, X., Geng, R., Li, N., & Liu, X. (2020). Identification of hub genes associated with outcome of clear cell renal cell carcinoma. Oncology Letters, 19, 2846-2860.
Li, R., Wang, L., Wang, X., Geng, R., Li, N., Liu, X."Identification of hub genes associated with outcome of clear cell renal cell carcinoma". Oncology Letters 19.4 (2020): 2846-2860.
Li, R., Wang, L., Wang, X., Geng, R., Li, N., Liu, X."Identification of hub genes associated with outcome of clear cell renal cell carcinoma". Oncology Letters 19, no. 4 (2020): 2846-2860.