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Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis.

ABSTRACT: Background:Clear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of kidney with high mortality. The pathogenesis of ccRCC is complicated and effective prognostic predictors for clinical practice are still limited. This study aimed to identify significant genes with prognostic influence in ccRCC via bioinformatics analysis. Methods:Four gene expression profiles were acquired from the Gene Expression Omnibus (GEO) database, including 168 ccRCC tissues and 143 normal tissues. Common differentially expressed genes (DEGs) between ccRCC tissues and normal kidney tissues were screened out. Then gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were investigated. Protein-protein interaction (PPI) network of the common DEGs was diagrammed and analyzed. Kaplan-Meier analysis was conducted to identify genes with prognostic influence in ccRCC. Gene Expression Profiling Interactive Analysis (GEPIA) was finally applied to validating differential expression of genes. Results:Ninety-nine common DEGs between ccRCC tissues and normal kidney tissues were eventually screened out (P<0.05, |log FC| >2). GO functional analysis showed that the down-regulated genes were enriched in excretion, negative regulation of cell proliferation, heparin binding and cellular response to BMP stimulus, etc. KEGG pathway analysis indicated that the common DEGs were particularly enriched in HIF-1 signaling pathway and aldosterone-regulated sodium reabsorption. Seven core DEGs were distinguished through PPI network analysis, of which 6 core genes ANGPTL4, CA9, CXCR4, LOX, EGF and HRG showed significantly prognostic difference in patients with ccRCC by Kaplan-Meier analysis (P<0.05). And GEPIA confirmed these genes were expressed differentially between tumor and normal tissues (P<0.05). High expression of HRG was correlated with good OS in ccRCC patients. Specifically, HRG was commonly down-regulated in ccRCC tissues compared with normal tissues according to GEPIA. Conclusions:Our study shows that high expression of HRG denotes a better prognosis in ccRCC patients. HRG is down-regulated in ccRCC tissues compared with normal kidney tissues. The selective expression pattern suggests that HRG could be a novel prognostic predictor and potential therapeutic target for ccRCC patients.

SUBMITTER: Zhang F

PROVIDER: S-EPMC7215011 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis.

Zhang Fangyuan F Wu Pengjie P Wang Yalong Y Zhang Mengxian M Wang Xiaodan X Wang Ting T Li Shengwen S Wei Dong D

Translational andrology and urology 20200401 2

<h4>Background</h4>Clear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of kidney with high mortality. The pathogenesis of ccRCC is complicated and effective prognostic predictors for clinical practice are still limited. This study aimed to identify significant genes with prognostic influence in ccRCC via bioinformatics analysis.<h4>Methods</h4>Four gene expression profiles were acquired from the Gene Expression Omnibus (GEO) database, including 168 ccRCC tissues and 143 no ...[more]

PMID: 32420151

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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.MethodsTranscriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.ResultsIn the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.ConclusionsThe present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

Project description:BackgroundRenal cell carcinoma (RCC) is one of the ten most prevalent cancers in the world and its incidence has been rising over the past decade. However, effective biomarkers to predict the prognosis of patients remains absent, and the exact molecular mechanism of the disease remains unclear. Therefore, the identification of key genes and their biological pathways are of great significance to identify the differential expressed genes associated with the prognosis for patients with RCC, and to further explore their potential protein-protein interactions (PPIs) in tumorigenesis.MethodsThe gene expression microarray data for GSE15641 and GSE40435 were extracted from the Gene Expression Omnibus (GEO) database, including 150 primary tumors and their matched adjacent non-tumor tissues. Afterwards, gene expression for fold changes (FCs) and P value for tumor and non-tumor tissues were analyzed using online tool GEO2R. Gene expression with logFCs of greater than two combined with P value of lower than 0.01 were considered as candidate targets for treatment of RCC. The survival analysis of candidate genes was performed by online software OncoLnc. The PPI network was implemented with Search Tool for the Retrieval of Interacting Genes (STRING).ResultsIn total, there were 625 differentially expressed genes (DEGs) in GSE15641, including 415 increased and 210 decreased genes. A total of 343 DEGs were identified in the GSE40435 with 101 upregulated and 242 downregulated genes, the 20 genes with highest FC in high or low expression in each database were summarized. Five candidate genes were overlapped genes in the two GEO datasets. However, aldolase, fructose-bisphosphate B (ALDOB) was found to be the only gene affecting the prognosis. A number of critical genes were identified behind the mechanism, of which they interacted with ALDOB. Among them, phosphofructokinase, platelet (PFKP), phosphofructokinase, muscle (PFKM), pyruvate kinase L/R (PKLR), and fructose-bisphosphatase 1 (FBP1) showed a better prognosis, whereas only glyceraldehyde-3-phosphate dehydrogenase (GAPDH) rendered a bleak outcome.ConclusionsFive genes were found to be overlappingly expressed in the top 20 greatest FC in two human GEO datasets. This is of great value in the treatment and prognosis of RCC.

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Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis.

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Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis.

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