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Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer, and more and more researches find that the occurrence of ccRCC is associated with genetic changes, but the molecular mechanism still remains unclear. The present study aimed to identify aggregation trend of differentially expressed genes (DEGs) in ccRCC, which would be beneficial to the treatment of ccRCC and provide research ideas using a series of bioinformatics approach. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis were used to get the enrichment trend of DEGs of GSE53757 and GSE16449. Draw Venn Diagram was applied for co-expression of DEGs. Cytoscape with the Retrieval of Interacting Gene (STRING) datasets and Molecular Complex Detection (MCODE) were performed protein-protein interaction (PPI) of DEGs. The Kaplan-Meier Plotter analysis of top 15 upregulated and top 15 downregulated were selected in Gene Expression Profiling Interactive Analysis (GEPIA). Then, the expression level of hub genes between normal renal tissue and different pathological stages of ccRCC tissue, which significantly correlated with overall survival in ccRCC patients, were also analyzed by Ualcan based on The Cancer Genome Atlas (TCGA) database. In this study, we got 167 co-expression DEGs, including 72 upregulated DEGs and 95 downregulated DEGs. We identified 11 hub genes had significantly correlated with overall survival in ccRCC patients. Among them, KIF23, APLN, ADCY1, GREB1, TLR4, IRF8, CXCL1, CXCL2, deserved our attention.

SUBMITTER: Zhang N

PROVIDER: S-EPMC7249934 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis.

Zhang Ning N Chen Wenxin W Gan Zhilu Z Abudurexiti Alimujiang A Hu Xiaogang X Sang Wei W

Medicine 20200501 21

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer, and more and more researches find that the occurrence of ccRCC is associated with genetic changes, but the molecular mechanism still remains unclear. The present study aimed to identify aggregation trend of differentially expressed genes (DEGs) in ccRCC, which would be beneficial to the treatment of ccRCC and provide research ideas using a series of bioinformatics approach. Gene ontology (GO) and Kyoto Encyclo ...[more]

PMID: 32481352

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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:The purpose of the present study was to screen potential pathogenic biomarkers of clear cell renal cell carcinoma (ccRCC) via microarray analysis. The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of HAPLN1. The mRNA levels of hsa-miR-204 and hsa-miR-218 were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. HAPLN1 was lowly expressed and highly methylated in ccRCC tissues, while hsa-miR-204 and hsa-miR-218 were highly expressed. The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.

Project description:BackgroundClear 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.MethodsFour 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.ResultsNinety-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.ConclusionsOur 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.

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Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis.

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Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis.

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