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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:Background Clear 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. Methods Transcriptome 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. Results In 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. Conclusions The 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:Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumor. Tumor recurrence and metastasis is the primary cause of cancer‑associated mortality in patients with ccRCC. Therefore, identification of efficient diagnostic and prognostic molecular markers may improve survival times. The GSE46699, GSE36895, GSE53000 and GSE53757 gene datasets were downloaded from the Gene Expression Omnibus database and contained 196 ccRCC samples and 164 adjacent normal kidney samples. Bioinformatics analysis was used to integrate the four microarray datasets to identify and analyze differentially expressed genes. Functional analysis revealed that there were 12 genes associated with cancer, based on the tumor‑associated gene database. Erb‑B2 receptor tyrosine kinase 4, centrosomal protein 55 (CEP55) and vascular endothelial growth factor A are oncogenes, all of which were associated with tumor stage, whereas only CEP55 was significantly associated with survival time as determined by Gene Expression Profiling Interactive Analysis. The mRNA expression levels of CEP55 in ccRCC samples were significantly higher than those observed in adjacent normal kidney tissues based on The Cancer Genome Atlas data and reverse transcription‑polymerase chain reaction results. The receiver operating characteristic curve analysis revealed that CEP55 may be considered a diagnostic biomarker for ccRCC with an area under the curve of >0.85 in the training and validation sets. High CEP55 expression was strongly associated with sex, histological grade, stage, T classification, N classification and M classification. Univariate and multivariate Cox proportional hazards analyses demonstrated that CEP55 expression was an independent risk factor for poor prognosis. In addition, gene set enrichment analysis indicated that high CEP55 expression was associated with immunization, cell adhesion, inflammation, the Janus kinase/signal transducer and activator of transcription signaling pathway and cell proliferation. In conclusion, CEP55 was increased in ccRCC samples, and may be considered a potential diagnostic and prognostic biomarker for ccRCC.

Project description:Genetic alterations in lipid metabolism genes are correlated with progression and poor prognosis of Clear cell renal cell carcinoma (ccRCC). PPARα play a critical role in lipid metabolism. This study aimed to identify that PPARα is a diagnosis and prognostic biomarker in ccRCC by integrated bioinformatics analysis. UALCAN database was used to explore the differential expression status and prognostic value of PPARα gene in various tumor types, qRT-PCR and immunohistochemical staining experiments were utilized for validation. Next, ccRCC data were obtained from TCGA. Correlation between PPARα expression levels and patients' clinicopathological characteristics was assessed, and the clinically diagnosis and prognostic value of PPARα were explored in ccRCC. According to the gene set enrichment analysis (GSEA) analysis, PPARα gene associated biological pathways were identified. PPARα has prognostic significance only in ccRCC tumors. Expression of PPARα was associated with ccRCC stages. PPARα was significantly down-regulated in ccRCC and associated with survival. Gender, tumor dimension, grade and stage showed a significant relevance with PPARα expression. Lower PPARα expression revealed significantly poorer survival and progression compared with higher PPARα expression. Adjusted by other clinical risk factors, PPARα remained an independent prognostic factor. Moreover, Low PPARα expression was a potential diagnostic biomarker of ccRCC. A nomogram was constructed based on PPARα expression and other clinicopathological risk factors, and it performed well in predict patients survival. GSEA analysis showed that PPARα gene associated biological pathways were enriched in mTOR pathway, AKT pathway, IGF1-mTOR pathway and Wnt signaling pathways. In conclusion, PPARα expression was decreased in ccRCC tumors. Lower expression of PPARα is closely correlated with poorer survival. It can be used as a clinically diagnosis and prognostic biomarker in ccRCC.

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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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