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Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis
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 i...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215011/ https://www.ncbi.nlm.nih.gov/pubmed/32420151 http://dx.doi.org/10.21037/tau.2020.02.11 |
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author | Zhang, Fangyuan Wu, Pengjie Wang, Yalong Zhang, Mengxian Wang, Xiaodan Wang, Ting Li, Shengwen Wei, Dong |
author_facet | Zhang, Fangyuan Wu, Pengjie Wang, Yalong Zhang, Mengxian Wang, Xiaodan Wang, Ting Li, Shengwen Wei, Dong |
author_sort | Zhang, Fangyuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7215011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-72150112020-05-15 Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis Zhang, Fangyuan Wu, Pengjie Wang, Yalong Zhang, Mengxian Wang, Xiaodan Wang, Ting Li, Shengwen Wei, Dong Transl Androl Urol Original Article 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. AME Publishing Company 2020-04 /pmc/articles/PMC7215011/ /pubmed/32420151 http://dx.doi.org/10.21037/tau.2020.02.11 Text en 2020 Translational Andrology and Urology. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Zhang, Fangyuan Wu, Pengjie Wang, Yalong Zhang, Mengxian Wang, Xiaodan Wang, Ting Li, Shengwen Wei, Dong Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis |
title | Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis |
title_full | Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis |
title_fullStr | Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis |
title_full_unstemmed | Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis |
title_short | Identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis |
title_sort | identification of significant genes with prognostic influence in clear cell renal cell carcinoma via bioinformatics analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215011/ https://www.ncbi.nlm.nih.gov/pubmed/32420151 http://dx.doi.org/10.21037/tau.2020.02.11 |
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