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The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve...

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Autores principales: Zhang, Bo, Wu, Qiong, Wang, Ziheng, Xu, Ran, Hu, Xinyi, Sun, Yidan, Wang, Qiuhong, Ju, Fei, Ren, Shiqi, Zhang, Chenlin, Qin, Lin, Ma, Qianqian, Zhou, You Lang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503072/
https://www.ncbi.nlm.nih.gov/pubmed/30793530
http://dx.doi.org/10.1002/mgg3.607
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author Zhang, Bo
Wu, Qiong
Wang, Ziheng
Xu, Ran
Hu, Xinyi
Sun, Yidan
Wang, Qiuhong
Ju, Fei
Ren, Shiqi
Zhang, Chenlin
Qin, Lin
Ma, Qianqian
Zhou, You Lang
author_facet Zhang, Bo
Wu, Qiong
Wang, Ziheng
Xu, Ran
Hu, Xinyi
Sun, Yidan
Wang, Qiuhong
Ju, Fei
Ren, Shiqi
Zhang, Chenlin
Qin, Lin
Ma, Qianqian
Zhou, You Lang
author_sort Zhang, Bo
collection PubMed
description BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC. METHODS: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC. RESULTS: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression. CONCLUSIONS: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.
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spelling pubmed-65030722019-05-10 The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data Zhang, Bo Wu, Qiong Wang, Ziheng Xu, Ran Hu, Xinyi Sun, Yidan Wang, Qiuhong Ju, Fei Ren, Shiqi Zhang, Chenlin Qin, Lin Ma, Qianqian Zhou, You Lang Mol Genet Genomic Med Original Articles BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC. METHODS: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC. RESULTS: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression. CONCLUSIONS: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment. John Wiley and Sons Inc. 2019-02-21 /pmc/articles/PMC6503072/ /pubmed/30793530 http://dx.doi.org/10.1002/mgg3.607 Text en © 2019 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Zhang, Bo
Wu, Qiong
Wang, Ziheng
Xu, Ran
Hu, Xinyi
Sun, Yidan
Wang, Qiuhong
Ju, Fei
Ren, Shiqi
Zhang, Chenlin
Qin, Lin
Ma, Qianqian
Zhou, You Lang
The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data
title The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data
title_full The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data
title_fullStr The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data
title_full_unstemmed The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data
title_short The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data
title_sort promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: evidence from bioinformatics analysis of high‐throughput data
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6503072/
https://www.ncbi.nlm.nih.gov/pubmed/30793530
http://dx.doi.org/10.1002/mgg3.607
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