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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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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. |
format | Online Article Text |
id | pubmed-6503072 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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