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Integrative analysis of cancer driver genes in prostate adenocarcinoma

Large-scale genomics studies have identified recurrently mutated genes in the ETS gene family, including fusions and copy number variations (CNVs), which are involved in the development of prostate adenocarcinoma (PRAD). However, the aetiology of PRAD remains to be fully elucidated. In the present s...

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Autores principales: Zhao, Xin, Lei, Yi, Li, Ge, Cheng, Yong, Yang, Haifan, Xie, Libo, Long, Hao, Jiang, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423600/
https://www.ncbi.nlm.nih.gov/pubmed/30720096
http://dx.doi.org/10.3892/mmr.2019.9902
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author Zhao, Xin
Lei, Yi
Li, Ge
Cheng, Yong
Yang, Haifan
Xie, Libo
Long, Hao
Jiang, Rui
author_facet Zhao, Xin
Lei, Yi
Li, Ge
Cheng, Yong
Yang, Haifan
Xie, Libo
Long, Hao
Jiang, Rui
author_sort Zhao, Xin
collection PubMed
description Large-scale genomics studies have identified recurrently mutated genes in the ETS gene family, including fusions and copy number variations (CNVs), which are involved in the development of prostate adenocarcinoma (PRAD). However, the aetiology of PRAD remains to be fully elucidated. In the present study, 333 driver genes were identified using four computational tools: OncodriveFM, OncodriveCLUST, iCAGES and DrGaP. In addition, 32 driver pathways were identified using DrGaP. SPOP, TP53, SPTA1, AHNAK, HMCN1, ATM, FOXA1, CSMD3, LRP1B and FREM2 were the 10 most recurrently mutated genes in PRAD. ITGAL, TAGAP, SIGLEC10, RAC2 and ITGA4 were the five hub genes in the yellow module that were associated with the number of positive lymph nodes. Hierarchical clustering analysis of the 20 driver genes with the most frequent CNVs revealed three clusters of patients with PRAD. Cluster 3 tumours exhibited significantly higher numbers of positive lymph nodes, higher Gleason scores, more advanced cancer stages and poorer prognosis than cluster 1 and 2 tumours. A total of 48 genes were significantly associated with the number of positive lymph nodes, Gleason scores and pathologic stage in patients with PRAD. The identified set of cancer genes and pathways sheds light on the tumorigenesis of PRAD and creates avenues for the development of prognostic biomarkers and driver gene-targeted therapies in PRAD.
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spelling pubmed-64236002019-03-22 Integrative analysis of cancer driver genes in prostate adenocarcinoma Zhao, Xin Lei, Yi Li, Ge Cheng, Yong Yang, Haifan Xie, Libo Long, Hao Jiang, Rui Mol Med Rep Articles Large-scale genomics studies have identified recurrently mutated genes in the ETS gene family, including fusions and copy number variations (CNVs), which are involved in the development of prostate adenocarcinoma (PRAD). However, the aetiology of PRAD remains to be fully elucidated. In the present study, 333 driver genes were identified using four computational tools: OncodriveFM, OncodriveCLUST, iCAGES and DrGaP. In addition, 32 driver pathways were identified using DrGaP. SPOP, TP53, SPTA1, AHNAK, HMCN1, ATM, FOXA1, CSMD3, LRP1B and FREM2 were the 10 most recurrently mutated genes in PRAD. ITGAL, TAGAP, SIGLEC10, RAC2 and ITGA4 were the five hub genes in the yellow module that were associated with the number of positive lymph nodes. Hierarchical clustering analysis of the 20 driver genes with the most frequent CNVs revealed three clusters of patients with PRAD. Cluster 3 tumours exhibited significantly higher numbers of positive lymph nodes, higher Gleason scores, more advanced cancer stages and poorer prognosis than cluster 1 and 2 tumours. A total of 48 genes were significantly associated with the number of positive lymph nodes, Gleason scores and pathologic stage in patients with PRAD. The identified set of cancer genes and pathways sheds light on the tumorigenesis of PRAD and creates avenues for the development of prognostic biomarkers and driver gene-targeted therapies in PRAD. D.A. Spandidos 2019-04 2019-01-28 /pmc/articles/PMC6423600/ /pubmed/30720096 http://dx.doi.org/10.3892/mmr.2019.9902 Text en Copyright: © Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Zhao, Xin
Lei, Yi
Li, Ge
Cheng, Yong
Yang, Haifan
Xie, Libo
Long, Hao
Jiang, Rui
Integrative analysis of cancer driver genes in prostate adenocarcinoma
title Integrative analysis of cancer driver genes in prostate adenocarcinoma
title_full Integrative analysis of cancer driver genes in prostate adenocarcinoma
title_fullStr Integrative analysis of cancer driver genes in prostate adenocarcinoma
title_full_unstemmed Integrative analysis of cancer driver genes in prostate adenocarcinoma
title_short Integrative analysis of cancer driver genes in prostate adenocarcinoma
title_sort integrative analysis of cancer driver genes in prostate adenocarcinoma
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423600/
https://www.ncbi.nlm.nih.gov/pubmed/30720096
http://dx.doi.org/10.3892/mmr.2019.9902
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