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Key regulators in prostate cancer identified by co-expression module analysis

BACKGROUND: Prostate cancer (PrCa) is the most commonly diagnosed cancer in men in the world. Despite the fact that a large number of its genes have been investigated, its etiology remains poorly understood. Furthermore, most PrCa candidate genes have not been rigorously replicated, and the methods...

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Detalles Bibliográficos
Autores principales: Jiang, Junfeng, Jia, Peilin, Zhao, Zhongming, Shen, Bairong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4258300/
https://www.ncbi.nlm.nih.gov/pubmed/25418933
http://dx.doi.org/10.1186/1471-2164-15-1015
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author Jiang, Junfeng
Jia, Peilin
Zhao, Zhongming
Shen, Bairong
author_facet Jiang, Junfeng
Jia, Peilin
Zhao, Zhongming
Shen, Bairong
author_sort Jiang, Junfeng
collection PubMed
description BACKGROUND: Prostate cancer (PrCa) is the most commonly diagnosed cancer in men in the world. Despite the fact that a large number of its genes have been investigated, its etiology remains poorly understood. Furthermore, most PrCa candidate genes have not been rigorously replicated, and the methods by which they biologically function in PrCa remain largely unknown. RESULTS: Aiming to identify key players in the complex prostate cancer system, we reconstructed PrCa co-expressed modules within functional gene sets defined by the Gene Ontology (GO) annotation (biological process, GO_BP). We primarily identified 118 GO_BP terms that were well-preserved between two independent gene expression datasets and a consequent 55 conserved co-expression modules within them. Five modules were then found to be significantly enriched with PrCa candidate genes collected from expression Quantitative Trait Loci (eQTL), somatic copy number alteration (SCNA), somatic mutation data, or prognostic analyses. Specifically, two transcription factors (TFs) (NFAT and SP1) and three microRNAs (hsa-miR-19a, hsa-miR-15a, and hsa-miR-200b) regulating these five candidate modules were found to be critical to the development of PrCa. CONCLUSIONS: Collectively, our results indicated that genes with similar functions may play important roles in disease through co-expression, and modules with different functions could be regulated by similar genetic components, such as TFs and microRNAs, in a synergistic manner. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1015) contains supplementary material, which is available to authorized users.
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spelling pubmed-42583002014-12-08 Key regulators in prostate cancer identified by co-expression module analysis Jiang, Junfeng Jia, Peilin Zhao, Zhongming Shen, Bairong BMC Genomics Research Article BACKGROUND: Prostate cancer (PrCa) is the most commonly diagnosed cancer in men in the world. Despite the fact that a large number of its genes have been investigated, its etiology remains poorly understood. Furthermore, most PrCa candidate genes have not been rigorously replicated, and the methods by which they biologically function in PrCa remain largely unknown. RESULTS: Aiming to identify key players in the complex prostate cancer system, we reconstructed PrCa co-expressed modules within functional gene sets defined by the Gene Ontology (GO) annotation (biological process, GO_BP). We primarily identified 118 GO_BP terms that were well-preserved between two independent gene expression datasets and a consequent 55 conserved co-expression modules within them. Five modules were then found to be significantly enriched with PrCa candidate genes collected from expression Quantitative Trait Loci (eQTL), somatic copy number alteration (SCNA), somatic mutation data, or prognostic analyses. Specifically, two transcription factors (TFs) (NFAT and SP1) and three microRNAs (hsa-miR-19a, hsa-miR-15a, and hsa-miR-200b) regulating these five candidate modules were found to be critical to the development of PrCa. CONCLUSIONS: Collectively, our results indicated that genes with similar functions may play important roles in disease through co-expression, and modules with different functions could be regulated by similar genetic components, such as TFs and microRNAs, in a synergistic manner. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1015) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-24 /pmc/articles/PMC4258300/ /pubmed/25418933 http://dx.doi.org/10.1186/1471-2164-15-1015 Text en © Jiang et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Jiang, Junfeng
Jia, Peilin
Zhao, Zhongming
Shen, Bairong
Key regulators in prostate cancer identified by co-expression module analysis
title Key regulators in prostate cancer identified by co-expression module analysis
title_full Key regulators in prostate cancer identified by co-expression module analysis
title_fullStr Key regulators in prostate cancer identified by co-expression module analysis
title_full_unstemmed Key regulators in prostate cancer identified by co-expression module analysis
title_short Key regulators in prostate cancer identified by co-expression module analysis
title_sort key regulators in prostate cancer identified by co-expression module analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4258300/
https://www.ncbi.nlm.nih.gov/pubmed/25418933
http://dx.doi.org/10.1186/1471-2164-15-1015
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