In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population

Aberrant transcriptional activities have been documented in breast cancers. Studies often find some transcription factors to be inappropriately regulated and enriched in certain pathological states. The promoter regions of most target genes have binding sites for their transcription factors. An ampl...

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Main Authors:	Liu, Li-Yu D, Chang, Li-Yun, Kuo, Wen-Hung, Hwa, Hsiao-Lin, Shyu, Ming-Kwang, Chang, King-Jen, Hsieh, Fon-Jou
Format:	Online
Language:	English
Published:	Libertas Academica 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337786/

id	pubmed-3337786
recordtype	oai_dc
spelling	pubmed-33377862012-05-02 In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population Liu, Li-Yu D Chang, Li-Yun Kuo, Wen-Hung Hwa, Hsiao-Lin Shyu, Ming-Kwang Chang, King-Jen Hsieh, Fon-Jou Methodology Aberrant transcriptional activities have been documented in breast cancers. Studies often find some transcription factors to be inappropriately regulated and enriched in certain pathological states. The promoter regions of most target genes have binding sites for their transcription factors. An ample of evidence supports their combinatorial effect on their shared target gene expressions. Here, we used a new statistic method, bivariate CID, to predict combinatorial interaction activity between ERα and a transcription factor (E2F1or GATA3 or ERRα) in regulating target gene expression via four regulatory mechanisms. We identified gene sets in three signal transduction pathways perturbed in breast tumors: cell cycle, VEGF, and PDGFRB. Bivariate network analysis revealed several target genes previously implicated in tumor angiogenesis are among the predicted shared targets, including VEGFA, PDGFRB. In summary, our analysis suggests the importance for the multivariate space of an inferred ERα transcriptional regulatory network in breast cancer diagnostic and therapeutic development. Libertas Academica 2012-04-19 /pmc/articles/PMC3337786/ /pubmed/22553415 http://dx.doi.org/10.4137/CIN.S8470 Text en © the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Liu, Li-Yu D Chang, Li-Yun Kuo, Wen-Hung Hwa, Hsiao-Lin Shyu, Ming-Kwang Chang, King-Jen Hsieh, Fon-Jou
spellingShingle	Liu, Li-Yu D Chang, Li-Yun Kuo, Wen-Hung Hwa, Hsiao-Lin Shyu, Ming-Kwang Chang, King-Jen Hsieh, Fon-Jou In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population
author_facet	Liu, Li-Yu D Chang, Li-Yun Kuo, Wen-Hung Hwa, Hsiao-Lin Shyu, Ming-Kwang Chang, King-Jen Hsieh, Fon-Jou
author_sort	Liu, Li-Yu D
title	In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population
title_short	In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population
title_full	In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population
title_fullStr	In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population
title_full_unstemmed	In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population
title_sort	in silico prediction for regulation of transcription factors ontheir shared target genes indicates relevant clinical implications in a breast cancer population
description	Aberrant transcriptional activities have been documented in breast cancers. Studies often find some transcription factors to be inappropriately regulated and enriched in certain pathological states. The promoter regions of most target genes have binding sites for their transcription factors. An ample of evidence supports their combinatorial effect on their shared target gene expressions. Here, we used a new statistic method, bivariate CID, to predict combinatorial interaction activity between ERα and a transcription factor (E2F1or GATA3 or ERRα) in regulating target gene expression via four regulatory mechanisms. We identified gene sets in three signal transduction pathways perturbed in breast tumors: cell cycle, VEGF, and PDGFRB. Bivariate network analysis revealed several target genes previously implicated in tumor angiogenesis are among the predicted shared targets, including VEGFA, PDGFRB. In summary, our analysis suggests the importance for the multivariate space of an inferred ERα transcriptional regulatory network in breast cancer diagnostic and therapeutic development.
publisher	Libertas Academica
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337786/
_version_	1611525129181855744

In Silico Prediction for Regulation of Transcription Factors onTheir Shared Target Genes Indicates Relevant Clinical Implications in a Breast Cancer Population

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