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Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer

BACKGROUND: With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e....

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Autores principales: Chiu, Yu-Chiao, Wang, Li-Ju, Hsiao, Tzu-Hung, Chuang, Eric Y., Chen, Yidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629553/
https://www.ncbi.nlm.nih.gov/pubmed/28984209
http://dx.doi.org/10.1186/s12864-017-4028-4
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author Chiu, Yu-Chiao
Wang, Li-Ju
Hsiao, Tzu-Hung
Chuang, Eric Y.
Chen, Yidong
author_facet Chiu, Yu-Chiao
Wang, Li-Ju
Hsiao, Tzu-Hung
Chuang, Eric Y.
Chen, Yidong
author_sort Chiu, Yu-Chiao
collection PubMed
description BACKGROUND: With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. RESULTS: We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. CONCLUSIONS: Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.
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spelling pubmed-56295532017-10-13 Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer Chiu, Yu-Chiao Wang, Li-Ju Hsiao, Tzu-Hung Chuang, Eric Y. Chen, Yidong BMC Genomics Research BACKGROUND: With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. RESULTS: We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. CONCLUSIONS: Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations. BioMed Central 2017-10-03 /pmc/articles/PMC5629553/ /pubmed/28984209 http://dx.doi.org/10.1186/s12864-017-4028-4 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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
Chiu, Yu-Chiao
Wang, Li-Ju
Hsiao, Tzu-Hung
Chuang, Eric Y.
Chen, Yidong
Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer
title Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer
title_full Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer
title_fullStr Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer
title_full_unstemmed Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer
title_short Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer
title_sort genome-wide identification of key modulators of gene-gene interaction networks in breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629553/
https://www.ncbi.nlm.nih.gov/pubmed/28984209
http://dx.doi.org/10.1186/s12864-017-4028-4
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