Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.

Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated acti...

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Autores principales:	Sharon Nienyun Hsu, Erika Wong En Hui, Mengzhen Liu, Di Wu, Thomas A Hughes, James Smith
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/477059b12a244d7d89af8ed93665ed8c
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spelling	oai:doaj.org-article:477059b12a244d7d89af8ed93665ed8c2021-12-02T20:10:15ZRevealing nuclear receptor hub modules from Basal-like breast cancer expression networks.1932-620310.1371/journal.pone.0252901https://doaj.org/article/477059b12a244d7d89af8ed93665ed8c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252901https://doaj.org/toc/1932-6203Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.Sharon Nienyun HsuErika Wong En HuiMengzhen LiuDi WuThomas A HughesJames SmithPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252901 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sharon Nienyun Hsu Erika Wong En Hui Mengzhen Liu Di Wu Thomas A Hughes James Smith Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.
description	Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.
format	article
author	Sharon Nienyun Hsu Erika Wong En Hui Mengzhen Liu Di Wu Thomas A Hughes James Smith
author_facet	Sharon Nienyun Hsu Erika Wong En Hui Mengzhen Liu Di Wu Thomas A Hughes James Smith
author_sort	Sharon Nienyun Hsu
title	Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.
title_short	Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.
title_full	Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.
title_fullStr	Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.
title_full_unstemmed	Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.
title_sort	revealing nuclear receptor hub modules from basal-like breast cancer expression networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/477059b12a244d7d89af8ed93665ed8c
work_keys_str_mv	AT sharonnienyunhsu revealingnuclearreceptorhubmodulesfrombasallikebreastcancerexpressionnetworks AT erikawongenhui revealingnuclearreceptorhubmodulesfrombasallikebreastcancerexpressionnetworks AT mengzhenliu revealingnuclearreceptorhubmodulesfrombasallikebreastcancerexpressionnetworks AT diwu revealingnuclearreceptorhubmodulesfrombasallikebreastcancerexpressionnetworks AT thomasahughes revealingnuclearreceptorhubmodulesfrombasallikebreastcancerexpressionnetworks AT jamessmith revealingnuclearreceptorhubmodulesfrombasallikebreastcancerexpressionnetworks
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Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks.

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