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Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer

Ovarian cancer is a heterogeneous malignancy that poses tremendous clinical challenge. Based on unsupervised classification of whole-genome gene expression profiles, four molecular subtypes of ovarian cancer were recently identified. However, single-driver molecular events specific to these subtypes...

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Autores principales: Zhao, Linjie, Wang, Wei, Xu, Lian, Yi, Tao, Zhao, Xia, Wei, Yuquan, Vermeulen, Louis, Goel, Ajay, Zhou, Shengtao, Wang, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755993/
https://www.ncbi.nlm.nih.gov/pubmed/30478449
http://dx.doi.org/10.1038/s41388-018-0577-5
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author Zhao, Linjie
Wang, Wei
Xu, Lian
Yi, Tao
Zhao, Xia
Wei, Yuquan
Vermeulen, Louis
Goel, Ajay
Zhou, Shengtao
Wang, Xin
author_facet Zhao, Linjie
Wang, Wei
Xu, Lian
Yi, Tao
Zhao, Xia
Wei, Yuquan
Vermeulen, Louis
Goel, Ajay
Zhou, Shengtao
Wang, Xin
author_sort Zhao, Linjie
collection PubMed
description Ovarian cancer is a heterogeneous malignancy that poses tremendous clinical challenge. Based on unsupervised classification of whole-genome gene expression profiles, four molecular subtypes of ovarian cancer were recently identified. However, single-driver molecular events specific to these subtypes have not been clearly elucidated. We aim to characterize the regulatory mechanisms underlying the poor prognosis mesenchymal subtype of ovarian cancer using a systems biology approach, involving a variety of molecular modalities including gene and microRNA expression profiles. miR-508-3p emerged as the most powerful determinant that regulates a cascade of dysregulated genes in the mesenchymal subtype, including core genes involved in epithelial–mesenchymal transition (EMT) program. Moreover, miR-508-3p down-regulation, due to promoter hypermethylation, was directly correlated with metastatic behaviors in vitro and in vivo. Taken together, our multidimensional network analysis identified miR-508-3p as a master regulator that defines the mesenchymal subtype and provides a novel prognostic biomarker to improve management of this disease.
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spelling pubmed-67559932019-09-24 Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer Zhao, Linjie Wang, Wei Xu, Lian Yi, Tao Zhao, Xia Wei, Yuquan Vermeulen, Louis Goel, Ajay Zhou, Shengtao Wang, Xin Oncogene Article Ovarian cancer is a heterogeneous malignancy that poses tremendous clinical challenge. Based on unsupervised classification of whole-genome gene expression profiles, four molecular subtypes of ovarian cancer were recently identified. However, single-driver molecular events specific to these subtypes have not been clearly elucidated. We aim to characterize the regulatory mechanisms underlying the poor prognosis mesenchymal subtype of ovarian cancer using a systems biology approach, involving a variety of molecular modalities including gene and microRNA expression profiles. miR-508-3p emerged as the most powerful determinant that regulates a cascade of dysregulated genes in the mesenchymal subtype, including core genes involved in epithelial–mesenchymal transition (EMT) program. Moreover, miR-508-3p down-regulation, due to promoter hypermethylation, was directly correlated with metastatic behaviors in vitro and in vivo. Taken together, our multidimensional network analysis identified miR-508-3p as a master regulator that defines the mesenchymal subtype and provides a novel prognostic biomarker to improve management of this disease. Nature Publishing Group UK 2018-11-26 2019 /pmc/articles/PMC6755993/ /pubmed/30478449 http://dx.doi.org/10.1038/s41388-018-0577-5 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhao, Linjie
Wang, Wei
Xu, Lian
Yi, Tao
Zhao, Xia
Wei, Yuquan
Vermeulen, Louis
Goel, Ajay
Zhou, Shengtao
Wang, Xin
Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer
title Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer
title_full Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer
title_fullStr Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer
title_full_unstemmed Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer
title_short Integrative network biology analysis identifies miR-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer
title_sort integrative network biology analysis identifies mir-508-3p as the determinant for the mesenchymal identity and a strong prognostic biomarker of ovarian cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755993/
https://www.ncbi.nlm.nih.gov/pubmed/30478449
http://dx.doi.org/10.1038/s41388-018-0577-5
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