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Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer
High-grade serous ovarian carcinoma (HGS-OvCa) has the lowest survival rate among all gynecologic cancers and is hallmarked by a high degree of heterogeneity. The Cancer Genome Atlas network has described a gene expression-based molecular classification of HGS-OvCa into Differentiated, Mesenchymal,...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632004/ https://www.ncbi.nlm.nih.gov/pubmed/26530441 http://dx.doi.org/10.1038/srep16066 |
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author | Zhang, Shengzhe Jing, Ying Zhang, Meiying Zhang, Zhenfeng Ma, Pengfei Peng, Huixin Shi, Kaixuan Gao, Wei-Qiang Zhuang, Guanglei |
author_facet | Zhang, Shengzhe Jing, Ying Zhang, Meiying Zhang, Zhenfeng Ma, Pengfei Peng, Huixin Shi, Kaixuan Gao, Wei-Qiang Zhuang, Guanglei |
author_sort | Zhang, Shengzhe |
collection | PubMed |
description | High-grade serous ovarian carcinoma (HGS-OvCa) has the lowest survival rate among all gynecologic cancers and is hallmarked by a high degree of heterogeneity. The Cancer Genome Atlas network has described a gene expression-based molecular classification of HGS-OvCa into Differentiated, Mesenchymal, Immunoreactive and Proliferative subtypes. However, the biological underpinnings and regulatory mechanisms underlying the distinct molecular subtypes are largely unknown. Here we showed that tumor-infiltrating stromal cells significantly contributed to the assignments of Mesenchymal and Immunoreactive clusters. Using reverse engineering and an unbiased interrogation of subtype regulatory networks, we identified the transcriptional modules containing master regulators that drive gene expression of Mesenchymal and Immunoreactive HGS-OvCa. Mesenchymal master regulators were associated with poor prognosis, while Immunoreactive master regulators positively correlated with overall survival. Meta-analysis of 749 HGS-OvCa expression profiles confirmed that master regulators as a prognostic signature were able to predict patient outcome. Our data unraveled master regulatory programs of HGS-OvCa subtypes with prognostic and potentially therapeutic relevance, and suggested that the unique transcriptional and clinical characteristics of ovarian Mesenchymal and Immunoreactive subtypes could be, at least partially, ascribed to tumor microenvironment. |
format | Online Article Text |
id | pubmed-4632004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-46320042015-12-07 Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer Zhang, Shengzhe Jing, Ying Zhang, Meiying Zhang, Zhenfeng Ma, Pengfei Peng, Huixin Shi, Kaixuan Gao, Wei-Qiang Zhuang, Guanglei Sci Rep Article High-grade serous ovarian carcinoma (HGS-OvCa) has the lowest survival rate among all gynecologic cancers and is hallmarked by a high degree of heterogeneity. The Cancer Genome Atlas network has described a gene expression-based molecular classification of HGS-OvCa into Differentiated, Mesenchymal, Immunoreactive and Proliferative subtypes. However, the biological underpinnings and regulatory mechanisms underlying the distinct molecular subtypes are largely unknown. Here we showed that tumor-infiltrating stromal cells significantly contributed to the assignments of Mesenchymal and Immunoreactive clusters. Using reverse engineering and an unbiased interrogation of subtype regulatory networks, we identified the transcriptional modules containing master regulators that drive gene expression of Mesenchymal and Immunoreactive HGS-OvCa. Mesenchymal master regulators were associated with poor prognosis, while Immunoreactive master regulators positively correlated with overall survival. Meta-analysis of 749 HGS-OvCa expression profiles confirmed that master regulators as a prognostic signature were able to predict patient outcome. Our data unraveled master regulatory programs of HGS-OvCa subtypes with prognostic and potentially therapeutic relevance, and suggested that the unique transcriptional and clinical characteristics of ovarian Mesenchymal and Immunoreactive subtypes could be, at least partially, ascribed to tumor microenvironment. Nature Publishing Group 2015-11-04 /pmc/articles/PMC4632004/ /pubmed/26530441 http://dx.doi.org/10.1038/srep16066 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Zhang, Shengzhe Jing, Ying Zhang, Meiying Zhang, Zhenfeng Ma, Pengfei Peng, Huixin Shi, Kaixuan Gao, Wei-Qiang Zhuang, Guanglei Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer |
title | Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer |
title_full | Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer |
title_fullStr | Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer |
title_full_unstemmed | Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer |
title_short | Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer |
title_sort | stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632004/ https://www.ncbi.nlm.nih.gov/pubmed/26530441 http://dx.doi.org/10.1038/srep16066 |
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