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Prognostic immune-related gene models for breast cancer: a pooled analysis

Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal b...

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Autores principales: Zhao, Jianli, Wang, Ying, Lao, Zengding, Liang, Siting, Hou, Jingyi, Yu, Yunfang, Yao, Herui, You, Na, Chen, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5602680/
https://www.ncbi.nlm.nih.gov/pubmed/28979134
http://dx.doi.org/10.2147/OTT.S144015
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author Zhao, Jianli
Wang, Ying
Lao, Zengding
Liang, Siting
Hou, Jingyi
Yu, Yunfang
Yao, Herui
You, Na
Chen, Kai
author_facet Zhao, Jianli
Wang, Ying
Lao, Zengding
Liang, Siting
Hou, Jingyi
Yu, Yunfang
Yao, Herui
You, Na
Chen, Kai
author_sort Zhao, Jianli
collection PubMed
description Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER−) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER− breast cancer models achieved overall C-indices of 0.62–0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER−, LN+, and LN− breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER− and LN+ tumors.
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spelling pubmed-56026802017-10-04 Prognostic immune-related gene models for breast cancer: a pooled analysis Zhao, Jianli Wang, Ying Lao, Zengding Liang, Siting Hou, Jingyi Yu, Yunfang Yao, Herui You, Na Chen, Kai Onco Targets Ther Original Research Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER−) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER− breast cancer models achieved overall C-indices of 0.62–0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER−, LN+, and LN− breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER− and LN+ tumors. Dove Medical Press 2017-09-11 /pmc/articles/PMC5602680/ /pubmed/28979134 http://dx.doi.org/10.2147/OTT.S144015 Text en © 2017 Zhao et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Zhao, Jianli
Wang, Ying
Lao, Zengding
Liang, Siting
Hou, Jingyi
Yu, Yunfang
Yao, Herui
You, Na
Chen, Kai
Prognostic immune-related gene models for breast cancer: a pooled analysis
title Prognostic immune-related gene models for breast cancer: a pooled analysis
title_full Prognostic immune-related gene models for breast cancer: a pooled analysis
title_fullStr Prognostic immune-related gene models for breast cancer: a pooled analysis
title_full_unstemmed Prognostic immune-related gene models for breast cancer: a pooled analysis
title_short Prognostic immune-related gene models for breast cancer: a pooled analysis
title_sort prognostic immune-related gene models for breast cancer: a pooled analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5602680/
https://www.ncbi.nlm.nih.gov/pubmed/28979134
http://dx.doi.org/10.2147/OTT.S144015
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