Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1783264604525166592 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5602680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhaojianli prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT wangying prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT laozengding prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT liangsiting prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT houjingyi prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT yuyunfang prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT yaoherui prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT youna prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis AT chenkai prognosticimmunerelatedgenemodelsforbreastcancerapooledanalysis |