Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Abstract Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various predicti...

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Autores principales:	Chi-Ming Chu, Huan-Ming Hsu, Chi-Wen Chang, Yuan-Kuei Li, Yu-Jia Chang, Jyh-Cherng Yu, Chien-Ting Chen, Chen-En Jian, Meng-Chiung Lin, Kang-Hua Chen, Ming-Hao Kuo, Chia-Shiang Cheng, Ya-Ting Chang, Yi-Syuan Wu, Hao-Yi Wu, Ya-Ting Yang, Je-Ming Hu, Yu-Tien Chang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f9335fe875d41c9aca44e5deee283de
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spelling	oai:doaj.org-article:2f9335fe875d41c9aca44e5deee283de2021-12-02T13:26:37ZGenetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer10.1038/s41598-021-84995-z2045-2322https://doaj.org/article/2f9335fe875d41c9aca44e5deee283de2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84995-zhttps://doaj.org/toc/2045-2322Abstract Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.Chi-Ming ChuHuan-Ming HsuChi-Wen ChangYuan-Kuei LiYu-Jia ChangJyh-Cherng YuChien-Ting ChenChen-En JianMeng-Chiung LinKang-Hua ChenMing-Hao KuoChia-Shiang ChengYa-Ting ChangYi-Syuan WuHao-Yi WuYa-Ting YangJe-Ming HuYu-Tien ChangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chi-Ming Chu Huan-Ming Hsu Chi-Wen Chang Yuan-Kuei Li Yu-Jia Chang Jyh-Cherng Yu Chien-Ting Chen Chen-En Jian Meng-Chiung Lin Kang-Hua Chen Ming-Hao Kuo Chia-Shiang Cheng Ya-Ting Chang Yi-Syuan Wu Hao-Yi Wu Ya-Ting Yang Je-Ming Hu Yu-Tien Chang Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer
description	Abstract Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.
format	article
author	Chi-Ming Chu Huan-Ming Hsu Chi-Wen Chang Yuan-Kuei Li Yu-Jia Chang Jyh-Cherng Yu Chien-Ting Chen Chen-En Jian Meng-Chiung Lin Kang-Hua Chen Ming-Hao Kuo Chia-Shiang Cheng Ya-Ting Chang Yi-Syuan Wu Hao-Yi Wu Ya-Ting Yang Je-Ming Hu Yu-Tien Chang
author_facet	Chi-Ming Chu Huan-Ming Hsu Chi-Wen Chang Yuan-Kuei Li Yu-Jia Chang Jyh-Cherng Yu Chien-Ting Chen Chen-En Jian Meng-Chiung Lin Kang-Hua Chen Ming-Hao Kuo Chia-Shiang Cheng Ya-Ting Chang Yi-Syuan Wu Hao-Yi Wu Ya-Ting Yang Je-Ming Hu Yu-Tien Chang
author_sort	Chi-Ming Chu
title	Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer
title_short	Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer
title_full	Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer
title_fullStr	Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer
title_full_unstemmed	Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer
title_sort	genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2f9335fe875d41c9aca44e5deee283de
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Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

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