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

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number7268
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - Dec 2021

ASJC Scopus subject areas

  • General

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