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.
Highlights
Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC)
In the entry model of multivariable logistic regression of five clinicalpathological factors (Supplementary Table 1), tumor size is the only significant factor related to BC recurrence that we included as a mandatory variable during modeling
In Models 2–4, we found some highly co-expressed with 34 candidate genes that statistically associated with recurrence, including Model 2—CCNA2, Model 3—CCNA2, IDUA, MGC27165, CCNE2, KIF14 and C10orf[56] and Model 4—IDUA, MGC27165, CCNE2, KIF14, EBP and RORC (Supplementary Table 4)
Summary
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 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. Understanding the underlying molecular mechanisms[2,6] and identifying novel genome profiles will aid in the development of therapies[7,8]. Microarray analyses of gene profiles offer potential prognostic information and identify differentially expressed genes (DEGs) for the prognosis of newly diagnosed B C9–14. We proposed GCN-based modeling to create better prediction models or gene panels of BC prognosis and explore novel biomarkers and putative functional pathways
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