Identification of Pathway-Based Biomarkers with Crosstalk Analysis for Overall Survival Risk Prediction in Breast Cancer - Liu, Xiaohua; Su, Lili; Li, Jingcong; Ou, Guoping (Frontiers in Genetics, 2021)
Background: Recently, many studies have investigated the role of single-gene lists on the prognostic assessment of breast cancer, but the poor stability and reliability on independent datasets have limited the clinical usage of these models. Pathway-based approaches have thus appeared, which implanted the biological nature of cancers to generate reproducible features. Materials and Methods: Based on the expression profile, we could estimate pathways' deregulation scores (PDSs) to reflect the extent of pathway deregulation with crosstalk accommodated, aiming to identify pathway-based features to predict the overall survival in breast cancer. Results: With the cohort (N = 1090) of breast cancer patients from The Cancer Genome Atlas (TCGA), and based on the 100 new features, we conducted K-means clustering analysis to cluster the patients into two subgroups (G1: group 1, and G2: aggressive group 2). PDSs were robust and accurate in predicting the prognosis of breast cancer patients indicated by cross-validation. When validated on six external breast cancer datasets, we obtained consistent and favorable results. We also revealed significant differences between G2 and G1 subgroups on pathway, gene mutation, immune cell infiltration levels, and found that immune cells/pathways' activities were significantly negatively associated with breast cancer patients' outcomes. Conclusion: We establish a novel disease-specific prognostic classification system and successfully validate its clinical usage on multiple breast cancer datasets. These findings offer clinicians inspiration in formulating the clinical treatment plan.