Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning - Sun, Peishuo; Wu, Ying; Yin, Chaoyi; Jiang, Hongyang; Xu, Ying; Sun, Huiyan (Frontiers in Genetics, 2022)
Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solved this problem via multi-classification of gene-expressions of cancer samples. Although these methods especially deep learning perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference of cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of a cancer, generating network feature for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applying to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping are biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer and individualized treatment plan selection.