Multi-Affinity Network integration based on multi-omics data for tumor Stratification

Abstract

Abstract Tumor stratification facilitates clinical applications such as diagnosis and targeted treatment of patients. Sufficient multi-omics data have facilitated the study of tumor stratification, and many omics fusion methods have been proposed. However, most methods require that the omics data must contain the same samples. In this study, we propose a Multi-Affinity Network integration based on multi-omics data for tumor Stratification, call MANS. MANS addresses the limitation that omics data fusion must contain identical samples. Another novelty is that the subdivision of a single cancer type into a corresponding cancer subtype is unsupervised. Firstly, MANS constructs affinity networks based on the calculated similarity matrices between genes. Then we integrate multi-omics information by performing biased random walks in multiple affinity networks to obtain the neighborhood relationships of genes. Finally, the patient feature is constructed by using the somatic mutation profile. We classify the pan-cancer by lightGBM algorithm with an AUC value of approximately 0.94. The cancer is further subdivided into subtypes by unsupervised clustering algorithm. Among the 12 cancer types, MANS identifies significant differences in patient survival for subtypes of 10 cancer types. In conclusion, MANS is a potent precision oncology tool.