Multi-omic integrated curvature study on pan-cancer genomic data

In this work, we introduce a new mathematical framework based on network curvature to extract significant cancer subtypes from multi-omics data. This extends our previous work that was based on analyzing a fixed single-omics data class (e.g., CNA, gene expression, methylation, etc.). Notably, we are able to show that this new methodology provided us with significant survival differences on Kaplan–Meier curves across almost every cancer considered. Moreover, the variances in Ollivier–Ricci curvature were explored to investigate its usefulness in network geometry analysis as this curvature has the potential to capture subtle functional changes between various cancer subtypes.