Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data, such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), it gains increasing importance to detect the functional high-frequency mutation gene set in cells that causes cancer in the field of medicine. In this study, we propose a new recognition method of driver modules, named ECSWalk to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules detection, based on human protein–protein interaction networks and pan-cancer somatic mutation data. This study first utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Second, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are split using induced subgraph method, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules. This method is applied to TCGA pan-cancer data and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2. Thus, ECSWalk is of theoretical implication and practical value for cancer diagnosis, treatment and drug targets.