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

Abstract

Background: With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data such as the cancer genome atlas ICGC and TCGA, it gains increasing importance how to detect the functional high-frequency mutation gene set in cells that causes cancer within the field of medicine.Methods: In this study, to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules, we propose a new recognition method of driver modules, named ECSWalk, based on the human protein interaction networks and pan-cancer somatic mutation data. This study firstly 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. Secondly, 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 reasonably split using the way of the induced subgraph, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules.Results: This method is applied to the analysis of 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.Conclusions: ECSWalk is of theoretical guidance and practical value for cancer diagnosis, treatment and drug targets.

Identifying driver modules based on multi‐omics biological networks in prostate cancer

“…The results of applying the MCODE, ClusterONE, HC‐PIN, OH‐PIN and NetAP methods to prostate cancer data from TCGA are shown in Table 4 , where the effective modules are driver modules with p ‐value<0.05 and the module size is ≥ 3 [ 27 , 28 ]. It can be seen from Table 4 that both the HC‐PIN and NetAP methods identify more candidate driver genes than the other three methods, which indicates that the HC‐PIN and NetAP methods have a better ability to capture driver genes.…”

“…Previous studies have shown that the driver modules have two key characteristics, namely high coverage and high mutual exclusivity [ 14 ], which have been widely used in carcinogenic driver module identification [ 25 , 26 , 27 , 28 ]. The definitions of coverage and mutual exclusivity are described in this section.…”

“…Therefore, the influence of gene nodes on biological functions can be analysed by evaluating the similarity between adjacent nodes in the local area network. In this paper, the gene node similarity metrics are constructed utilising the Jensen-Shannon (JS) divergence [29,30], where the JS divergence is calculated based on the discrete probability set [28,31]. Firstly, a local area network with the gene node as the centre and the one-step range of the centre node as the radius is constructed.…”

A feature extraction framework for discovering pan‐cancer driver genes based on multi‐omics data

“…Additionally, while MVKTS currently concatenates features extracted by various methods, integrating advanced gene feature fusion techniques to combine four types of omics data could potentially enhance the identification of pan-cancer driver genes [33][34][35]. The application of weighted fusion can also be considered to further improve the performance of MVKTS [36,37].…”