Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein–Protein Interaction Networks

SIMPLE SUMMARY: Cancer research has been increasingly focusing on identifying genes and molecular pathways that drive the disease. In this context, protein–protein interaction networks, which provide insights into the interactions among proteins within a cell, have proven particularly useful. However, the effectiveness of existing approaches can be influenced by the specific network used, as different networks can have different topological structures. In addition, newer context-specific networks often come with incomplete structures, which complicates the analysis. To address these challenges, we propose a new method, called MultiFDRnet, that can identify driver genes and pathways using multiple protein–protein interaction (PPI) networks. Here, the false discovery rate (FDR) refers to the proportion of non-cancer genes within identified subnetworks. Our method, tested on both simulated and real cancer data, has been able to identify important subnetworks that are supported by multiple PPI networks and reveal novel modular structures in context-specific PPI networks. The software that we developed to implement this method is freely available for other researchers to use. ABSTRACT: Background: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. Methods: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. Results: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.