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.
背景：癌症驱动基因与关键分子通路的识别一直是大规模癌症基因组研究的核心。基于网络的方法通过整合基因组学数据与蛋白质相互作用（PPI）网络的拓扑信息，可检测显著扰动的子网络作为潜在的癌症通路。然而，常用PPI网络具有各异的拓扑结构，导致同一方法应用于不同网络时结果差异显著。此外，新兴的特定情境PPI网络常存在拓扑结构不完整的问题，这对现有子网络检测算法提出了严峻挑战。方法：本文提出一种名为MultiFDRnet的新方法以解决上述问题。其核心思想是将一组PPI网络建模为多层网络，在保留各网络拓扑结构的同时引入网络间关联性，进而利用所有结构信息在建模的多层网络上同步检测显著扰动的子网络。结果：为验证所提方法的有效性，我们在模拟数据与真实癌症数据上进行了系统性的基准分析。实验结果表明，该方法能够检测到多个PPI网络共同支持的显著扰动子网络，并在特定情境PPI网络中识别出新颖的模块化结构。
肿瘤(癌症)患者之家