In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

在这项研究中，我们将共表达网络分析的结果与人类相互作用组网络相结合，以预测新的假定疾病基因和模块。我们首先应用 SWITCH Miner (SWIM) 方法，该方法预测共表达网络中调节疾病状态转变的重要（开关）基因，然后将它们映射到人类蛋白质-蛋白质相互作用网络（PPI 或相互作用组）以预测新的疾病与疾病之间的关系（即基于 SWIM 的疾病组）。尽管最近评估了开关基因与观察到的表型的相关性，但它们在系统或网络水平上的表现构成了一个新的、有待探索的潜在迷人领域。通过量化相互作用组网络中开关基因与人类疾病之间的相互作用，我们发现与特定疾病相关的开关基因彼此之间的距离比网络中其他节点的距离更近，并且倾向于形成局部连接的子网络。这些子网络在相似疾病之间重叠，并且位于不同的邻域，具有不同的病理表型，这与众所周知的疾病基因的拓扑邻近特性一致。这些发现使我们能够证明基于 SWIM 的相关网络分析如何作为有效筛选潜在新疾病基因关联的有用工具。当与基于相互作用组的网络分析相结合时，它不仅可以识别新的候选疾病基因，还可以提供可检验的假设，通过这些假设来阐明人类疾病的分子基础并揭示看似不相关的疾病之间的共性。