Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

癌症是一种异质性疾病，主要由调控网络中的异常基因扰动驱动。因此，从多个癌症网络中识别出常见的和特定的扰动基因是很有吸引力的。我们开发了一种综合网络医学方法来识别新的生物标志物并研究跨癌症类型的药物再利用。我们使用基于网络的方法对使用人类转录组和相互作用组数据重建的癌症特异性网络中的基因进行优先排序。优先基因显示出广泛的扰动和与其他高度扰动基因的强烈调节相互作用，表明它们对肿瘤发生和肿瘤进展的重要贡献，因此被视为癌症基因。检测到的癌基因在区分肿瘤与正常组织和预测癌症患者的生存时间方面表现出显着的性能。最后，我们开发了一种网络邻近方法来系统地筛选药物，并确定了数十种在多种癌症类型中具有可再利用潜力的候选药物。总之，我们展示了网络医学方法在多种癌症类型中识别新型生物标志物和可再利用药物的能力。我们还使数据和代码可以免费访问，以确保开发的计算工作流的可重复性和可重用性。我们展示了网络医学方法在多种癌症类型中识别新型生物标志物和可再利用药物的能力。我们还使数据和代码可以免费访问，以确保开发的计算工作流的可重复性和可重用性。我们展示了网络医学方法在多种癌症类型中识别新型生物标志物和可再利用药物的能力。我们还使数据和代码可以免费访问，以确保开发的计算工作流的可重复性和可重用性。