识别癌症驱动基因，特别是罕见或患者特异性的癌症驱动基因，是癌症治疗的主要目标。尽管研究人员提出了一些方法来解决这个问题，但这些方法大多在单基因水平上识别癌症驱动基因，忽视了癌症驱动基因之间的合作关系。识别个体患者中的协作癌症驱动基因对于了解癌症病因和推进个性化治疗的发展至关重要。在这里，我们提出了一种新颖的个性化协作癌症驱动基因（PCoDG）方法，通过使用超图随机游走来识别协作的癌症驱动基因推动个体患者的癌症进展。通过利用超图表示多向关系的强大能力，PCoDG首先采用个性化超图来描述个体患者的突变基因和差异表达基因之间的复杂相互作用。然后，利用基于超边相似性的超图随机游走算法来计算突变基因的重要性得分，将这些得分与信号通路数据相结合，以识别个体患者中协同的癌症驱动基因。在三个TCGA癌症数据集（即、BRCA、LUAD 和 COADREAD）证明了 PCoDG 在识别个性化协作癌症驱动基因方面的有效性。 PCoDG 鉴定的这些基因不仅为与临床结果相关的患者分层提供了宝贵的见解，而且还为定制个性化治疗提供了有用的参考资源。我们提出了一种新方法，可以有效地识别个体患者的协同癌症驱动基因，从而加深我们的研究了解个性化癌症驱动基因之间的合作关系，推动精准肿瘤学的发展。版权所有 © 2024 Elsevier Inc. 保留所有权利。

Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies.Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients.The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments.We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.Copyright © 2024 Elsevier Inc. All rights reserved.