癌症是一种复杂的遗传性疾病，其特征是各种突变的积累，驱动基因在癌症的发生和进展中发挥着至关重要的作用。区分驱动基因和乘客突变对于了解癌症生物学和发现治疗靶点至关重要。然而，大多数现有方法忽略了患者之间的突变异质性和共性，这阻碍了更有效地识别驱动基因。本研究引入了MCSdriver，这是一种新颖的计算模型，它集成了网络和通路信息，以优先识别癌症驱动基因。 MCSdriver 采用双向随机游走算法来量化患者群体内突变基因之间的相互排他性和功能关系。它根据相互排他性加权网络和通路覆盖模式计算相似性分数，考虑到患者特异性异质性和分子谱相似性。这种方法提高了驱动基因识别的准确性和质量。 MCSdriver 在识别癌症基因组图谱中四种癌症类型的癌症驱动基因方面表现出卓越的性能，与现有的基于排名列表和基于模块的模型相比，显示出更高的 F 分数、召回率和精确度。MCSdriver 模型不仅优于其他模型在识别已知的癌症驱动基因的同时，还可以有效地识别参与癌症相关生物过程的新型驱动基因。该模型考虑了患者特异性的异质性和分子谱的相似性，显着提高了驱动基因识别的准确性和质量。通过基因本体富集分析和文献挖掘进行的验证进一步强调了其在个性化癌症治疗中的潜在应用价值，为促进我们对癌症的理解和治疗提供了一个有前景的工具。版权所有 © 2024 Elsevier Inc. 保留所有权利。

Cancer is a complex genetic disease characterized by the accumulation of various mutations, with driver genes playing a crucial role in cancer initiation and progression. Distinguishing driver genes from passenger mutations is essential for understanding cancer biology and discovering therapeutic targets. However, the majority of existing methods ignore the mutational heterogeneity and commonalities among patients, which hinders the identification of driver genes more effectively.This study introduces MCSdriver, a novel computational model that integrates network and pathway information to prioritize the identification of cancer driver genes. MCSdriver employs a bidirectional random walk algorithm to quantify the mutual exclusivity and functional relationships between mutated genes within patient cohorts. It calculates similarity scores based on a mutual exclusivity-weighted network and pathway coverage patterns, accounting for patient-specific heterogeneity and molecular profile similarity.This approach enhances the accuracy and quality of driver gene identification. MCSdriver demonstrates superior performance in identifying cancer driver genes across four cancer types from The Cancer Genome Atlas, showing a higher F-score, Recall and Precision compared to existing ranking list-based and module-based models.The MCSdriver model not only outperforms other models in identifying known cancer driver genes but also effectively identifies novel driver genes involved in cancer-related biological processes. The model's consideration of patient-specific heterogeneity and similarity in molecular profiles significantly enhances the accuracy and quality of driver gene identification. Validation through Gene Ontology enrichment analysis and literature mining further underscores its potential application value in personalized cancer therapy, offering a promising tool for advancing our understanding and treatment of cancer.Copyright © 2024 Elsevier Inc. All rights reserved.