MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2023-12-09 DOI:10.1093/bioinformatics/btad737

Jian Liu, Fubin Ma, Yongdi Zhu, Naiqian Zhang, Lingming Kong, Jia Mi, Haiyan Cong, Rui Gao, Mingyi Wang, Yusen Zhang

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引用次数: 0

Abstract

Motivation Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. Results Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein–protein interaction (PPI) networks and further improved by an information entropy (IE) index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. Availability The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main. Supplementary information Supplementary data are available at Bioinformatics online.

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MaxCLK：通过模块的最大聚类和信息熵发现癌症驱动基因

动机癌症是由多种途径中的体细胞突变累积引起的，其中驱动基因突变在患者中通常具有高覆盖率和高排他性的特性。识别癌症驱动基因对于了解肿瘤发生和治疗机制具有举足轻重的作用。结果在这里，我们介绍了一种用于识别癌症驱动基因的算法--MaxCLK，它是通过对体细胞突变数据和蛋白-蛋白相互作用（PPI）网络的综合分析而开发的，并通过信息熵（IE）指数得到了进一步改进。通过对胰腺癌和单种癌症的测试，MaxCLK的准确性优于其他现有方法。关于胰腺癌，我们预测了 154 个驱动基因和 787 个驱动模块。模块和通路之间的共存性和排他性分析揭示了其组合的相关性。总之，我们的研究加深了对PPI拓扑中驱动机制的理解，并发现了新的驱动基因。可用性 MaxCLK 的源代码可在 https://github.com/ShandongUniversityMasterMa/MaxCLK-main 免费获取。补充信息补充数据可在 Bioinformatics online 上获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.