Identification of driver genes based on gene mutational effects and network centrality

BMC Bioinformatics

卷:22

Jian-ping Zhao¹  Junfeng Xia²  Yun-Yun Tang³  Chun-Hou Zheng³  Pi-Jing Wei³  Rui-Fen Cao³ 

[1] College of Mathematics and System Sciences, Xinjiang University;

[2] Institute of Physical Science and Information Technology, Anhui University;

[3] Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University;

关键词: Cancer;    Driver genes;    Mutation data;    Local centrality;    Transcriptional network;   

DOI  :  10.1186/s12859-021-04377-0

来源: DOAJ

【 摘 要 】

Abstract Background As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. Results To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein–protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. Conclusions Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

【 授权许可】

Unknown