Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2021-05, Vol.18 (3), p.1026-1034
• Main Authors: Wang, Wenyan, Zhou, Yuming, Cheng, Mu-Tian, Wang, Yan, Zheng, Chun-Hou, Xiong, Yan, Chen, Peng, Ji, Zhiwei, Wang, Bing
• Format: Article
• Language: English
• Subjects: Bioinformatics; Cancer; Clinical medicine; Computational Biology - methods; Correlation analysis; Data mining; Genes; Humans; interaction network; Lethality; Melanoma; Melanoma - genetics; Mutation; Mutation - genetics; Neoplasms - genetics; Network analysers; Network analysis; Pathogenesis; Pathogenic genes; Pathogenicity; Pathogens; protein domain; Protein engineering; Protein Interaction Domains and Motifs - genetics; Protein Interaction Maps - genetics; Proteins; Skin cancer; somatic mutations
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2020.2983894

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.