Wang Wenyan, Zhou Yuming, Cheng Mu-Tian, Wang Yan, Zheng Chun-Hou, Xiong Yan, Chen Peng, Ji Zhiwei, Wang Bing
IEEE/ACM Trans Comput Biol Bioinform. 2021 May-Jun;18(3):1026-1034. doi: 10.1109/TCBB.2020.2983894. Epub 2021 Jun 3.
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.
致病性相关研究对于理解复杂疾病的发病机制以及提高临床医学水平具有重要意义。这项工作提出了一种生物信息学方案来分析癌症相关基因突变，并试图从蛋白质结构域-结构域相互作用网络中找出与疾病相关的潜在基因。在此，采用了中心致死性原则的五种度量来进行潜在相关性分析，并对基因的重要性进行排序。以恶性黑色素瘤为例，将该方法进一步应用于KEGG通路分析。实验结果表明，可以找到25个结构域，其中18个与恶性黑色素瘤的致病性具有高度潜在相关性。最后，开发了一种基于网络的工具，名为人类癌症相关结构域相互作用网络分析仪，用于对26种人类癌症的潜在致病基因进行排序，分析结果可以在线可视化和下载。
