SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis. (27th August 2022)

• Record Type: Journal Article
• Authors: Guo, Li; Dou, Yuyang; Xia, Daoliang; Yin, Zibo; Xiang, Yangyang; Luo, Lulu; Zhang, Yuting; Wang, Jun; Liang, Tingming
• Is Part Of: Database. Volume 2022(2022)
• Publication Date: 2022-08-27
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baac075
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD ) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality. Database URL http://www.tmliang.cn/SLOAD/