SLOAD: A comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis

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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/

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Guo, L., Dou, Y., Xia, D., Yin, Z., Xiang, Y., Luo, L., … Liang, T. (2022). SLOAD: A comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis. Database, 2022. https://doi.org/10.1093/database/baac075

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