SLOAD : Welcome to Synthetic Lethality Online Analysis Database

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 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 we constructed SLOAD via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide precise cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that promotes therapy strategies based on the principle of synthetic lethality.

The overall performance of the prediction method of random forest was better than our previous studies with the prediction method of decision tree. The mean AUC value was 0.816 via random forest, but it was 0.747 via decision tree. Both the accuracy and AUC values showed significant difference between the two algorithms, indicating the better prediction results using random forest method.

A total of 139,035 gene pairs were collected in 31 cancer types, containing 10,377 genes. In 31 cancers, numbers of gene pairs were different in diverse cancer types, and many synthetic lethal interactions were detected in multiple cancers, especially shared by 7-14 cancers. The genetic interactions among genes were complex, and some genes were involved in dynamic expression patterns across cancers.