Discovering Cancer-related miRNAs from miRNA-target Interactions by Support Vector Machines

Abstract miRNAs have been shown to be closely related to cancer progression. Traditional methods for discovering cancer-related miRNAs mostly require significant marginal differential expression, but some cancer-related miRNAs may be non-differentially or only weakly differentially expressed. Such miRNAs are called as Dark Matters miRNAs (DM-miRNAs) and targeted by Pian et al. (2018) through the Pearson correlation change on miRNA-target interactions (MTIs); but the efficiency of their method heavily relies on restrictive assumptions. In this paper, a novel method was developed to discover DM-miRNAs using Support Vector Machine (SVM) based on not only the miRNA expression data but also the expression of its regulating target. The application of the new method in breast and kidney cancer datasets found respectively 9 and 24 potential DM-miRNAs that cannot be detected by previous methods. 8 and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers respectively in existing literature, respectively. These results indicate that our new method is more effective in discovering cancer-related miRNAs.