Discovering Cancer-Related miRNAs from miRNA-Target Interactions by Support Vector Machines

• Published in: Molecular therapy. Nucleic acids Vol. 19; pp. 1423 - 1433
• Main Authors: Pian, Cong, Mao, Shanjun, Zhang, Guangle, Du, Jin, Li, Fei, Leung, Suet Yi, Fan, Xiaodan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 06.03.2020; American Society of Gene & Cell Therapy; Elsevier
• Subjects: cancer-related miRNAs; dark matters; expression data; miRNA-target interactions; support vector machine; expression data; cancer-related miRNAs; support vector machine; miRNA-target interactions; dark matters
• ISSN: 2162-2531; 2162-2531
• DOI: 10.1016/j.omtn.2020.01.019

MicroRNAs (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 dark matters miRNAs (DM-miRNAs) and are targeted 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. Eight and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers, respectively, in existing literature. These results indicate that our new method is more effective in discovering cancer-related miRNAs.