Prioritizing cancer-related microRNAs by integrating microRNA and mRNA datasets.

MicroRNAs (miRNAs) are small non-coding RNAs regulating the expression of target genes, and they are involved in cancer initiation and progression. Even though many cancer-related miRNAs were identified, their functional impact may vary, depending on their effects on the regulation of other miRNAs and genes. In this study, we propose a novel method for the prioritization of candidate cancer-related miRNAs that may affect the expression of other miRNAs and genes across the entire biological network. For this, we propose three important features: the average expression of a miRNA in multiple cancer samples, the average of the absolute correlation values between the expression of a miRNA and expression of all genes, and the number of predicted miRNA target genes. These three features were integrated using order statistics. By applying the proposed approach to four cancer types, glioblastoma, ovarian cancer, prostate cancer, and breast cancer, we prioritized candidate cancer-related miRNAs and determined their functional roles in cancer-related pathways. The proposed approach can be used to identify miRNAs that play crucial roles in driving cancer development, and the elucidation of novel potential therapeutic targets for cancer treatment.