Prioritizing candidate disease miRNAs by integrating phenotype associations of multiple diseases with matched miRNA and mRNA expression profiles

Abstract

MicroRNAs (miRNAs) have been validated to show widespread disruption of function in many cancers. However, despite concerted efforts to develop prioritization approaches based on a priori knowledge of disease-associated miRNAs, uncovering oncogene or tumor-suppressor miRNAs remains a challenge. Here, based on the assumption that diverse diseases with phenotype associations show similar molecular mechanisms, we present an approach for the systematic prioritization of disease-specific miRNAs by using known disease genes and context-dependent miRNA-target interactions derived from matched miRNA and mRNA expression data, independent of known disease miRNAs. After collecting matched miRNA and mRNA expression data for 11 cancer types, we applied this approach to systematically prioritize miRNAs involved in these cancers. Our approach yielded an average area under the ROC curve (AUC) of 75.84% according to known disease miRNAs from the miR2Disease database, with the highest AUC (80.93%) for pancreatic cancer. Moreover, we assessed the sensitivity and specificity as well as the integrative importance of this approach. Comparative analyses also showed that our method is comparable to previous methods. In summary, we provide a novel method for prioritization of disease-related miRNAs that can help researchers better understand the important roles of miRNAs in human disease.