Prioritizing Candidate Disease miRNAs by Topological Features in the miRNA-Target Dysregulated Network

Abstract

Recently, miRNAs have taken centre stage in the field of human molecular oncology. However, their roles in tumor biology remain largely unknown. According to the assumption that miRNAs implicated in a specific tumor phenotype will show aberrant regulation of their target genes, we introduce an approach based on the miRNA-Target dysregulated network (MTDN) to prioritize novel disease miRNAs. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and non-Tumor tissues. Application of the proposed method to prostate cancer (PC) revealed that known PC miRNAs are characterized by a greater number of dysregulations and co-regulators, and the tendency to co-regulate with each other, and that they share a higher proportion of targets with other PC miRNAs. SVM classifier based on these features and changes in miRNA expression was constructed and gave an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier was then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs were closely associated with oncogenesis. In addition, predicted cancer miRNAs within families or from different families show combinatorial dysregulation of target genes, as revealed by analysis of the MTDN modular organization. Finally, three miRNA-Target regulations were verified to hold in PC cells by transfection assays. These results show that the network-centric method could prioritize novel disease miRNAs and model how oncogenic lesions are mediated by miRNAs, providing important insights into tumorigenesis.