Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA–miRNA synergistic network from a new view. Here, we constructed a miRNA–miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.

Recently, microRNAs (miRNA), small noncoding RNAs, have taken center 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. Target genes have predicted binding sites for any miRNA. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and nontumor tissues. Application of the proposed method to prostate cancer reveals that known prostate cancer miRNAs are characterized by a greater number of dysregulations and coregulators and the tendency to coregulate with each other and that they share a higher proportion of targets with other prostate cancer miRNAs. Support vector machine classifier, based on these features and changes in miRNA expression, is constructed and gives an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier is then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs are 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, 3 miRNA target regulations are verified to hold in prostate cancer 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.