Predicting miRNA targets for hepatocellular carcinoma with an integrated method

BACKGROUND: MicroRNAs (miRNAs) were aberrantly regulated in cancers, showing their roles as novel classes of oncogenes and tumor suppressors. Hence, an integrated method was introduced in this study to explore miRNA targets for hepatocellular carcinoma (HCC). METHODS: The Borda count election algorithm was applied to combine a correlation method (Pearson’s correlation coefficient, PCC), a causal inference method (IDA), and a regression method (Lasso) to generate an integrated method. Subsequently, to confirm the performance of the integrated method, the predicted miRNA targets results were compared with the confirmed database. Finally, pathway enrichment analysis was applied to evaluate the target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions. RESULTS: The method was confirmed to be an approach to predict miRNA targets. Moreover, 50 highly confident miRNA-mRNA interactions were obtained, including 6 miRNA targets with predicted times ≥10 (for instance, MEG3). The 860 target genes of the top 1,000 miRNA-mRNA interactions were enriched in 26 pathways, of which complement and coagulation cascades were most significant. CONCLUSIONS: The results might supply great insights for revealing the pathological mechanism underlying HCC and explore potential biomarkers for the diagnosis and treatment of this tumor. However, these biomarkers have not been confirmed, and the related validations should be performed in future studies.