Connecting rules from paired miRNA and mRNA expression data sets of HCV patients to detect both inverse and positive regulatory relationships

摘要

Background: Intensive research based on the inverse expression relationship has been undertaken to discover tl miRNA-mRNA regulatory modules involved in the infection of Hepatitis C virus (HCV), the leading cause of chror liver diseases. However, biological studies in other fields have found that inverse expression relationship is not th only regulatory relationship between miRNAs and their targets, and some miRNAs can positively regulate a mRN by binding at the 5' UTR of the mRNA.Results: This work focuses on the detection of both inverse and positive regulatory relationships from a paired miRNA and mRNA expression data set of HCV patients through a 'change-to-change' method which can derive connected discriminatory rules. Our study uncovered many novel miRNA-mRNA regulatory modules. In particular was revealed that GFRA2 is positively regulated by miR-557, miR-765 and miR-17-3p that probably bind at differe locations of the 5' UTR of this mRNA. The expression relationship between GFRA2 and any of these three miRNA has not been studied before, although separate research for this gene and these miRNAs have all drawn conclusions linked to hepatocellular carcinoma. This suggests that the binding of mRNA GFRA2 with miR-557, mi 765,or miR-17-3p, or their combinations, is worthy of further investigation by experimentation. We also report another mRNA QKI which has a strong inverse expression relationship with miR-129 and miR-493-3p which may bind at the 3' UTR of QKI with a perfectsequence match. Furthermore, the interaction between hsa-miR-129-5p (previous ID: hsa-miR-129) and QKI is supported with CLIP-Seq data from starBase. Our method can be easily extended for the expression data analysis of other diseases.Conclusion: Our rule discovery method is useful for integrating binding information and expression profile for identifying HCV miRNA-mRNA regulatory modules and can be applied to the study of the expression profiles of other complex human diseases.