microRNAs' differential regulations mediate the progress of Human Papillomavirus (HPV)-induced Cervical Intraepithelial Neoplasia (CIN).

BACKGROUND

microRNA (miRNA)'s direct regulation on target mRNA is affected by complex factors beyond miRNA. Therefore, at different stages during the course of carcinogenesis, miRNA may regulate different targets, which we termed 'miRNA's differential regulation'. HPV-induced cervical intraepithelial neoplasia (CIN) is an important pre-cancerous course ahead of cervical cancer formation. Currently, the molecular mechanisms of CIN progress remain poorly understood, and it is interesting to unravel this from the perspective of miRNA differential regulation.

RESULTS

In this study, we performed transcriptome analysis of miRNAs and mRNAs for the totally 24 cervical samples in three stages (normal, CIN I, and CIN III) along CIN progress, and proposed the SIG++ algorithm to detect the miRNA - mRNA pairs with significant regulation change, and further proposed the definitions of Efficient Pair, Efficient Target, and Related Effector Biological Process, as the elemental steps to construct miRNA differential regulatory network. Finally, for the course of disease progressing from normal stage to CIN I stage, and for the course of disease progressing from CIN I stage to CIN III stage, miRNA differential regulatory networks were constructed, respectively, based on two distinct strategies: one is founded on the knowledge of human GO biological processes to detect Efficient Targets and Related Effector Biological Processes, the other is solely founded on literature review to detect the targets closely related to cervical carcinogenesis and instructive in revealing mechanisms that promote CIN development.

CONCLUSIONS

This study provided the conception of miRNA's differential regulation, the algorithm for how to identify them during disease development, and the strategy for how to construct miRNA differential regulatory network with instructive biological meanings. The finally constructed networks provide clues for understanding CIN progress.