Integrated bioinformatic analysis of dysregulated microRNA-mRNA co-expression network in ovarian endometriosis

Abstract

Introduction: Ovarian endometriosis is a frequently occurring gynecological disease with large socioeconomic impact. Accumulating evidence has suggested that aberrant miRNA-mRNA interactions are involved in the pathogenesis and progression of ovarian endometriosis. This study aims to identify key miRNAs in ovarian endometriosis by using integrated bioinformatic analysis of a dysregulated miRNA-mRNA co-expression network. Material and methods: Expression profiling of miRNA and mRNA in three normal endometria and five pairs of ectopic/eutopic endometria from patients with ovarian endometriosis was determined by high-throughput sequencing techniques. The data were then integrated with the public sequencing datasets (GSE105764 and GSE105765) using a non-biased approach and a miRNA-mRNA co-expression regulatory network was constructed by in-depth bioinformatic analysis. Results: The constructed miRNA-mRNA network included 87 functionally DEMs, 482 target mRNAs and 1850 paired miRNA-mRNA regulatory interactions. Specifically, five miRNAs (miR-141-3p, miR-363-3p, miR-577, miR-767-5p, miR-96-5p) were gradually decreased and two miRNAs (miR-493-5p, miR-592) were gradually increased from normal endometria to eutopic endometria, and then ectopic endometria tissues. Importantly, miR-141-3p, miR-363-3p and miR-96-5p belonged to the miR-200 family, miR-106a-363 cluster and miR-183/96/182 cluster, respectively. Their target mRNAs were mainly associated with cell adhesion, locomotion and binding, which are suggested to play vital regulatory roles in the pathogenesis of ovarian endometriosis. Conclusions: Integrated bioinformatic analysis of the miRNA-mRNA co-expression network defines the crucial roles of the miR-200 family, miR-106a-363 cluster and miR-183/96/182 cluster in the pathogenesis of ovarian endometriosis. Further in-depth functional studies are needed to unveil the molecular mechanisms of these miRNAs, and may provide clues for the optimization of therapeutic strategies for ovarian endometriosis.