Construction of a competing endogenous RNA network to identify drug targets against polycystic ovary syndrome.

Abstract

Would the construction of a competing endogenous RNA (ceRNA) network help identify new drug targets for the development of potential therapies for polycystic ovary syndrome (PCOS)? Both Food and Drug Administartion (FDA)-approved and candidate drugs could be identified by combining bioinformatics approaches with clinical sample analysis based on our established ceRNA network. Thus far, no effective drugs are available for treating PCOS. ceRNAs play crucial roles in multiple diseases, and some of them are in current use as prognostic biomarkers as well as for chemo-response and drug prediction. For the bioinformatics part, five microarrays of human granulosa cells were considered eligible after applying strict screening criteria and were used to construct the ceRNA network for target identification. For population-based validation, samples from 24 women with and without PCOS were collected from January 2021 to July 2021. The public data included 27 unaffected women and 25 women with PCOS, according to the Rotterdam criteria proposed in 2003. The limma and RobustRankAggreg R packages were used to identify differentially expressed messenger RNAs and noncoding RNAs. Gene Ontology, Reactome and Kyoto Encyclopedia of Genes and Gemomes (KEGG) enrichment analyses were performed. A ceRNA network was constructed by integrating the differentially expressed genes and target genes. The population-based validation included human luteinized granulosa cell samples from 12 unaffected women and 12 women with PCOS. Quantitative real-time polymerase chain reaction was conducted to detect the levels of mRNAs and microRNAs (miRNAs). Connectivity map and computational model algorithms were implemented to predict therapeutic drugs from the ceRNA network. Additionally, we compared the predicted drugs with known clinical medications in DrugBank. A set of 10 mRNAs, 11 miRNAs and 53 long non-coding RNAs (lncRNAs) were differentially expressed. Functional enrichment analysis revealed the highest relevance to immune system-related biological processes and signalling pathways, such as cytokine secretion and leucocyte chemotaxis. A ceRNA consisting of two lncRNAs, two miRNAs and five mRNAs was constructed. Through network construction via bioinformatic analysis, we identified some already approved drugs (such as metformin) that could target some molecules in the network as potential drug candidates for PCOS. Public sequencing data were obtained from GSE34526, GSE84376, GSE102293, GSE106724 and GSE114419, which have been deposited in the Gene Expression Omnibus database. Experiments, such as immunoprecipitation, luciferase reporter assays and animal model studies, are needed to validate the potential targets in the ceRNA network before the identified drug candidates can be tested using cellular and animal model systems. Our findings provide new bioinformatic insight into the possible pathogenesis of PCOS from ceRNA network analysis, which has not been previously studied in the human reproductive field. Our study also reveals some potential drug candidates for the future development of possible therapies against PCOS. This study was supported by grants from the National Key Research and Development Program of China (2021YFC2700400) and the National Natural Science Foundation of China (82001498). The authors have no conflicts of interest to disclose.