Identification of Crucial lncRNAs, miRNAs, mRNAs, and Potential Therapeutic Compounds for Polycystic Ovary Syndrome by Bioinformatics Analysis

Abstract

Background: This study was aimed at mining crucial long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) for the development of polycystic ovary syndrome (PCOS) based on the coexpression and the competitive endogenous RNA (ceRNA) theories and investigating the underlying therapeutic drugs that may function by reversing the expression of lncRNAs, miRNAs, and mRNAs.

Methods: RNA (GSE106724, GSE114419, GSE137684, and GSE138518) or miRNA (GSE84376 and GSE138572) expression profile datasets of PCOS patients were downloaded from the Gene Expression Omnibus database. The weighted gene coexpression network analysis (WGCNA) using four RNA datasets was conducted to construct the lncRNA-mRNA coexpression networks, while the common differentially expressed miRNAs in two miRNA datasets and module RNAs were used to establish the ceRNA network. A protein-protein interaction (PPI) network was created to explore the potential interactions between genes. Gene Ontology and KEGG pathway enrichment analyses were performed to explore the functions of genes in networks. Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD) analyses were performed to identify potential therapeutic agents for PCOS.

Results: Three modules (black, magenta, and yellow) were identified to be PCOS-related after WGCNA analysis, in which KLF3-AS1-PLCG2, MAPKAPK5-AS1-MAP3K14, and WWC2-AS2-TXNIP were important coexpression relationship pairs. WWC2-AS2-hsa-miR-382-PLCG2 was a crucial ceRNA loop in the ceRNA network. The PPI network showed that MAP3K14 and TXNIP could interact with hub genes PLK1 (degree = 21) and TLR1 (degree = 18), respectively. These genes were enriched into mitosis (PLK1), immune response (PLCG2 and TLR1), and cell cycle (TXNIP and PLK1) biological processes. Ten small molecule drugs (especially quercetin) were considered to be therapeutical for PCOS.

Conclusion: Our study may provide a novel insight into the mechanisms and therapy for PCOS.

Figure 1

Differentially expressed RNAs and miRNAs. (a) The heat map of differentially expressed RNA (including mRNAs and lncRNAs) identified in four datasets (GSE106724, GSE114419, GSE137684, and GSE138518); (b, d) the volcano plot of differentially expressed miRNAs identified in the GSE84376 (b) and GSE138572 (d) datasets, respectively; (c, e) the heat map of differentially expressed miRNAs identified in the GSE84376 (c) and GSE138572 (e) datasets, respectively; (f) Venn diagram to identify the common differentially expressed miRNAs between GSE84376 and GSE138572 datasets.

Figure 2

Assessment of the correlation between datasets and selection of soft threshold power β based on the training dataset. (a) The correlation of the RNA expression levels; (b) the correlation of the connectivity; (c) selection of power when the square value was equal to the red standard line (0.9) for the first time; (d) calculation of mean connectivity according to β values.

Figure 3

WGCNA analysis. (a–d) Clustering of differentially expressed lncRNAs and mRNAs of GSE106724 (a), GSE138518 (b), GSE137684 (c), and GSE114419 (d) datasets; (e) the correlation between gene modules and PCOS development.

Figure 4

The coexpression relationships in three crucial modules: (a) black module; (b) magenta module; (c) yellow module. Genes in boxes indicate the lncRNAs; genes in circles indicate the mRNAs.

Figure 5

Construction of the ceRNA network using the lncRNAs, mRNAs in three crucial modules, and common miRNAs identified by GSE84376 and GSE138572 datasets. Genes in boxes indicate the lncRNAs; genes in circles indicate the mRNAs; genes in triangles indicate the miRNAs. Different colors for lncRNAs and mRNAs represent different modules.

Figure 6

Construction of the PPI network for all the differentially expressed mRNAs in three crucial modules. Different colors for mRNAs represent different modules.

Figure 7

The target relationships between differentially expressed RNAs and small molecular drugs.