Background: This study aimed to integrate DNA methylation, miRNA, and mRNA microarray data to construct a gene co-expression network for polycystic ovarian syndrome (PCOS).
Methods: The weighted gene co-expression network analysis (WGCNA) was conducted to construct a PCOS-related co-expression network by using the GEO public datasets. We performed Gene Ontology and KEGG pathway enrichment analyses for a further exploration of gene function in networks. Finally, the dysfunction module consisting of a co-expression network was mapped to the PCOS patients and tried to provide guidance to the PCOS phenotyping.
Results: Three modules (Midnightbule, Pink, and Red) were identified to be PCOS-related by WGCNA analysis. These module-related genes were enriched in cell response to stimulus, PI3K-Akt signaling pathway, insulin biological process, signaling pathway, and cytokine-cytokine receptor interaction biological processes. The multiple-factor network, including miRNA-lncRNA and DNA methylation-mRNA interaction, was closely associated with PCOS dysfunction.
Conclusion: Our study render a novel insight into the mechanisms and might provide candidate biomarkers and therapeutic targets for the classification of PCOS dysfunction.
Keywords: DNA methylation-mRNA network; Polycystic ovarian syndrome; dysfunction phenotype; miRNA-lncRNA; reproductive disorder.
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