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Updated: Aug 29, 2025
Evaluation of Hepatic Glucose Production in a Polycystic Ovary Syndrome Mouse Model
Published on: March 5, 2022
Integrated bioinformatics analysis and screening of hub genes in polycystic ovary syndrome
Gan Qiao1, Jinshan Xing2, Xin Luo1
1Department of Pharmacology, School of Pharmacy, Nucleic Acid Medicine of Luzhou Key Laboratory, Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, 646000, Sichuan, China.
Purpose:
Polycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders, posing a serious threat to the health of women. Herein, we aimed to explore new biomarkers and potential therapeutic targets for PCOS by employing integrated bioinformatics tools.
Methods:
Three gene expression profile datasets (GSE138518, GSE155489, GSE106724) were obtained from the Gene Expression Omnibus database and the differentially expressed genes in PCOS and normal groups with an adjusted p-value < 0.05 and a |log fold change (FC) | > 1.2 were first identified using the DESeq package. The weighted correlation network analysis (WGCNA) R package was used to identify clusters of highly correlated genes or modules associated with PCOS. Protein-protein interaction (PPI) network analysis and visualization of genes in the key module were performed using the STRINGdb database and the NetworkX package (edge > 5), respectively. The genes overlapping among the key module genes and PCOS-associated genes were further analyzed. Ligand molecules with strong binding energy < -10 kJ/mol to GNB3 were screened in the drug library using MTiOpenScreen. AutoDock, ChimeraX, and BIOVIA Discovery Studio Visualizer were further used to elucidate the mechanism of ligand interaction with GNB3. Finally, the relationship between GNB3 and PCOS was verified using experimental models in vivo and in vitro.
Results:
Of the 11 modules identified by WGCNA, the black module had the highest correlation with PCOS (correlation = 0.96, P = 0.00016). The PPI network of 351 related genes revealed that VCL, GNB3, MYH11, LMNA, MLLT4, EZH2, PAK3, and CHRM1 have important roles in PCOS. The hub gene GNB3 was identified by taking the intersection of PCOS-related gene sets. MTiOpenScreen revealed that five compounds interacted with GNB3. Of these five, compound 1 had the strongest binding ability and can bind amino acids in the WD40 motif of GNB3, which in turn affects the function of the G protein-coupled receptor β subunit. GNB3 was also significantly downregulated in PCOS models.
Conclusion:
We identified the hub gene GNB3 as the most important regulatory gene in PCOS. We suggest that compound 1 can target the WD40 motif of GNB3 to affect related functions and must be considered as a lead compound for drug development. This study will provide new insights into the development of PCOS-related drugs.
