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Integrated bioinformatics analysis and screening of hub genes in polycystic ovary syndrome

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Abstract

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.

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Data availability

The data that support the findings of this study are available online. Contact with correspondence author for any data and material on reasonable request.

Abbreviations

PCOS:

Polycystic ovary syndrome

GCs:

Granulosa cells

DEGs:

Differentially expressed genes

BMI:

Body mass index

WGCNA:

Weighted gene co-expression network analysis

GEO:

Gene Expression Omnibus

GO:

Gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

PPI:

Protein–Protein Interaction.

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Acknowledgements

We appreciate members of Nucleic Acid Medicine of Luzhou Key Laboratory for discussions throughout the study. We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

Author contributions

J.Y., C.Z. and G.Q. conceived and designed the study. J.Y. and J.X. wrote the manuscript. G.Q. and X.L. completed data collection and management. All authors contributed and approved the manuscript.

Funding

This research was supported by Southwest Medical University Research Program (2020ZRQNA044 and 2020ZRQNB036) to J.Y. and G.Q. This work was supported by the research start-up fund of Southwest Medical University (No. 00170031) to J.Y. The investigation was also supported by the Central Nervous System Drug Key Laboratory of Sichuan Province (No# 200015-01SZ) to G.Q.

Author information

Author notes

  1. These authors contributed equally: Gan Qiao, Jinshan Xing, and Xin Luo

Authors and Affiliations

  1. Department 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

    Gan Qiao & Xin Luo

  2. Department of Neurosurgery, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China

    Jinshan Xing

  3. Nucleic Acid Medicine of Luzhou Key Laboratory, Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, Sichuan, China

    Chunxiang Zhang

  4. Department of Medical Cell Biology and Genetics, School of Basic Medical Sciences, Nucleic Acid Medicine of Luzhou Key Laboratory, Key Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, (Collaborative Innovation Center for Prevention of Cardiovascular Diseases), Institute of Cardiovascular Research, Southwest Medical University, Luzhou, 646000, Sichuan, China

    Jingyan Yi

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  1. Gan Qiao
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Correspondence to Chunxiang Zhang or Jingyan Yi.

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Ethical approval was obtained to report on the patients involved as all databases used in this study are public databases. The animal experiments were performed under the approval of the Animal Care and Use Committee of Southwest Medical University.

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Qiao, G., Xing, J., Luo, X. et al. Integrated bioinformatics analysis and screening of hub genes in polycystic ovary syndrome. Endocrine 78, 615–627 (2022). https://doi.org/10.1007/s12020-022-03181-x

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