| Title: | Identification of Key Genes and Imbalanced SNAREs Assembly in the Comorbidity of Polycystic Ovary Syndrome and Depression |
| Journal: | Genes |
| Published: | 15 Apr 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38674428/ |
| DOI: | https://doi.org/10.3390/genes15040494 |
| URL: | https://www.mdpi.com/2073-4425/15/4/494/pdf?version=1713194781 |
Publication 9899
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Abstract
BACKGROUND: Women with polycystic ovary syndrome (PCOS) have increased odds of concurrent depression, indicating that the relationship between PCOS and depression is more likely to be comorbid. However, the underlying mechanism remains unclear. Here, we aimed to use bioinformatic analysis to screen for the genetic elements shared between PCOS and depression.</p>
METHODS: Differentially expressed genes (DEGs) were screened out through GEO2R using the PCOS and depression datasets in NCBI. Protein-protein interaction (PPI) network analysis and enrichment analysis were performed to identify the potential hub genes. After verification using other PCOS and depression datasets, the associations between key gene polymorphism and comorbidity were further studied using data from the UK biobank (UKB) database.</p>
RESULTS: In this study, three key genes, namely, SNAP23, VTI1A, and PRKAR1A, and their related SNARE interactions in the vesicular transport pathway were identified in the comorbidity of PCOS and depression. The rs112568544 at SNAP23, rs11077579 and rs4458066 at PRKAR1A, and rs10885349 at VTI1A might be the genetic basis of this comorbidity.</p>
CONCLUSIONS: Our study suggests that the SNAP23, PRKAR1A, and VTI1A genes can directly or indirectly participate in the imbalanced assembly of SNAREs in the pathogenesis of the comorbidity of PCOS and depression. These findings may provide new strategies in diagnosis and therapy for this comorbidity.</p>
6 Authors
- Yi Cao
- Weijing Wang
- Xuxia Song
- Qian Wen
- Jing Xie
- Dongfeng Zhang
1 Application
| Application ID | Title |
|---|---|
| 95715 | Effects of genomic, metabolomic, environmental and clinical factors on neurological and psychiatric disorders, and construction of predictive models |
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