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. 2024 Apr 15;15(4):494.
doi: 10.3390/genes15040494.

Identification of Key Genes and Imbalanced SNAREs Assembly in the Comorbidity of Polycystic Ovary Syndrome and Depression

Yi Cao  1 , Weijing Wang  2 , Xuxia Song  1 , Qian Wen  1 , Jing Xie  1 , Dongfeng Zhang  2
Affiliations

Identification of Key Genes and Imbalanced SNAREs Assembly in the Comorbidity of Polycystic Ovary Syndrome and Depression

Yi Cao et al. Genes (Basel). .

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.

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.

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.

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.

Keywords: SNAREs; comorbidity; depression; polycystic ovary syndrome (PCOS); single nucleotide polymorphisms (SNPs).

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The design of the study.
Figure 2
Figure 2
Identification of DEGs in PCOS and depression datasets. (A): the DEGs in GSE8157 (PCOS vs. control), (B): the DEGs in GSE23848 (depression vs. control), and (C): consistently changing DEGs in Venn diagram.
Figure 3
Figure 3
PPI network analysis to identify the hub genes. (A): Enrichment analysis conducted before the PPI network analysis. GO terms and KEGG pathways (p < 0.05). (B): GO terms and KEGG pathways (p < 0.05) related genes in enrichment analysis. (C): the 50 DEGs found in enrichment analysis. (D): the 17 hub genes in PPI network. (E): the 17 hub genes. (F,G): the 17 hub genes marked out in GSE8157 and GES 23848.
Figure 4
Figure 4
Enrichment analysis of the hub genes. (A): the top 10 GO terms classified as BP, CC, and MF and top 10 KEGG pathways in DAVID analysis. (B): the 17 hub genes were divided into four clusters. (C): the enrichment results of GO BP and KEGG pathway from Enrichr analysis. (D): DisGeNET from Enrichr analysis to show the hub gene-related diseases. (E): Proteomics Drug Atlas from Enrichr analysis to show the potential drugs.
Figure 5
Figure 5
Gene–miRNA interaction network to 3 key genes. (A): the gene–miRNA interaction network. (B): top 10 miRNAs that regulated the key genes.
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