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. 2024 Sep 9:15:1442483.
doi: 10.3389/fendo.2024.1442483. eCollection 2024.

From proteome to pathogenesis: investigating polycystic ovary syndrome with Mendelian randomization analysis

Jiaqi Zhang #  1 , Yuqing Li #  2 , Aixia Gong  1 , Jingmin Wang  2
Affiliations

From proteome to pathogenesis: investigating polycystic ovary syndrome with Mendelian randomization analysis

Jiaqi Zhang et al. Front Endocrinol (Lausanne). .

Abstract

Background: Polycystic ovary syndrome (PCOS) is defined by oligo/anovulation, hyperandrogenism, and polycystic ovaries with uncertain pathogenesis. The proteome represents a substantial source of therapeutic targets, and their coding genes may elucidate the mechanisms underlying PCOS. However, reports on the profiles of the human plasma protein-coding genes and PCOS are limited. Here, we aimed to investigate novel biomarkers or drug targets for PCOS by integrating genetics and the human plasma proteome.

Methods: Our study acquired the protein quantitative trait loci from DECODE Genetics, offering 4,907 proteins in 35,559 individuals while obtaining PCOS summary statistics by accessing the FinnGen biobank (1,639 cases and 218,970 controls) and the genome-wide association study catalog (797 cases and 140,558 controls). Herein, we sequentially used two-sample Mendelian randomization (MR) analyses and colocalization to verify the causal link between candidate proteins, their coding genes, and PCOS. Further PCOS data download was conducted by accessing the Gene Expression Omnibus and Zenodo platforms. Gene expression level analysis, pathway enrichment analysis, immune cell infiltration, and transcription factor prediction were performed, aiming at detecting specific cell types with enriched expression and exploring potential optimized treatments for PCOS.

Results: MR analysis revealed 243 protein-coding genes with a causal relationship to PCOS risk, of which 12 were prioritized with the most significant evidence. Through colocalization analysis, three key genes, CUB domain-containing protein 1 (CDCP1), glutaredoxin 2 (GLRX2), and kirre-like nephrin family adhesion molecule 2 (KIRREL2), were identified. Subsequently, the three genes were strongly related to immune function and metabolism in terms of biological significance. In single-cell analysis, the expression levels of genes in ovarian theca cells were explored.

Conclusion: Overall, three protein-coding genes (CDCP1, GLRX2, and KIRREL2) may be related to a higher PCOS risk, suggesting that they may be entry points for exploration of PCOS pathogenesis and treatment, warranting further clinical investigations.

Keywords: Mendelian randomization; bioinformatics; metabolism; polycystic ovary syndrome; proteome.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The study design schematic diagram.
Figure 2
Figure 2
Forest plot indicating Mendelian randomization estimation of the connection between plasma protein-coding genes and PCOS risk.
Figure 3
Figure 3
Scatter plots of Mendelian randomization analysis for twelve genes, with distinct colors indicating distinct statistical methods, with the lines’ slopes indicating each method’s causal effect. (A) ERBB4, (B) ADAMTS4, (C) KLK8, (D) GLTPD2, (E) CDCP1, (F) HEPACM, (G) ERP27, (H) PROK2, (I) GLRX2, (J) KIRREL2, (K) EIF4EBP1, and (L) CMBL.
Figure 4
Figure 4
Forest plots of leave-out test for SNPs corresponding to the twelve identified genes. (A) ERBB4, (B) ADAMTS4, (C) KLK8, (D) GLTPD2, (E) CDCP1, (F) HEPACM, (G) ERP27, (H) PROK2, (I) GLRX2, (J) KIRREL2, (K) EIF4EBP1, and (L) CMBL.
Figure 5
Figure 5
Co-localization analysis. (A) CDCP1, (B) GLRX2, and (C) KIRREL2.
Figure 6
Figure 6
(A) GSEA results of CDCP1. (B) GSVA results of CDCP1. (C) GSEA results of GLRX2. (D) GSVA results of GLRX2. (E) GSEA results of KIRREL2. (F) GSVA results of KIRREL2.
Figure 7
Figure 7
Assessment of immune infiltration. (A) Relative infiltrating proportion of 29 immune cell subtypes. (B) Correlation relationship between 29 immune cell subtypes. (C) Differences in immune cell infiltration between PCOS patients and controls. (D-F) The relationship between key genes (D) CDCP1, (E) GLRX2, and (F) KIRREL2 and immune cell subtypes (*P<0.05, **P<0.01, ***P<0.001). ns, non-significant.
Figure 8
Figure 8
Prediction of transcription factors (TFs). (A) The key genes-TF regulatory network. The red node represents TFs, and the green node represents key genes. (B) Enrichment analysis of TF binding motifs of the key genes.
Figure 9
Figure 9
Single-cell (SC) type expression in PCOS for protein-coding genes. (A) tSNE cluster analysis diagram. (B, C) SC gene expression of three coding genes in every cluster.
Figure 10
Figure 10
Gene co-expression of SHBG and three key genes at the single cell level. (A) CDCP1, (B) GLRX2, and (C) KIRREL2.
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