Polycystic ovary syndrome and epithelial-mesenchymal transition: Mendelian randomization and single-cell analysis insights

Abstract

Background: The process of epithelial-mesenchymal transition (EMT) may promote fibrosis in ovarian tissue related to polycystic ovary syndrome (PCOS), thus affecting ovarian function and hormonal balance.

Objective: This study aimed to explore key genes associated with EMT in PCOS and their potential molecular regulatory mechanisms, exclusively from the perspective of transcriptomics and single-cell RNA sequencing (scRNA-seq), combined with Mendelian Randomization (MR) analysis.

Methods: The dataset for PCOS and EMT-related genes (EMT-RGs) were sourced from public databases. The key genes in this study were identified via differential expression analysis, MR, and evaluation of expression levels. Enrichment analysis and a series of functional analyses were conducted on these genes to further elucidate their potential mechanisms. Subsequently, using scRNA-seq data and validation of the expression of key genes, key cell group in PCOS were identified, followed by pseudo-time and cell communication analyses to provide deeper insights.

Results: Three key genes, NUCB2 [odds ratio (OR) = 0.8634, 95% confidence interval (CI): 0.8145-0.9152, P < 0.0001], PGF (OR = 0.8393, 95% CI: 0.7185-0.9805, P < 0.05), and CRIM1 (OR = 0.7539, 95% CI: 0.6556-0.670, P < 0.0001), were identified as having a unidirectional causal association with PCOS and were associated with a reduced risk of PCOS. In public datasets, NUCB2 exhibited significantly increased expression in PCOS samples, while PGF and CRIM1 showed the opposite trends. These three genes were enriched in pathways related to cellular functions, metabolic processes, and the operation of the nervous system, and they were co-expressed in smooth muscle. Additionally, five cell clusters were annotated, among which fibroblasts were identified as key cells due to their highest expression of all three key genes. Further analysis revealed a bifurcation event occurring during the mid-development stage of fibroblasts, with PCOS samples displaying a higher abundance of fibroblasts. In PCOS samples, fibroblasts exhibited more extensive communication with secretory epithelial cells, indicating a more complex intercellular interaction within this condition.

Conclusion: This study identified three EMT-RGs: NUCB2, PGF, and CRIM1, which were associated with a reduced risk of PCOS, with fibroblast identified as a key cell group in the disease's pathology. This provides new insights for PCOS research.

Keywords: Epithelial-mesenchymal transition; Mendelian randomization; Polycystic ovary syndrome; Single-cell RNA sequencing.

Fig. 1

Identification and enrichment analysis of differential genes. (a-1,2) Volcano plot of differential gene analysis. Red dots represent upregulated genes, blue dots represent downregulated genes, and grey dots represent genes with no significant difference or smallfold changes. (b-1,2) GO and KEGG Enrichment Analysis of Differentially Expressed Genes (DEGs). (c) Venn map to obtain a total of 215 candidate genes shared by EMT-RGs and DEGs. (d-1,2) Enrichment pathway network using the key KEGG results. (e) PPI network constructed using the 215 DE-EMT-RGs

Fig. 2

Screening and identification of key genes. (a) Scatter Plot Analysis of the Expression Patterns of NUCB2, PGF, and CRIM1 Genes. (b) Forest Plot Analysis of the Association between PCOS and the Genes NUCB2, PGF, and CRIM1. (c) Analysis of Funnel Plots for the Genes NUCB2, PGF, and CRIM1 Genes. (d) Leave-One-Out Sensitivity Analysis of the Association between PCOS and the Genes NUCB2, PGF, and CRIM1

Fig. 3

Acquisition of key pathways. (a) KEGG Pathway Enrichment Analysis of the NUCB2 Gene. (b) KEGG Pathway Enrichment Analysis of the PGF Gene. (c) KEGG Pathway Enrichment Analysis of the CRIM1 Gene. (d) Key Pathway GSVA Scores in PCOS and Control Samples. (e) KEGG Pathway Enrichment Analysis of the Pathophysiology of PCOS

Fig. 4

Expression and localization of key Genes in immune response and organ-specific gene networks. (a) Expression Levels of Key Genes in Immunocells with Low Specificity. (b) Expression Levels of Key Genes in Non-Specific Immune Cells. (c) Expression Levels of Key Genes in Highly Specific Immune Cells.(d) Subcellular localization of key genes. (e) Chromosomal localization of key genes. (f) The Gene-Gene Interaction (GGI) network. (g) mRNA expression profiles of NUCB2, PGF, and CRIM1 genes in diverse organs and tissues

Fig. 5

Regulatory networks of key genes. (a) lncRNA-miRNA-mRNA interaction network. (b) MTF1, STAT3, and GCM1 as potential regulators of PGF. (c) Signal transduction among key genes

Fig. 6

Potential drug targets in key genes. (a) Drug prediction analysis for NUCB2, PGF, and CRIM1 genes. (b) 3D structure prediction model for the protein encoded by the NUCB2 gene. (c) 3D structure prediction model for the protein encoded by the PGF gene. (d) 3D structure prediction model for the protein encoded by the CRIM1 gene. (e) 3D structure prediction model for the protein encoded by the NUCB2 and HYDROCHLOROTHIAZIDE

Fig. 7

Identification of key cell. (a) Quality control (QC) process of key cells. (b) Identified Cell Clusters Along Pseudotime Trajectories in Biological Processes.(c) Gene expression levels. (d) Annotation of five cell clusters. (e) Bubble plot of related marker genes. (f) Analysis of key gene expression in cell clusters. (g) Expression levels of key genes across five cellular clusters

Fig. 8

Trajectories of fibroblast maturation and heterogeneity. (a) Pseudo-Time trajectory inference of fibroblasts. (b) Pseudo-Time trajectory inference analysis of six fibroblast cellular clusters. (c) The trajectory graphs of fibroblasts in different samples. (d) Expression dynamics of key genes during fibroblast development

Fig. 9

Communication networks among cellular clusters. (a) Communication networks between secretory epithelial cell clusters and fibroblast cell clusters.(b) Communication networks between Immune cell clusters and fibroblast cell clusters. (c)Communication networks of a single cell cluster with four other cell clusters.(d)Ligand-receptor interactions between fibroblasts and other cell types