Bioinformatics-driven investigation into the molecular pathways of polycystic ovary syndrome

doi:10.3969/j.issn.1004-583X.2025.07.005

Abstract

Abstract:

Objective To identify novel genetic targets for polycystic ovary syndrome (PCOS) and to reveal the molecular basis underlying the development of PCOS using microarray datasets. Methods A comprehensive analysis was conducted on three independent PCOS datasets from the Gene Expression Omnibus database, and data processing and normalization were performed using R software. The evaluation of the relationship between differentially expressed genes (DEGs) and PCOS included differential expression analysis, expression quantitative trait locus analysis, and Mendelian randomization (MR) analysis. Gene Set Variation Analysis and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to explore the functional roles and pathways of these genes. Lasso regression, SVM machine learning, and random forest tree method were used to screen PCOS intersection feature genes, and then a cross validation was conducted with MR method. In addition, CIBERSORT analysis was used to evaluate the infiltration levels of 22 immune cells in PCOS. Finally, a single gene ceRNA regulatory network was constructed for visualization. Results This study identified 59 significant differentially expressed genes (DEGs), including 28 upregulated DEGs and 31 downregulated DEGs. Lasso regression, SVM machine learning, and random forest tree method were used to screen six intersection feature genes related to PCOS, including DNAJC3, ASPH, TLR4, SEC24D, SGK1 and AMFR. These genes were involved in important biological processes and pathways, including responses to endoplasmic reticulum stress, chemical stress, autophagy regulation, endogenous apoptosis, lipid and atherosclerosis, neurodegeneration, etc. The MR analysis method for the cross validation of the aforementioned three methods further suggested that SEC24D had a causal relationship with PCOS, and 37 miRNAs and 15 lncRNAs were closely related to SEC24D. CIBERSORT analysis showed a unique distribution of immune cells in PCOS, particularly a significant increase in neutrophil count, emphasizing the importance of immune processes in PCOS. Conclusion This study provides new insights into the molecular basis of PCOS and emphasizes the potential for therapeutic interventions and targeted molecular pathways for treating PCOS, laying the foundation for further research and clinical work.

Key words: polycystic ovary syndrome, differentially expressed genes, microarray data, eQTL analysis, Mendelian randomization, immune cell infiltration

CLC Number:

R711.75

Guo Qian, Chen Jie, Ma Weirong, Zhang Yan, Yang Yingqian, Tan Yong. Bioinformatics-driven investigation into the molecular pathways of polycystic ovary syndrome[J]. Clinical Focus, 2025, 40(7): 608-618.

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URL: http://www.lchc.cn/EN/10.3969/j.issn.1004-583X.2025.07.005

http://www.lchc.cn/EN/Y2025/V40/I7/608

Figures/Tables 12

Tab.1 The characteristics of the three GEO datasets

GSE ID	样本	组织	平台	试验类型
GSE6798	16例病例和13例对照	肌肉	GPL570	队列
GSE10946	12例病例和11例对照	卵丘细胞	GPL570	队列
GSE8157	30例病例和13例对照	肌肉	GPL570	队列

Fig.1 The pre-PCA and post-PCA analysis of the three PCOS gene datasets a. Before batch correction; b. After batch correction

Fig.2 A Heatmap of differentially expressed genes

Fig.3 A volcano map of the GEO datasets

Fig.4 GO and KEGG enrichment analysis of differentially expressed genes a. GO enrichment analysis of DEGs; b. KEGG enrichment analysis of DEGs

Fig.5 Venn diagram of the intersection characteristic genes

Fig.6 MR analysis of the relationship between SEC24D and PCOS a. Scatter plot; b. Forest plot; c. Leave-one-out sensitivity analysis plot; d. Funnel plot

Fig.7 GSVA analysis diagram of the intersection characteristic gene SEC24D

Fig.8 Bar charts and box plots comparing 22 types of immune cells between the PCOS group and the control group a. Bar chart; b. Box plot

Fig.9 Correlation heat map between 22 types of immune cells and the SEC24D gene

Tab.2 LncRNAs that bind to miRNAs

miRNA	lncRNA	Sources
hsa-miR-875-3p	CDR1-AS	spongeScan
hsa-miR-875-3p	PCBP3-OT1	spongeScan
hsa-miR-875-3p	RP11-64K12.8	spongeScan
hsa-miR-875-3p	FRMPD3-AS1	spongeScan
hsa-let-7d-3p	AC011718.2	spongeScan
hsa-let-7d-3p	RP3-323A16.1	spongeScan
hsa-miR-382-5p	MUC19	spongeScan
hsa-let-7a-2-3p	AC011718.2	spongeScan
hsa-miR-32-3p	TCF4-AS2	spongeScan
hsa-miR-10a-5p	MEG3	spongeScan
hsa-miR-124-5p	RP11-13K12.1	spongeScan
hsa-miR-944	RP5-1077H22.2	spongeScan
hsa-miR-136-3p	TP73-AS1	spongeScan
hsa-let-7f-2-3p	FAM230B	spongeScan
hsa-miR-30c-1-3p	AC011284.3	spongeScan

Fig.10 A ceRNA regulatory network of the intersection characteristic gene SEC24D

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