Shared pathogenesis in polycystic ovaries and rheumatoid arthritis: an analysis of key genes and pathways.

Objective: The study aims to explore the potential shared pathogenic processes between PCOS and RA through bioinformatics analysis to identify novel therapeutic targets and biomarkers for disease management.

Methods: Microarray datasets for polycystic ovary and RA were obtained from the GEO database. Differential gene expression analysis identified commonly dysregulated genes in both conditions. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed to understand the biological processes and pathways associated with the differentially expressed genes (DEGs). Protein interaction analysis, machine learning algorithms, and validation analyses were employed to identify core genes with potential diagnostic value. Immune cell infiltration analysis and evaluation of hypoxia and angiogenesis scores were conducted to assess the role of the core genes in immune-related disorders.

Results: Microarray analysis identified differentially expressed genes (DEGs) commonly dysregulated in PCOS and RA. GO and KEGG enrichment analyses highlighted the involvement of cell death, inflammation, and redox pathways. Ten key genes were identified through protein interaction analysis, and machine learning further narrowed it down to six core genes: CSTA, DPH3, CAPZA2, GLRX, CD58, and IFIT1. The core genes were overexpressed in PCOS and RA tissues, suggesting their potential involvement in disease development. Validation analyses confirmed the diagnostic potential of these genes, especially in RA. Immune cell infiltration analysis correlated the expression of core genes with neutrophil and CD8⁺ T cell infiltration. Hypoxia and angiogenesis scores indicated the significance of these genes in immune-related disorders.

Conclusion: The study unveils potential molecular links between PCOS and RA, highlighting the importance of immune dysregulation in their pathogenesis. The identified core genes offer novel therapeutic targets and potential biomarkers for disease management, providing insights into the complex interplay between these two seemingly unrelated conditions.