Meta-analysis of differentially expressed genes in PCOS

Background: Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrine disorders affecting reproductive-age females, with a global prevalence of 6-10%. It is characterized by hyperandrogenism, ovulatory dysfunction, and/or the presence of polycystic ovaries. Common clinical manifestations include infertility, pregnancy loss, hirsutism, insulin resistance, metabolic syndrome, coronary heart disease, venous thromboembolism, and psychosocial disorders (i.e., mood and eating disorders). Despite its broad impact, the role of genetic factors in PCOS pathogenesis and progression remains underexplored.
Objective: This study aims to identify critical genes and biomarkers associated with PCOS, offering insight to enhance pathophysiological understanding, improve diagnostic precision, and support the development of targeted therapeutics.
Methods: Granulosa cells from patients with PCOS (n=23) and control patients without PCOS (n=13) were identified via a query in the Search Tag Analyze Resource for NCBI’s Gene Expression Omnibus (STARGEO). Meta-analysis was conducted in STARGEO to identify differentially expressed genes in PCOS granulosa cells versus control, yielding 22,684 genes. These genes were restricted to p<0.05 for statistical significance and absolute experimental log ratio >0.1 for further analysis in Ingenuity Pathway Analysis (IPA).
Results: A total of 192 molecules were included for analysis, of which 128 were upregulated and 64 were downregulated. CD163, TGFBI, and CSF3R are genes showing significant upregulation and are involved in induction of inflammation and protection against oxidative damage, regulation of cell adhesion and bone formation, and granulocyte function and differentiation, respectively. Downregulated genes, such as CARMIL1 and CPM, contribute to actin filament formation and differentiation of monocytes to macrophages, respectively. The top canonical pathways predicted to show altered expression in PCOS are neutrophil degranulation (z-score 4.899), osteoarthritis pathway (1.633), phagosome formation (4.123), and neutrophil extracellular trap signaling pathway (2.496). Top upstream regulators predicted to contribute to the observed downstream differential expression of genes in the dataset include lipopolysaccharide (z-score 3.642), FAS (0.663), PCSK9 (no z-score prediction), whereas the top causal networks are inavolisib (1.313), PRTN3 (3.773), and SLPI (-2.771).
Conclusion: Identifying target genes is a preliminary and vital step in developing efficacious targeted treatment for polycystic ovarian syndrome for the 5-6 million people impacted by this disease daily. The genes and pathways presented herein provide further insight into the pathogenesis and genetic drivers of PCOS. Biomarkers identified may also enable prediction and prevention of PCOS development to optimize fertility and reduce gestational complications.