Evecare • Vol 11 • No. 2 • Sep–Dec 2023 10 Advances in Gynecology The HD domain coding 3 (HDDC3) gene is a cytosolic nicotinamide adenine dinucleotide phosphatase– coding gene that regulates ferroptosis, involved in the development of polycystic ovary syndrome (PCOS). The syndecan 2 (SDC2) gene is a heparan sulfate proteoglycan–coding gene that facilitates T-cell proliferation. The downregulation of these 2 protein-coding genes is hypothesized to be associated with PCOS and immune dysregulation during PCOS. Hence, a study was conducted to identify the expression of HDDC3 and SDC2 biomarkers in PCOS and validate their association with immune dysregulation in PCOS. Screening and Identification Strategy Five datasets (3 microarray datasets and 2 RNA sequencing datasets) were selected for the analysis. The microarray datasets contained granulosa cell (GC) samples from 19 patients with PCOS and 15 controls. The RNA sequencing datasets contained GC samples from 7 patients with PCOS and 7 controls. The datasets were subjected to the identification of differentially expressed genes and functional enrichment analytical assays. The Least Absolute Shrinkage and Selection Operator logistic regression model and the Support Vector Machine Recursive Feature Elimination algorithm were used to identify the target biomarkers. The HDDC3 and SDC2 biomarkers identified in this study showed > 0.8 area under the curve, indicating their potential predictive abilities in PCOS. Differential expression studies showed significantly low expression of HDDC3 and SDC2 in the GC samples of patients with PCOS compared with that in the control groups. HDDC3 and SDC2 Potential Biomarkers in the Diagnosis of Polycystic Ovary Syndrome Validation of the biomarkers in clinical samples The expression of HDDC3 and SDC2 biomarkers in clinical GC samples from 5 patients with PCOS and 5 controls was verified using quantitative reverse transcription polymerase chain reaction. It was identified that HDDC3 and SDC2 were significantly downregulated in GC samples of patients with PCOS compared with that of controls, thereby showing consistency with the bioinformatics analysis results. Immune Cell Infiltration Analysis The infiltration of immune cells around GCs in PCOS was predicted using the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts algorithm. The resting and memory CD4 T cells and neutrophils dominated in both PCOS and control groups. A significant difference was detected in the infiltration of activated mast cells and eosinophils between the groups, indicating the potential role of mast cells and eosinophils in the pathogenesis of PCOS. Relationship of biomarkers with infiltrating immune cells A correlation analysis revealed a positive correlation of HDDC3 with T-regulatory cells, activated mast cells, and monocytes, but a negative correlation with activated memory CD4 T cells; and a positive correlation of SDC2 with activated mast cells, plasma cells, and M2 macrophages, but a negative correlation with eosinophils and neutrophils. Thus, HDDC3 and SDC2 were identified as potential biomarkers of PCOS and their potential interactions with immune cells during the pathogenesis of PCOS were determined. Source: Na Z, et al. J Ovarian Res. 2022;15:80.
RkJQdWJsaXNoZXIy MjAwNDg=