Untangling hidden players of PCOS: From transcriptomics to key biomarkers using WGCNA, LASSO and ROC analysis

Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder characterized by hormonal imbalance, inflammation and insulin resistance. This study utilized a comprehensive RNA-Seq workflow to uncover the molecular mechanisms underlying PCOS. Data were retrieved from the NCBI- Sequence Read Archive [SRA] repository, followed by quality assessment using FastQC and Trimmomatic trimming. Reads were aligned to the GRCh38 genome using HISAT2 and differential expression analysis was performed by DESeq2 in R, identifying 395 significant DEGs (268 upregulated, 127 downregulated). Gene ontology (GO) analysis revealed dysregulated biological processes including activation of protein kinase activity, telomerase capping, protein serine/ threonine kinase activity and MAP kinase kinase activity, while KEGG pathway analysis highlighted key signaling pathways such as MAPK signaling, Ras signaling, and GnRH signaling. Weighted gene co-expression network analysis (WGCNA) identified modules associated with PCOS traits and Protein-Protein Interaction (PPI) network analysis identified 20 hub genes. Through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, five key predictor genes - PRKACA, SREBF2, MAPT, EHMT1, and JUP were identified, highlighting their critical roles in the pathogenesis of PCOS. The prognostic value of these genes was further validated by receiver operating characteristics (ROC) curve analysis, which showed a high area under the curve (AUC) of 0.89. This strong discriminatory power between PCOS and healthy groups highlights the potential of these biomarkers as reliable indicators for diagnosing and understanding the disease. The results highlight the therapeutic and diagnostic potential of these biomarkers and pave the way for precision medicine approaches in PCOS treatment.