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Untangling hidden players of PCOS: From transcriptomics to key biomarkers using WGCNA, LASSO and ROC analysis
Harshini Senthilkumar1, Mohanapriya Arumugam1
1Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore- 632014, Tamil Nadu, India.
Summary
This study identifies five key genes (PRKACA, SREBF2, MAPT, EHMT1, JUP) that are critical predictors for diagnosing polycystic ovary syndrome (PCOS). These biomarkers show high accuracy, offering potential for precision medicine in PCOS treatment.
Area of Science:
- Endocrinology
- Genomics
- Metabolomics
Background:
- Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder.
- PCOS is characterized by hormonal imbalance, inflammation, and insulin resistance.
Purpose of the Study:
- To uncover the molecular mechanisms underlying PCOS using a comprehensive RNA-Seq workflow.
- To identify key predictor genes for PCOS diagnosis and understanding its pathogenesis.
Main Methods:
- RNA-Seq data analysis from NCBI SRA repository.
- Differential gene expression analysis using DESeq2 and WGCNA.
- LASSO regression and ROC curve analysis to identify and validate key biomarkers.
Main Results:
- Identified 395 significant differentially expressed genes (DEGs) in PCOS.
- Discovered dysregulated biological processes (e.g., kinase activity) and signaling pathways (e.g., MAPK, GnRH).
- Validated five key predictor genes (PRKACA, SREBF2, MAPT, EHMT1, JUP) with high diagnostic accuracy (AUC=0.89).
Conclusions:
- The identified genes serve as reliable biomarkers for PCOS diagnosis.
- These findings support the potential for precision medicine approaches in PCOS treatment.
- Highlights therapeutic and diagnostic potential of novel biomarkers for PCOS.
