Deciphering the Molecular Mechanisms of Polycystic Ovary Syndrome and Flaxseed Therapy Through Transcriptomics and Machine Learning

Abstract

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder characterised by heterogeneous clinical and molecular phenotypes. Flaxseed, widely used in traditional Chinese medicine and as a nutritional supplement, has shown promising therapeutic potential for PCOS. In this study, we integrated transcriptomic data with machine learning-based analytical approaches and network pharmacology to investigate the molecular mechanisms underlying PCOS and to identify the potential targets and pathways modulated by flaxseed. Differentially expressed genes (DEGs) and PCOS-related targets were systematically identified from GEO, GeneCards and DisGeNet databases. Bioactive compounds in flaxseed were predicted using TCMSP, SwissTargetPrediction and INPUT2.0. Functional and pathway enrichment analyses were conducted to explore mechanistic insights. Core targets were prioritised using Centiscape network topology parameters and LASSO regression, followed by molecular docking validation using AutoDock. Our results revealed that flaxseed's therapeutic action may primarily involve modulation of immune regulation, insulin signalling, apoptosis and inflammation pathways. Key active compounds, notably β-sitosterol and stigmasterol, exhibited strong binding affinities with critical targets, such as IL1B, GSK3B and HMGCR, suggesting potential anti-inflammatory and antioxidant effects. The findings provide a theoretical foundation for future experimental studies and support the development of flaxseed-based therapeutic strategies for PCOS through precision medicine frameworks.