Unveiling miRNA biomarkers for hypertrophic cardiomyopathy through integrated bioinformatics and machine learning analysis

This study explores microRNAs (miRNAs) as biomarkers for hypertrophic cardiomyopathy (HCM), an inherited cardiac disease with clinical diversity and sudden death risk. Using bioinformatics and machine learning (ML), Gene Expression Omnibus (GEO) datasets were analysed to identify miRNA signatures for early detection, risk assessment, and personalised treatment of HCM. Differential expression analysis of three GEO datasets identified 155 differentially expressed genes (DEGs) and 5 differentially expressed miRNAs (DE-miRNAs). Functional annotation and pathway analysis revealed their roles in inflammatory responses, extracellular matrix organisation, and cellular stress responses. Notably, upregulated (COL21A1, PROM1) and downregulated (FOS, BTG2, ELL2, PDK4, SERPINE1, SRGN, TIPARP) genes were detected as potential DE-miRNA targets. Validation highlighted importance of ELL2 and PDK4 in HCM pathology. Support Vector Machine (SVM) and Random Forest (RF) models demonstrated high predictive accuracy for HCM using DE-miRNAs, suggesting new paths for early diagnosis and personalised therapy.