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.

本研究探讨了微小核糖核酸（microRNAs，miRNAs）作为肥厚型心肌病（hypertrophic cardiomyopathy，HCM）生物标志物的潜力——肥厚型心肌病是一类兼具临床异质性与猝死风险的遗传性心脏疾病。本研究借助生物信息学与机器学习（machine learning，ML）技术，对基因表达综合数据库（Gene Expression Omnibus，GEO）的数据集展开分析，以筛选可用于肥厚型心肌病早期检测、风险评估与个体化治疗的miRNA特征谱。通过对3个GEO数据集进行差异表达分析，共鉴定出155个差异表达基因（differentially expressed genes，DEGs）与5个差异表达微小核糖核酸（differentially expressed miRNAs，DE-miRNAs）。功能注释与通路分析揭示了这些分子在炎症反应、细胞外基质组织以及细胞应激反应中发挥的调控作用。值得注意的是，本研究检测到上调表达基因COL21A1、PROM1与下调表达基因FOS、BTG2、ELL2、PDK4、SERPINE1、SRGN、TIPARP可作为DE-miRNA的潜在靶标。验证实验证实了ELL2与PDK4在肥厚型心肌病病理进程中的关键作用。基于DE-miRNAs构建的支持向量机（Support Vector Machine，SVM）与随机森林（Random Forest，RF）模型对肥厚型心肌病展现出优异的预测准确率，为该疾病的早期诊断与个体化治疗提供了新的研究方向。