Exploring Hypertrophic Cardiomyopathy Biomarkers through Integrated Bioinformatics Analysis: Uncovering Novel Diagnostic Candidates

Abstract

HCM is a heterogeneous monogenic cardiac disease that can lead to arrhythmia, heart failure, and atrial fibrillation. This study aims to identify biomarkers that have a positive impact on the treatment, diagnosis, and prediction of HCM through bioinformatics analysis. We selected the GSE36961 and GSE180313 datasets from the Gene Expression Omnibus (GEO) database for differential analysis. GSE36961 generated 6 modules through weighted gene co‐expression network analysis (WGCNA), with the green and grey modules showing the highest positive correlation with HCM (green module: cor = 0.88, p = 2e − 48; grey module: cor = 0.78, p = 4e − 31). GSE180313 generated 17 modules through WGCNA, with the turquoise module exhibiting the highest positive correlation with HCM (turquoise module: cor = 0.92, p = 6e − 09). We conducted GO and KEGG pathway analysis on the intersection genes of the selected modules from GSE36961 and GSE180313 and intersected their GO enriched pathways with the GO enriched pathways of endothelial cell subtypes calculated after clustering single‐cell data GSE181764, resulting in 383 genes on the enriched pathways. Subsequently, we used LASSO prediction on these 383 genes and identified RTN4, COL4A1, and IER3 as key genes involved in the occurrence and development of HCM. The expression levels of these genes were validated in the GSE68316 and GSE32453 datasets. In conclusion, RTN4, COL4A1, and IER3 are potential biomarkers of HCM, and protein degradation, mechanical stress, and hypoxia may be associated with the occurrence and development of HCM.