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 (tu

SUBMITTER: Li G 

PROVIDER: S-EPMC11239233 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature