Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy.

ABSTRACT: This study looked at possible targets for hypertrophic cardiomyopathy (HCM), a condition marked by thickening of the ventricular wall, primarily in the left ventricle. We employed differential gene analysis and weighted gene co-expression network analysis (WGCNA) on samples. We then carried out an enrichment analysis. We also investigated the process of immunological infiltration. We employed six machine learning techniques and two protein-protein interaction (PPI) network gene selection approaches to search for the most characteristic gene (MCG). In the validation ladder, we verified the expression of MCG. Furthermore, we examined the MCG expression levels in HCM animal and cell models. Finally, we performed molecular docking and predicted potential medications for HCM treatment. 7975 dif

SUBMITTER: Chen JL 

PROVIDER: S-EPMC12041389 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature