Integration of multi-omics and machine learning strategies identifies immune related candidate biomarkers in inflammation-associated hypertrophic cardiomyopathy.

ABSTRACT:

Background

Hypertrophic cardiomyopathy (HCM) is a common inherited heart disease frequently leading to heart failure. Although sarcomeric gene mutations are known, they only account for a subset of cases. The role of immune dysregulation in HCM progression has gained increasing attention, necessitating the exploration of immune-related biomarkers and therapeutic targets. This study integrates Mendelian randomization (MR), transcriptomics, machine learning, and experimental validation to investigate the immune mechanisms underlying HCM.

Methods

We analyzed three transcriptomic datasets from the GEO database (210 healthy controls, 152 HCM patients) and identified differentially expressed genes (DEGs) using the R package limma. MR analysis was performed on 19,942 expression qua

SUBMITTER: Liang Q 

PROVIDER: S-EPMC12510942 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature