Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene.

ABSTRACT:

Background

Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and acts as a major contributor to cardiovascular diseases. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in AS, remain largely unexplored and are a subject of ongoing research.

Methods

This study employed advanced bioinformatics techniques to identify and validate FAMGs associated with AS. We conducted differential expression analysis on a select list of 49 candidate FAMGs. GSEA and GSVA were utilized to elucidate the

SUBMITTER: Li H 

PROVIDER: S-EPMC11742484 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature