Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.

ABSTRACT: Atherosclerosis (AS) is a predominant etiological factor in numerous cardiovascular diseases, with its associated complications such as myocardial infarction and stroke serving as major contributors to worldwide mortality rates. Here, we devised dependable AS-related biomarkers through the utilization of single-cell RNA sequencing, weighted co-expression network (WGCNA), and differential expression analysis. Furthermore, we employed various machine learning techniques (LASSO and SVM-RFE) to enhance the identification of AS biomarkers, subsequently validating them using the GEO dataset. Following this, CIBERSORT was employed to investigate the correlation between biomarkers and infiltrating immune cells. Consequently, 256 differentially expressed genes (DEGs) were selected in samples of AS

SUBMITTER: Yong X 

PROVIDER: S-EPMC11880100 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature