Immunophenotyping identifies key immune biomarkers for coronary artery disease through machine learning.

INTRODUCTION

The differences among immune subtypes in coronary artery disease (CAD), their interrelationships, and the associated immune biomarkers remain incompletely understood.

METHODS

The samples were collected from the GSE20686 and GSE42148 datasets for analysis. Principal component analysis (PCA) and Gene Set Variation Analysis (GSVA) were performed on the subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to determine functional and pathways in CAD. Machine learning models were constructed for CAD prediction. Model validation was performed using GSE56885 and GSE71226 datasets. The expression and function of the identified genes were evaluated using immunohistochemistry, CCK-8 assays, wound healing assays, and Transwell invasion assays.

RESULTS

Multiple immune cells showed correlations with CAD samples. Two immune cell subtypes were identified, with significant differences in programmed cell death-ligand (PD-L1) expression, immune scores, and stromal scores between subtypes (P < 0.05). Three CAD hub genes were identified by WGCNA. GO analysis revealed enrichment in Biological Process (BP) and Molecular Function (MF). Among the several machine learning models, the RF model was selected based on combining parameters. The model mainly included two CAD immune marker genes, AKT1 and PTK2B. Differential expression of AKT1 and PTK2B was observed in cardiac myocytes. Inhibition of PTK2B suppressed cell proliferation and invasion, and induced apoptosis in HUVEC cells.

CONCLUSION

Immunophenotyping revealed an association between CAD and PD-L1. AKT1 and PTK2B were identified as key disease signature genes, which may hold clinical significance for the diagnosis, prognostic assessment and treatment of CAD.