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

Author

Listed:
  • Lelin Jiang
  • Minghao Jiang
  • Yiying Liu
  • Wei Zhao
  • Xinlang Zhou
  • Ying Liu
  • Shue Huang
  • Lina Chen
  • Wenbing Jiang

Abstract

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

Suggested Citation

  • Lelin Jiang & Minghao Jiang & Yiying Liu & Wei Zhao & Xinlang Zhou & Ying Liu & Shue Huang & Lina Chen & Wenbing Jiang, 2025. "Immunophenotyping identifies key immune biomarkers for coronary artery disease through machine learning," PLOS ONE, Public Library of Science, vol. 20(8), pages 1-23, August.
    • Handle: RePEc:plo:pone00:0328811
    DOI: 10.1371/journal.pone.0328811
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