Objective:This study aims to investigate the mechanisms underlying the role of
chromatin regulator-related genes (CRRGs) in coronary artery disease (CAD) and develop a diagnostic
model for CAD.Methods:We downloaded CAD datasets from the GEO database and utilized R software for
machine learning, modeling, and classification of CAD based on CRRGs.Results:The random forest model was found to be the best approach, identifying USP44,
MOCS1, SSRP1, ZNF516, and SCML1 as the top contributing genes for CAD diagnosis and
prevention. Differentially expressed CRRGs were associated with aberrant immune cell infiltration
in CAD patients. CAD patients were classified into two subtypes based on the expression of
differentially expressed CRRGs. The differential expression analysis identified MMP9, LCE1D,
LOC92659, SYNGR4, EN2, CACNA1E, GPR78, and LOC92249 as differentially expressed
genes distinguishing the two subtypes of CAD. Functional analyses revealed that the differentially
expressed genes are enriched in biological processes related to cellular functions, such as responses
to metal ions and inorganic substances. The enriched pathways included inflammation
and hormone-related pathways, such as IL-17 signaling, endocrine resistance, TNF signaling,
and estrogen signaling pathways.Conclusion:CAD is associated with CRRGs, which may represent a new direction for CAD
treatment.