CCL4 as a Potential Immune-Metabolic Biomarker in Acute Myocardial Infarction via Machine Learning and Single-Cell...

Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide, highlighting the need for novel complementary biomarkers. By integrating bulk and single-cell transcriptomic data with machine learning approaches, calmodulin-related genes associated with AMI and explored their immune-metabolic features were identified. Differential expression and weighted co-expression analyses revealed 60 calmodulin-related genes, from which six key genes (SOCS3, GBP4, ST14, KPNA5, STAB1 and CCL4) were screened using multiple machine learning algorithms and validated in independent datasets. Functional analyses indicated enrichment in immune, inflammatory, and metabolic pathways. Immune infiltration and single-cell transcriptomics showed cell-type-specific expression patterns, with CCL4 predominantly expressed in T and NK cells and markedly reduced in AMI samples. qPCR validation confirmed significant expression changes for four of the six genes in the local cohort. Drug-gene interaction and docking analyses suggested candidate compounds for further investigation. Collectively, the findings suggest that CCL4 may serve as a potential diagnostic biomarker for AMI, and the observed immune-metabolic associations provide a basis for future mechanistic and translational studies.