Tryptophan Metabolism-Related Biomarkers in Acute Myocardial Infarction Identified by Machine Learning.

Abstract

Emerging evidence suggests that tryptophan metabolism (TrM) is dysregulated in acute myocardial infarction (AMI), but the underlying mechanisms remain unclear. In this study, we integrated weighted gene co-expression network analysis, differential expression analysis, and four machine learning algorithms to identify key TrM-related genes in AMI. The diagnostic model was further validated by quantitative polymerase chain reaction (qPCR), and functional enrichment, immune infiltration, molecular docking, molecular dynamics simulation, and single-cell analyses were performed to investigate the potential mechanisms and therapeutic targets. Five candidate genes were identified, among which ADM, MCEMP1, and TSPO were selected to construct a diagnostic model. Immune infiltration analysis revealed that monocytes and neutrophils were closely associated with AMI progression and correlated with these key genes. Molecular docking and molecular dynamics simulations demonstrated a stable interaction between TSPO and ONO-2952. Furthermore, single-cell and SCENIC analyses identified monocytes as key cell populations and TFEC and CEBPD as potential transcriptional regulators of key gene expression. Collectively, this study provides a comprehensive characterization of TrM-related molecular alterations in AMI and identifies ADM, MCEMP1, and TSPO as potential diagnostic key genes, offering new insights into disease mechanisms and candidate targets for future therapeutic investigation.