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

Summary

The protocol systematically identifies tryptophan metabolism-related genes in acute myocardial infarction using integrated bioinformatics, machine learning, and experimental validation, providing a framework for key gene discovery and mechanistic investigation.

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.

Introduction

Acute myocardial infarction (AMI) occurs when coronary blood flow is partially or completely interrupted, resulting in myocardial ischemia and irreversible injury¹. Clinical studies have demonstrated that multiple pathological processes, including coronary atherosclerosis, plaque rupture, and thrombosis, contribute to the development of AMI². Furthermore, AMI-related complications, including malignant arrhythmias and cardiogenic shock, remain major contributors to mortality among patients with cardiovascular diseases³. Current diagnostic methods for AMI have limited value in identifying patients w....

Protocol

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Wannan Medical College Second Affiliated Hospital (Approval No. WYEFYLS2025033). Anonymized clinical samples were analyzed in this study, and the ethics committee waived the need for informed consent.

Data source and preprocessing

We obtained AMI datasets (Table 1) from Gene Expression Omnibus (GEO), including GSE59867¹⁵ (training), GSE123342¹⁶ (validation), and scRNA-seq dataset GSE269269^1....

Results

Identification of 31 overlapping genes in AMI

Comparison of intergroup differences in the training set revealed a significant difference in TMRGs scores between the AMI and non-AMI groups (p = 0.0008; Figure 1A), indicating that the training set is suitable for subsequent WGCNA. Following this, quality control confirmed the absence of outliers (Figure 1B). Subsequently, an optimal soft threshold of 24 was identified (.......

Discussion

AMI is one of the leading causes of death worldwide and remains a major public health challenge³⁴. Previous studies have reported alterations in TrM in patients with AMI³⁵. However, the underlying molecular mechanisms remain incompletely understood. By integrating TMRG analysis with machine-learning approaches and single-cell transcriptomic profiling, we identified three key genes (ADM, MCEMP1, and TSPO) associated with AMI. Further analyses revealed significant association.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Bio-Rad PCR system	Bio-Rad Laboratories, Inc.	CFX96	Quantitative PCR amplification
CB-Dock2	Sichuan University	https://cadd.labshare.cn/cb-dock2/php/index.php	Molecular docking
clusterProfiler	R Foundation for Statistical Computing	Version 4.7.1.003	GSEA, GO and KEGG enrichment analysis
Cytoscape	Cytoscape Consortium	Version 3.9.1	Draw network diagram
Drug-Gene Interaction Database	Washington University School of Medicine / DGIdb Consortium	https://dgidb.org	Prediction of drugs targeting biomarkers
Gaussian	Gaussian, Inc.	Version 6	charge calculation
GeneMANIA	University of Toronto	http://www.genemania.org/	Construction of functional association network
GEO Database	NCBI	https://www.ncbi.nlm.nih.gov/geo/	Transcriptome data source
GraphPad Prism	GraphPad Software, LLC	Version 10.1.2	qPCR data processing
Hieff® qPCR SYBR Green Master Mix (ROX-free)	GOONIE	500102	Quantitative PCR
limma	R Foundation for Statistical Computing	Vesion 3.62.1	Differential expression analysis
miRanda	Memorial Sloan Kettering Cancer Center	http://mirtoolsgallery.tech/mirtoolsgallery/node/1055	miRNA prediction
MSigDB	Broad Institute	https://www.gsea-msigdb.org/gsea/msigdb	GSEA analysis
multiMiR	R Foundation for Statistical Computing	Vesion 1.20.0	Construction of ceRNA network
NanoDrop 2000 spectrophotometer	Thermo Fisher Scientific	NanoDrop 2000	Detection of RNA concentration and purity
PDB	RCSB Protein Data Bank	https://www.rcsb.org/	Download the 3D structure of proteins for biomarkers
PubChem	National Center for Biotechnology Information (NCBI), NIH	https://pubchem.ncbi.nlm.nih.gov/	Download the molecular structures of drugs
Primers	Tsingke Biotechnology Co., Ltd.	N/A	Custom synthesis of qPCR primers
R software	R Foundation for Statistical Computing	Version 4.2.2	Statistical analysis and visualization
Reverse transcriptase	Yugong Bio	EG15133S	Reverse transcription
RNA Isolater Total RNA Extraction Reagent	Vazyme Biotech Co., Ltd.	R401-01	Total RNA extraction from whole blood
Seurat	R Foundation for Statistical Computing	Version 4.3.0	Single-cell transcriptomic analysis
StarBase	Shanghai Center for Bioinformation Technology	https://rnasysu.com/encori/	lncRNA prediction
STRING Database	STRING Consortium	https://string-db.org/	PPI network construction
Sobtop	N/A	Vesion 1.0	GAFF parameter generation