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

Summary

This study integrates bulk and single-cell transcriptomics with machine learning to identify CCL4 and five other genes as potential diagnostic biomarkers for acute myocardial infarction, revealing immune-metabolic crosstalk and cell-type-specific expression patterns.

Abstract

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.

Introduction

Acute myocardial infarction (AMI), caused by sustained myocardial ischemia, is among the most lethal cardiovascular diseases worldwide¹˒². Its pathogenesis is multifactorial, with major contributors including atherosclerosis, thrombosis, coronary artery spasm, and arrhythmias³˒⁴. Hypertension, diabetes, hyperlipidemia, smoking, obesity, and psychological stress are also recognized as important risk factors⁵. Although advances in interventional techniques, antiplatelet therapies, and thrombolytic strategies have markedly improved short-term ....

Protocol

The study was conducted in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of Anhui Public Health Clinical Center on September 19, 2025 (approval ID: PJ-YX2025-062). Written informed consent was obtained from all participants before blood collection. The local cohort included eight patients with acute myocardial infarction (AMI) and eight healthy controls. The research tools used in the protocol are listed in the Table of Materials.

1. Data sources and processing

Bulk RNA sequencing datasets related to myocardial infarction were obtain....

Results

Identification and enrichment analysis of calmodulin-related genes associated with AMI
Batch-effect correction was first performed on the GSE59867 dataset to ensure cross-sample comparability (Figure 1A). A total of 168 differentially expressed genes (DEGs) were subsequently identified, including 77 upregulated and 91 downregulated genes in AMI samples compared with controls (Figure 1B). Single-sample gene set enrichment analysis indicated t.......

Discussion

Acute myocardial infarction (AMI) results from acute coronary artery occlusion, leading to myocardial ischemic necrosis and high morbidity and mortality worldwide. Calmodulin, a calcium-dependent regulatory protein, plays a pivotal role in intracellular signal transduction. Previous studies have demonstrated that calmodulin regulates cardiomyocyte apoptosis, inflammatory responses, and calcium homeostasis during AMI, suggesting that it may be an important molecule in AMI pathogenesis²⁰. In the pre.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
β-actin primer	Tsingke	N/A	Forward: 5′-CATGTACGTTGCTATCCAGGC-3′ Reverse: 5′-CTCCTTAATGTCACGCACGAT-3′
calibrate	rms	N/A	Parameters: lrmModel, method = "boot", B = 1000
CB-Dock2	CB-Dock2 Server	Online tool	https://cadd.labshare.cn/cb-dock2/
CCL4 primer	Tsingke	N/A	Forward: 5′-CTGTGCTGATCCCAGTGAATC-3′ Reverse: 5′-TCAGTTCAGTTCCAGGTCATACA-3′
CIBERSORT	Stanford University	Online tool	Parameters: function(sig_matrix, mixture_file, perm = 0, QN = TRUE)Spearman correlation analysis was performed between immune-cell abundance and gene expression.
clusterProfiler	Bioconductor	4.12.6	https://bioconductor.org/packages/clusterProfiler
Cytoscape	Cytoscape Consortium	3.9.1	https://cytoscape.org/
Drug-Gene Interaction Database	DGIdb	Online database	https://www.dgidb.org/
Gene Expression Omnibus	NCBI	Online database	https://www.ncbi.nlm.nih.gov/geo/
Gene Ontology database	Gene Ontology Consortium	Online database	http://geneontology.org/
GeneMANIA	University of Toronto	Online tool	https://genemania.org/
GBP4 primer	Tsingke	N/A	Forward: 5′-AGGCTGCTAAAACACAAGCTG-3′ Reverse: 5′-CCCCAGGTAGAGTGACAATCAT-3′
ggalluvial	CRAN	0.12.5	Sankey diagram generation
GSVA	Bioconductor	1.52.3	Parameter: P < 0.05
Harmony integration framework	CRAN	1.2.0	https://github.com/immunogenomics/harmony
Hifair III 1st Strand cDNA Synthesis SuperMix	YEASEN	11141ES10	Reverse-transcription reagent
Hieff qPCR SYBR Green Master Mix	YEASEN	11201ES	qPCR reagent
Human Protein Atlas	Human Protein Atlas Consortium	Online database	https://www.proteinatlas.org/
KPNA5 primer	Tsingke	N/A	Forward: 5′-TCAAGAGATGCGTAGACGAAGA-3′ Reverse: 5′-ACATTTCTGCGTTTGAACAACTG-3′
Kyoto Encyclopedia of Genes and Genomes database	Kanehisa Laboratories	Online database	https://www.kegg.jp/
LightCycler 480 Instrument II	Roche	LightCycler 480 II	Real-time PCR instrument
limma	Bioconductor	3.60.6	Differentially expressed gene screening
lrm	rms	N/A	Parameters: Type ~ SOCS3 + GBP4 + ST14 + KPNA5 + STAB1 + CCL4, data = aSAH, maxit = 100
miRanda	MicroRNA.org	Online tool	http://www.microrna.org/microrna/home.do
miRDB	miRDB	Online tool	http://mirdb.org/
miRTarBase	National Chiao Tung University	Online database	https://mirtarbase.cuhk.edu.cn/
Protein Data Bank	RCSB	Online database	https://www.rcsb.org/
PubChem	NCBI	Online database	https://pubchem.ncbi.nlm.nih.gov/
PyMOL	Schrödinger, LLC	2.5.4	Visualization of molecular docking results
pROC	CRAN	1.18.5	Receiver operating characteristic curve generation and visualization
Random forest package	CRAN	3.3.1	Parameters: ntree = 100, seed = 200
R software	R Foundation	4.2.2	Bioinformatics and statistical analyses
regplot	CRAN	1.1	Parameters: glm(Status ~ gene, family = binomial(), data = merged_data)lrm(Type ~ SOCS3 + GBP4 + ST14 + KPNA5 + STAB1 + CCL4, data = aSAH, maxit = 100)
rmda	CRAN	1.6	Parameters: decision_curve(Type ~ SOCS3 + GBP4 + ST14 + KPNA5 + STAB1 + CCL4, data = aSAH, family = binomial(link = "logit"), thresholds = seq(0, 1, by = 0.01), confidence.intervals = 0.95)
RNA isolater MolPure Blood RNA Kit	YEASEN	19241ES50	RNA isolation kit
Seurat	Satija Lab	5.1.0	Marker genes used for cell annotation:Monocytes: FCN1, S100A9, S100A8B cells: CD79B, MS4A1T cells: LDHB, CD4, CD3DNK cells: FGFBP2, GZMB, NKG7, KLRD1Megakaryocytes: CD163, CD36, PF4, PPBPBlood cells: ALAS2, AHSP, CA1Plasma cells: IGHA1, CD79A, MZB1, JCHAINDendritic cells: HLA-DMB, HLA-DPA1, HLA-DQB1Mast cells: FCER1A, MS4A2
SOCS3 primer	Tsingke	N/A	Forward: 5′-CCTGCGCCTCAAGACCTTC-3′ Reverse: 5′-GTCACTGCGCTCCAGTAGAA-3′
spongeScan	spongeScan	Online tool	http://spongescan.rc.ufl.edu/
ST14 primer	Tsingke	N/A	Forward: 5′-TTCCTGCCAGTCAACAACGTC-3′ Reverse: 5′-GGTACTGCAAATGCCACACC-3′
STAB1 primer	Tsingke	N/A	Forward: 5′-CCGGGAAATCCTTACCACAGC-3′ Reverse: 5′-ACCTTCGTGTTTGTTGGGTCC-3′
Support vector machine package	CRAN	1.7-13	Parameters: input, k = 10, halve.above = 100
survminer	CRAN	0.5.0	Survival curve plotting
TargetScan	TargetScan	Online tool	http://www.targetscan.org/
UniProt	UniProt Consortium	Online database	https://www.uniprot.org/
Weighted gene co-expression network analysis package	CRAN	1.73	Parameters: R² > 0.8, minModuleSize = 200
XGBoost package	CRAN	1.7.8.1	Parameters: nrounds = c(50, 200), max_depth = c(3, 8), eta = c(0.01, 0.3)