Tryptophan metabolism-related biomarkers ADM, MCEMP1, and TSPO in acute myocardial infarction: insights from bioinformatics and machine learning

Tryptophan metabolism-related biomarkers ADM, MCEMP1, and TSPO in acute myocardial infarction: insights from bioinformatics and machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tryptophan metabolism-related biomarkers ADM, MCEMP1, and TSPO in acute myocardial infarction: insights from bioinformatics and machine learning Lei Wang, Chengmin Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8783966/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Emerging evidence indicates that intermediates of tryptophan metabolism were decreased in acute myocardial infarction (AMI) patients. However, underlying mechanisms linking tryptophan metabolism to AMI pathogenesis remain poorly characterized. This study systematically investigates the role of tryptophan metabolism in AMI through multi-omics integration. Methods Tryptophan metabolism-related genes (TMRGs) were retrieved from the MSigDB database and analyzed using weighted gene co-expression network analysis and differential expression analysis to identify AMI-associated candidates. Four machine-learning algorithms (LASSO regression, XGBoost, RF, and SVM-RFE) were applied to screen biomarkers and construct a diagnostic model, which was subsequently validated by qPCR. Gene set enrichment, immune infiltration, and regulatory network analyses were performed to elucidate biomarker functions. Molecular docking identified potential target drugs, followed by 100 ns molecular dynamics simulations of drug molecules and target proteins using the GAFF force field. Single-cell data were employed to identify key cell populations and transcriptional regulators. Results Five candidate biomarkers were identified, among which ADM, MCEMP1, and TSPO were selected to establish a diagnostic model with potential clinical utility. Immune infiltration analysis implicated monocytes and neutrophils in AMI progression and demonstrated their significant correlation with these biomarkers. Molecular docking revealed a strong binding affinity between TSPO and ONO-2952, which was confirmed as stable by molecular dynamics simulations. Single-cell and SCENIC analyses further highlighted monocytes as central players in AMI and identified TFEC and CEBPD as key transcription factors regulating biomarker expression. Discussion Our findings suggest that dysregulation of tryptophan metabolism contributes to AMI progression mainly through immune cell activation and inflammatory remodeling. The identified biomarkers-ADM, MCEMP1, and TSPO-may bridge metabolic disturbances and immune dysfunction, providing mechanistic insights into AMI pathology. Furthermore, the interaction between TSPO and ONO-2952 highlights the therapeutic potential of targeting metabolic-immune crosstalk in cardiovascular disease. Conclusion This study comprehensively investigates the association between tryptophan metabolism and acute myocardial infarction, identifying three biomarkers and two therapeutic targets. Our findings provide a novel perception of AMI pathogenesis and give to the diagnosis of AMI. Tryptophan metabolism Acute myocardial infarction Machine learning Immunity Molecular dynamics Single-cell transcriptomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Acute myocardial infarction (AMI) is caused by an insufficient or total interruption of coronary artery blood flow due to various elements, leading to severe and persistent AMI 1 . Clinical studies have indicated that many risk elements increase the risk of AMI, such as myocardial fibrosis, coronary artery atherosclerosis, and thrombosis 2 . Besides, the primary cause of death from cardiovascular disease is malignant arrhythmias and cardiogenic shock caused by AMI 3 . Currently, several diagnostic methods are available for AMI. Electrocardiography can detect the occurrence and progression of AMI, but this method may have limited diagnostic value for early-stage AMI patients 4 , 5 . Furthermore, changes in high-sensitivity troponin levels are also used for AMI diagnosis, but this method is prone to false-positive results, leading to inaccurate diagnoses 6 . Recent studies demonstrate that serum levels of tryptophan (Trp) and its metabolites in AMI patients differ significantly compared with healthy controls, suggesting their potential as novel biomarkers 7 . Tryptophan is a vital amino acid that the human body cannot synthesize on its own. It plays a key role in protein synthesis and the synthesis of various active compounds 8 . Increasing evidence showed that tryptophan metabolism (TrM) regulates multiple physiological processes including immune function and gut microbiome balance 9 – 11 . The 5-hydroxytryptamine (5-HT), indole, and kynurenine (Kyn) pathways collectively represent the TrM 12 . Among these, the Kyn pathway plays a dominant role, with its metabolites capable of regulating inflammatory responses and promoting myocardial cell apoptosis, thereby increasing the risk of AMI 13 , 14 . Therefore, identifying new diagnostic markers for AMI based on tryptophan metabolism and its metabolites could both improve early AMI diagnosis accuracy and offer new strategies for personalized therapy. In this study, we systematically explored the role of the tryptophan metabolic pathway in AMI by integrating bulk and single-cell RNA-seq data. First, candidate genes were screened using differential expression analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, four machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE), were used to further screen biomarkers, and their expression levels in clinical samples were validated by qPCR. Based on these biomarkers, we constructed and validated a diagnostic model. Furthermore, enrichment analysis, immune cell infiltration analysis, and regulatory networks construction explored the potential mechanisms of these biomarkers in AMI. Finally, we identified key cell populations and transcription factors associated with AMI through single-cell RNA sequencing analysis. Overall, our findings provide new insights into the precise diagnosis and personalized treatment of AMI. The investigational flowchart is represented in Fig. 1 . 2. Materials and methods 2.1 Data source AMI-related datasets were downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ), which included training dataset GSE59867, validation dataset GSE123342, and single-cell RNA sequencing (scRNA-seq) dataset GSE269269. The detail information of datasets above is provided in Table 1 . Furthermore, a total of 50 tryptophan metabolism-related genes (TMRGs) were obtained from three gene sets, namely KEGG_TRYPTOPHAN_METABOLISM.v2024.1.Hs.gmt, REACTOME_TRYPTOPHAN_CATABOLISM.v2024.1.Hs.gmt, and WP_TRYPTOPHAN_METABOLISM.v2024.1.Hs.gmt, using the MSigDB database ( https://www.gsea-msigdb.org/gsea/msigdb ) (Table S1). Table 1 Information of datasets utilized in this research. Dataset Sample (controls/patients) Sequencing platform GSE59867 46/111 GPL6244 GSE123342 22/67 GPL17586 GSE269269 10 AMI patients GPL24676 2.2 Weighted gene co-expression network analysis To identify key gene modules, we constructed gene co-expression modules using the GSE59867 dataset. First, we used single-sample gene set enrichment analysis (ssGSEA) to compute the enrichment scores of samples and TMRGs in the dataset. TMRG scores were then incorporated as phenotypic features into subsequent WGCNA analysis. The optimal soft threshold (β = 24) was determined based on scale-free topology criteria (R 2 > 0.9). Using the topological overlap matrix (TOM) to assess the interaction strength between genes, we employed hierarchical clustering with average linkage and dynamic tree cutting (minimum module size = 50 genes) to screen co-expression modules. Key module exhibiting the strongest significant correlation with TMRG-scores (p < 0.05) was designated as the key module. Genes within this module were classified as WGCNA-TMRGs for downstream analysis. 2.3 Screening and functional enrichment analysis of candidate genes Differential expression analysis was employed by the R package limma (v 3.62.1) to screen differentially expressed genes (DEGs) in AMI samples and control samples 15 . The threshold for DEGs was set at p-value 0.5. Candidate genes were obtained by intersecting the DEGs with WGCNA-TMRGs through intersecting analysis. Functional enrichment analysis of candidate genes was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Separately, the STRING database ( https://cn.string-db.org/ ) was used to explore the interaction relationships between candidate genes. Interaction relationships with a confidence level ≤ 0.4 (medium confidence) were removed, and a protein-protein interaction network was constructed using Cytoscape (v 4.2.2). 2.4 Identification of biomarkers and construction of diagnostic models To further screen for biomarkers of AMI, we employed four machine learning algorithms, comprising least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE). We used 10-fold cross-validation to determine the regularization parameter (λ) for LASSO regression analysis and removed genes with zero coefficients. Feature importance was calculated using the XGBoost and RF algorithms, and the top 20 genes ranked by gain and Gini values were retained. For SVM-RFE, features with the lowest weights were iteratively eliminated to retain genes associated with the minimum cross-validation error. Next, we extracted overlapping key genes common to all four algorithms. To ensure robustness, we evaluated these genes’ expression levels in both training and validation datasets, retaining only genes with consistent trends as biomarkers. Subsequently, we developed a logistic regression-based diagnostic model for AMI to evaluate potential applicative value of biomarkers. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and confusion matrices were used to evaluate model performance. 2.5 Gene set enrichment analysis and construction of regulatory networks We employed gene set enrichment analysis (GSEA) using the R package clusteProfiler (v4.7.1.003) 16 to characterize the biological functions of AMI-associated biomarkers. Significantly enriched terms were selected with threshold of adjust p-value < 0.05. To identify potential regulatory interactions, we predicted miRNAs targeting the biomarkers using the microcosm database 17 and miRanda database ( http://mirtoolsgallery.tech/mirtoolsgallery/node/1055 ). Only miRNAs identified by both prediction methods were retained for subsequent analysis. Then, we predicted the lncRNAs based on the intersecting miRNAs using the StarBase database ( https://rnasysu.com/encori/ ). A comprehensive competing endogenous RNA (ceRNA) network was constructed using the R package multiMiR (v 1.20.0) 18 . Finally, we also utilized GeneMANIA ( http://www.genemania.org/ ) to forecast other genes associated with biomarkers and investigate the pathways in which they are collectively involved. 2.6 Drug prediction and immune infiltration analysis The Drug-Gene Interaction Database (DGIdb, https://dgidb.org ) was used to predict drugs targeting biomarkers, and the drug-biomarker interaction network was constructed using Cytoscape (v 3.9.1). Among these, the drug with the highest interaction score was selected for molecular docking. Incidentally, we performed molecular docking using the CB-Dock2 ( https://cadd.labshare.cn/cb-dock2/php/index.php ) to investigate the binding of drugs. Specifically, the 3D structures of proteins containing biomarkers were downloaded from the PDB database ( https://www.rcsb.org/ ), while the molecular structures of drugs were obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). Then, we conducted molecular docking analysis from the CB-Dock2. To explore the immune microenvironment in AMI, CIBERSORT and MCP-counter were conducted to quantifying infiltration levels of immune cell types. 2.7 Molecular dynamics simulation To assess the interaction strength and stability of the receptor–ligand complex, MD simulations were performed. The receptor was obtained from the PDB and parameterized using the Amber99SB-ILDN force field 19 . The ligand was protonated and pre-optimized in Avogadro 20 , followed by charge calculation in Gaussian (v6) and GAFF parameter generation via Sobtop (v1.0). The complex was solvated in a TIP3P triangular-prism water box with a 10 Å buffer 21 , and Na + /Cl − ions were added for neutralization. Energy minimization involved restrained solvent/ion relaxation followed by full-system minimization. Equilibration included 100 ps NVT heating (0-300 K) and 100 ps NPT density equilibration. A 100-ns production run was then conducted under NPT with a 2-fs step and trajectories saved every 10 ps. PME handled long-range electrostatics, SHAKE constrained hydrogen bonds 22 , and temperature/pressure were controlled by standard thermostat/barostat schemes. System stability was examined via root mean square deviation (RMSD), flexibility via root mean square fluctuation (RMSF), and compactness/solvent exposure using radius of gyration (Rg) and solvent-accessible surface area (SASA). 2.8 Single-cell data processing Single-cell transcriptomic analysis was primarily performed using the R package Seurat (v 4.3.0). Data quality control measures included nFeature_RNA less than 100 and larger than 5,000, nCount_RNA less than 100 and larger than 50,000, and the cell with a percentage of mitochondrial genes lower than 20%. Next, perform standardized analysis (standardization function) on the quality-controlled data and identify highly variable genes (FindVariableFeatures function). Dimensionality reduction was employed through principal component analysis (top 25 PCs). Batch-effect removal method was conducted by RunHarmony function. Subsequently, unsupervised clustering was conducted via the FindNeighbors, FindClusters, and RunUMAP functions to delineate cell populations in an unbiased manner. Cell type annotation was performed using established marker genes 23 . To prioritize biologically relevant populations, AUCell scores were calculated to identify cell population most strongly associated with the biomarkers of interest. Separately, the exploration of transcriptional regulatory mechanisms in key cell populations was facilitated by single-cell regulatory network inference and clustering (SCENIC). 2.9 qPCR validation To validate the expression of biomarkers in clinical samples, we collected blood samples from five patients with AMI and five healthy controls at the Second Affiliated Hospital of Wannan Medical College (Approval No. WYEFYLS2025033). All samples were immediately stored at -80°C until further processing. Total RNA was extracted from each sample using TRIzol reagent (Vazyme, R401-01, China). Reverse transcription was subsequently performed with reverse transcriptase (Yugong Bio, Cat. No. EG15133S, China) to synthesize cDNA, and concentrations were quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). To ensure consistency in amplification, all cDNA samples were diluted to a final concentration of 150 ng/mL. Quantitative PCR was then carried out using gene-specific primers and Hieff® qPCR SYBR Green Master Mix (ROX-free) (GOONIE, Cat. No. 500102) on a StepOnePlus Real-Time PCR System (BIO-RAD, USA). Primer sequences synthesized by Qingke Bio (Nanjing, China) are listed in Table 2 . β-actin served as the internal reference gene for normalization, and relative expression levels were calculated using the 2 −ΔΔCt method. All experiments were performed in triplicate. Table 2 The list of primer sequences. Gene Primer sequence ADM Forward: 5’-ATGAAGCTGGTTTCCGTCG-3’ Reverse: 5’-GACATCCGCAGTTCCCTCTT-3’ MECEMP1 Forward: 5’-GGGGTCTCAGCCAAGAATCAA-3’ Reverse: 5’-ACCACCCTTTGCATGGTCC-3’ TSPO Forward: 5’-CCTGCTCTACCCCTACCTGG-3’ Reverse: 5’-GCCATACGCAGTAGTTGAGTG-3’ PPARG Forward: 5’-TACTGTCGGTTTCAGAAATGCC-3’ Reverse: 5’-GTCAGCGGACTCTGGATTCAG-3’ ECRP Forward: 5’-TAACCCAACTATAACCTGCCCT-3’ Reverse: 5’-GTCGTTGATCCCTGTTGTCAC-3’ β-actin Forward: 5’-CATGTACGTTGCTATCCAGGC-3’ Reverse: 5’-CTCCTTAATGTCACGCACGAT-3’ 2.10 Statistical analysis All statistical analyses were performed using R software (v 4.2.2) and GraphPad Prism (v 10.1.2), while Cytoscape (v 3.9.1) was used for network visualization. For comparisons between two groups, normally distributed data were analyzed using Student's t -test, while non-normally distributed data were assessed using the Mann-Whitney U test. The threshold for statistical significance was set at p < 0.05. 3. Results 3.1 Identification of 31 candidate genes in AMI We compared the differences in TMRG scores between the AMI and non-AMI groups to assess the usability of the training set. The results showed that there was a statistically significant difference between the two groups (p = 0.0008, Fig. 2 A), indicating that the dataset was appropriate for subsequent analyses. Next, we performed quality control to assess sample availability, confirming the absence of outlier samples (Fig. 2 B). Using an optimal soft threshold (β = 24, Fig. 2 C), we constructed a weighted gene co-expression network, which revealed 11 distinct gene modules (Fig. 2 D). Pearson’s correlation analysis demonstrated that the green module exhibited a strong correlation with TMRG-scores (r = 0.82, p = 3.0E-40, Fig. 2 E). This module contained 305 genes, hereafter designated as WGCNA-TMRGs for further investigation. Subsequently, we intersected the 166 differentially expressed genes (DEGs) from the training dataset (Fig. 2 F) with the 305 WGCNA-TMRGs, yielding 31 candidate genes (Fig. 2 G). Functional enrichment analysis revealed that these genes were primarily associated with inflammatory responses and immune cell dysregulation pathways (Fig. 2 H-I), suggesting a pivotal role of immunity in AMI pathogenesis. Moreover, PPI network analysis identified key functional relationships among these candidate genes (Fig. 2 J). 3.2 Screening of three biomarkers using four machine learning algorithms To systematically identify robust AMI-related biomarkers from the 31 candidate genes, we implemented four machine learning algorithms: LASSO regression, XGBoost, RF, and SVM-RFE. In LASSO regression, we identified 16 genes based on optimal lambda values selected via 10-flod cross-validation (Fig. 3 A-B, Table S2). XGBoost and RF models were conducted to evaluate the importance of each gene by gain value and Gini score, each yielding 20 top-ranking features (Fig. 3 C-D, Table S2). SVM-RFE algorithm yielded seven high-priority genes based on variable importance metrics (Fig. 3 E, Table S2). By intersecting the outputs from all four models, five overlapping genes-ADM, ECRP, MCEMP1, TSPO, and PPARG-were identified as robust biomarkers (Fig. 3 F). In the training dataset, all five genes showed significant upregulation in AMI patients (Fig. 3 G). Additionally, the validation dataset confirmed that ADM, MCEMP1, and TSPO were significantly upregulated in the AMI group (p < 0.05, Fig. 3 H). Furthermore, qPCR validation using clinical samples revealed significant upregulation of ECRP in AMI patients (p = 0.0145; Fig. 3 I), while the remaining four genes exhibited consistent trends with the training dataset, though not statistically significant. 3.3 Construction and evaluation of logistic regression-based diagnostic model To assess the clinical utility of the identified biomarkers in AMI diagnosis, we developed a logistic regression-based diagnostic model based on three validated genes (ADM, MCEMP1, and TSPO). The risk of AMI was calculated using the following equation: logit(P) = 2.4763 + (1.5574 × expression value of ADM) + (1.7825 × expression value of MCEMP1) + (0.8808 × expression value of TSPO). The model's performance was validated using ROC curves on both the training set and validation set. The area under the receiver operating characteristic curve (AUC) reached 0.944 on the training set (Fig. 4 A), indicating that the model exhibits strong discriminative ability. Additionally, the AUC reached 0.730 on the validation set, confirming the model’s generalizability (Fig. 4 B). To facilitate clinical translation, a nomogram integrating the three biomarkers was developed (Fig. 4 C). Further performance evaluation via confusion matrix analysis revealed an overall accuracy of 0.90, with a sensitivity of 0.78, specificity of 0.95, and an F1 score of 0.82 (Fig. 4 D). DCA indicated a favorable net clinical benefit across a broad spectrum of threshold probabilities (Fig. 4 E). Additionally, the calibration curve exhibited excellent agreement between predicted and observed probabilities, with minimal bias (Fig. 4 F). Collectively, these results indicate that the logistic regression model based on ADM, MCEMP1, and TSPO possesses high diagnostic accuracy and holds promise for incorporation into clinical decision-making processes for AMI detection. 3.4 Function enrichment analysis of biomarkers in AMI Beyond identifying biomarkers for AMI, we also explored their potential biological functions. Gene Ontology (GO) Biological Process (BP) enrichment analysis indicated that they can promote reactive oxygen species metabolism, while positive regulation of natural killer (NK) cell-mediated immune processes was significantly negatively correlated with the biomarkers (Fig. 5 A, C, E). Additionally, KEGG pathway enrichment analysis revealed that their significant enrichment in multiple pathways, such as lysosome-related pathways and neutrophil extracellular trap (NET) formation pathways (Fig. 5 B, D, F). Notably, negative associations were observed with pathways related to cell cycle regulation, T cell receptor signaling, and nucleocytoplasmic transport. The above results recommend that these biomarkers may play a key role in the pathogenesis of AMI by regulating the immune system and oxidative stress pathways. 3.5 Regulatory networks construction and drug prediction in AMI To investigate the regulatory mechanisms of biomarkers in AMI, we constructed a competing endogenous RNA (ceRNA) network. By intersecting 62 miRNAs from the Microcosm database with 30 miRNAs from the miRanda database, we identified six overlapping miRNAs (Fig. 6 A). Subsequent analysis predicted five lncRNAs targeting two of these miRNAs. Integrating miRNA-mRNA and lncRNA-miRNA interactions, we established a comprehensive ceRNA network, revealing key regulatory axes implicated in AMI (Fig. 6 B). Notably, this network demonstrated that five lncRNAs modulate ADM expression by targeting two miRNAs. Additionally, we generated a co-expression network of biomarkers using GeneMANIA (Fig. 6 C), which comprised three central genes and 20 peripheral predicted genes. This network demonstrated that they may be associated with functions like hormone biosynthetic process and cAMP-mediated signaling. To identify potential therapeutic agents, we performed drug prediction analysis. TSPO was associated with 10 candidate drugs, while ADM corresponded to five (Fig. 6 D). Among these, ONO-2952 (a known TSPO antagonist) were selected for molecular docking. Computational analysis revealed strong binding affinity between ONO-2952 and TSPO (docking score = -5.8 kcal/mol; Fig. 6 E). These computational findings position ONO-2952 as promising therapeutic candidate warranting further investigation for AMI treatment. 3.6 Molecular dynamic simulation In the 100 ns MD simulation, the protein–ligand complex remained structurally stable. RMSD rose in the first 10 ns, plateaued after 20 ns, and stabilized at 0.4–0.5 nm (Fig. 7 A); ligand RMSD also remained steady (~ 0.48 nm), indicating a consistent binding mode (Fig. 7 B). RMSF analysis showed localized flexibility in loop and terminal regions (0.5–0.6 nm, up to 1 nm at the N-terminus), minimal fluctuations in α-helices (< 0.2 nm), and enhanced rigidity at binding residues (~ 0.15 nm) (Fig. 7 C). The ligand’s SASA and radius of gyration remained essentially constant (Fig. 7 D), supporting a compact, stable complex. HDBSCAN (≤ 5 Å) identified 12 states (Fig. 7 E). Clusters 3 and 4 dominated (27.71% and 38.81%), with an RMSD of 11.149 Å between them and > 20 Å relative to others, indicating cluster 3 as a distinct metastable state and cluster 4 as the dominant conformation (Fig. 7 F). Together, these two clusters accounted for more than 66% of the trajectory, revealing a dynamic equilibrium between the dominant and functionally relevant metastable states. Cluster 3 exhibited dense H-bonding (Trp53, Asn151), hydrophobic stacking (Tyr34, Trp95), and water mediation, while cluster 4 retained key contacts but showed altered H-bond geometry (min 2.63 Å) and water distribution, likely due to microenvironment-driven side-chain shifts (Thr147, Cys19) or slight ligand reorientation (Fig. 7 G-H). Contact-frequency and PLIP analyses highlighted Trp53, Trp95, Pro96, Phe100, Cys19, and Gly50 (> 70% frequency) as core interaction anchors, with contributions from Trp93 and Tyr57 and occasional His43/Phe99 involvement (Fig. 7 I-J). Hydrophobic, π-π, H-bond, and halogen interactions collectively stabilized binding and suggested possible allosteric effects. Two independent 100 ns simulations showed consistent key inter-residue distances (F20, W53, W95, N151), with representative structures displaying comparable geometry and distances across runs (Fig. 7 K-L), confirming reproducible binding behavior. 3.7 Immune microenvironment in AMI To characterize the immune microenvironment in AMI pathogenesis, we performed comprehensive immune infiltration analysis using both CIBERSORT and MCP-counter algorithms. CIBERSORT revealed monocytes as the predominant immune cell population among the 22 cell types analyzed (Fig. 8 A). Meanwhile, comparative analysis demonstrated monocytes and neutrophils were significantly upregulated in AMI group compared to non-AMI group, while CD8 T cells were significantly downregulated in AMI group (p < 0.05, Fig. 8 B-C). To elucidate biomarker-immune cell relationships, we conducted Spearman correlation analysis. Both of CIBERSORT and MCP-counter analyses consistently revealed all three biomarkers were positively correlated with monocytes and neutrophils (r > 0.5, p < 0.05, Fig. 8 D-E). These findings highlight monocytes and neutrophils as key mediators of immune microenvironment remodeling in AMI, providing mechanistic insights into disease-associated inflammation and establishing a framework for future single-cell resolution studies. 3.8 Single-cell data analysis Single-cell RNA sequencing analysis was performed using the GSE269269 dataset, comprising 95,176 cells initially captured. After stringent quality control filtering, 92,876 high-quality cells were retained for downstream analysis (Fig. 9 A). We identified the top 3,000 highly variable genes and performed batch effect correction to eliminate systemic variations across samples (Fig. 9 B, Fig. S1A-B). Principal component analysis revealed 15 distinct clusters (Fig. 9 C, Fig. S1C), with top five cluster-specific marker genes visualized in the heatmap (Fig. 9 D). Using established marker genes, we successfully annotated nine major immune cell populations: B cells, eosinophils, erythrocytes, megakaryocytes, monocytes, neutrophils, NK cells, plasma cells, and T cells (Fig. S1D, Fig. 9 E). 3.9 Transcriptional regulatory network analysis reveals key mechanisms of monocytes in AMI immune microenvironment To identify cell population most strongly associated with AMI biomarkers, we performed AUCell analysis. Monocytes demonstrated the highest AUC score among all cell populations (Fig. 10 A-B), suggesting their predominant role in biomarker expression dynamics during AMI pathogenesis. Subsequently, we employed SCENIC to identify key transcription factors (TFs) regulating biomarker expression in monocytes. This analysis revealed 15 candidate TFs potentially regulating TSPO and ADM expression (Fig. 10 C). Comparative analysis of TF activity across cell populations identified TFEC and CEBPD as exhibiting particularly high regulatory activity in monocytes (Fig. 10 D). Comparative analysis of TF activity across cell populations identified TFEC and CEBPD as exhibiting particularly high regulatory activity in monocytes (Fig. 10 E). Validation in bulk transcriptomic data confirmed significant upregulation of both TFEC and CEBPD in AMI patients compared to controls (p < 0.05, Fig. 10 F), indicating its potential as both a diagnostic biomarker and a therapeutic target for AMI intervention. 4. Discussion Acute myocardial infarction is one of the leading causes of death worldwide, significantly impacting public health 24 . Studies have shown that the concentrations of multiple tryptophan metabolites in the plasma of AMI patients are elevated, suggesting that tryptophan metabolism may serve as a monitoring pathway for AMI 25 . Therefore, we conducted an in-depth exploration of the role of TrM in AMI. In this study, we comprehensively analyzed the association between tryptophan metabolism-related genes and the pathological mechanisms of AMI, ultimately identifying three biomarkers (ADM, MCEMP1, and TSPO). On this basis, we observed a significant association of these biomarkers with the immune-inflammatory response. Furthermore, we found that monocytes play a vital role in the immune microenvironment of AMI. Single-cell data analysis results further validated the role of monocytes in AMI and identified potential regulatory transcription factors TFEC and CEBPD. Integrating WGCNA and differential expression analysis, we screened 31 candidate genes. Subsequently, five candidate biomarkers were obtained from multiple machine learning algorithms. We further confirmed the expression levels of these genes in the training and validation sets, and eventually recognized three biomarkers (ADM, MCEMP1, and TSPO). ADM, also known as adrenomedullin, is a multifunctional peptide hormone. Its main function involves vasodilation, regulation of blood pressure, and protection against oxidative stress 26 . In AMI, ADM can reduce size by promoting angiogenesis and inhibiting cardiomyocyte apoptosis 27 . Additionally, elevated ADM levels may serve as a compensatory mechanism in patients following AMI to counteract the adverse effects of ischemia-reperfusion, thereby improving cardiac function 28 . Mast cell expressed membrane protein 1 (MCEMP1) primarily functions in mast cell activation and immune response regulation 29 . The role of MCEMP1 in AMI is unclear. Given the involvement of mast cells in the inflammatory response during AMI, MCEMP1 may influence the inflammatory process associated with AMI by regulating mast cell degranulation and inflammatory mediator release 30 . The transporter protein TSPO, a mitochondrial external membrane protein, plays a vital role in regulating cholesterol transport and steroidogenesis 31 . TSPO protects cardiomyocytes from apoptosis as well as enhances mitochondrial function to reduce oxidative stress, thereby inhibiting the progression of AMI 32 . Additionally, TSPO may modulate the immune response in the heart by interacting with immune cells, thus influencing the post-AMI remodeling process and overall cardiac function 33 . Molecular docking results revealed good binding affinity between ONO-2952 and TSPO, which was subsequently validated by 100 ns molecular dynamics (MD) simulations. The results demonstrated that the interaction between ONO-2952 and TSPO also resulted in good stability in the protein's overall structure, internal stability, and compactness. MD is currently widely used in drug discovery and in understanding protein-drug interactions 34 . This computational tool can reduce experimental resources and enable rapid molecular screening 35 . Overall, this approach is contributing to a deeper understanding of the pathological mechanisms of AMI. The immune infiltration analysis results clearly demonstrate significant alterations in the immune microenvironment of patients with AMI. The three identified biomarkers are predominantly associated with the infiltration of monocytes and neutrophils, highlighting their crucial roles within the immune microenvironment. Monocytes, a type of leukocyte that circulates in the bloodstream, play a key role in the innate immune response by phagocytosis and digestion of pathogens 36 . In AMI, dangerous molecules released by damaged myocardial cells allure monocytes to migrate to the injury area, where they distinguish into macrophages to clear dead cells 37 . However, this leads to immoderate accumulation of monocytes at the injury area, thereby increasing the magnitude of infarction damage in patients 37 . Additionally, monocytes secrete certain cytokines to expand the inflammatory response 38 . Neutrophils, as the first responders to the injured site, combat bacteria by releasing antimicrobial substances and phagocytosing pathogens 39 . Therefore, a key feature of the early immune response in AMI is an elevation in neutrophil levels. However, while clearing pathogens, they also damage normal myocardial tissue, leading to ventricular remodeling and heart failure 40 . We analyzed the GSE269269 dataset to identify a total of nine cell types, and monocytes were confirmed as a key cell population in AMI by AUC scores. The SCENIC analysis was also applied to identify the possible regulatory TFs TFEC and CEBPD for monocytes. TFEC is a member of the microphthalmia-associated TF family, and plays a role in cell-specific gene regulation 41 . Significant upregulation of TFEC in monocytes suggests an enhanced immune response in AMI, exacerbating tissue damage 42 . CEBPD, a member of the CCAAT/enhancer-binding protein family, primarily regulates cell proliferation, differentiation, and inflammatory responses 43 . CEBPD upregulation increases proinflammatory cytokine secretion by monocytes, subsequently contributing to systemic inflammation in AMI patients 44 . Moreover, it also affects the long-term prognosis of AMI patients by regulating the balance between cell survival and apoptosis in cardiac tissue 45 . This study elucidates the role of tryptophan metabolism in the pathogenesis of AMI and identifies three diagnostic biomarkers. However, several limitations must be acknowledged. First, as our analysis relies on publicly available datasets, reliance on publicly available datasets entails inherent population bias, while the relatively small sample size may introduce result bias. Second, due to sample size limitations, our qPCR results are not universally applicable. Therefore, our future research will focus more on in vivo and in vitro experiments. Conclusion In conclusion, we integrated bulk and single-cell transcriptomic data to systematically elucidate the critical involvement of tryptophan metabolism-related genes in AMI pathogenesis. Our study identified three robust diagnostic biomarkers-ADM, MCEMP1, and TSPO-that demonstrate high clinical discriminative power. Immune infiltration analysis demonstrated monocytes and neutrophils as key cellular mediators of AMI progression, with parallel transcriptional regulatory analysis identifying TFEC and CEBPD as master regulators targeting TSPO. By drug prediction and molecular docking, potential therapeutic targets and candidate drugs were screened, providing a novel insight for AMI treatment. Abbreviations AMI acute myocardial infarction TMRGs tryptophan metabolism-related genes WGCNA weighted gene co-expression network analysis LASSO least absolute shrinkage and selection operator XGBoost extreme gradient boosting RF random forest SVM-RFE support vector machine-recursive feature elimination Declarations Author Contributions: Lei Wang , Conceptualization, Design, Methodology, Data curation, Writing - original draft, Writing - review and editing. Chengmin Tao , Design, Methodology, writing - review and editing. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: 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). Informed Consent Statement: Anonymized clinical samples were analyzed in this study, and the need for informed consent was waived by the ethics committee. Data availability statement: All the RNA-sequencing data and single-cell RNA-sequencing data of acute myocardial infarction patients were acquired from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi). Acknowledgments: Not applicable. Conflict of Interest: The authors declare no conflicts of interest. References Liao PD, Chen KJ, Ge JB, Zhang MZ. Clinical Practice Guideline of Integrative Chinese and Western Medicine for Acute Myocardial Infarction. Chin J Integr Med. 2020;26(7):539–51. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8783966","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588247567,"identity":"b09a575b-c131-4615-a327-80677a731125","order_by":0,"name":"Lei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBADHj725oMPPhjY2BGvhY3nWLLhjIK0ZOKtYZPIMZPm+XCIsYGQSoPjvQ8/F9Rsk2FjSDCQtjE4wMzAfvjoBrxazhw3lp5x7DYPG8OBBOMcgzt8DDxpaTfwaZGckcYgzcMG1MLYcCA5x+AZM4MEjxl+LfOfMf/m+QfUwszYcNjC4DBjAyEt/BJsbNK8bUAtQD3NDERp4Uljs57ZB9QCsqfHIC2ZjZBf2NiPMd8u+Hbbnl/+/fcfP/7Y2PGzHz6GVwsIMKMaQkg5ppZRMApGwSgYBegAABYyQvcsUjyuAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Wannan Medical College cardiovascular medicine department","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":588247576,"identity":"32076a3d-b3eb-4e14-8d7b-7d2d1693800e","order_by":1,"name":"Chengmin Tao","email":"","orcid":"","institution":"Wuhu First People's Hospital Cardiovascular Department","correspondingAuthor":false,"prefix":"","firstName":"Chengmin","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2026-02-04 08:53:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8783966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8783966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102312158,"identity":"09c87906-7437-4727-ba27-7726fa127acc","added_by":"auto","created_at":"2026-02-10 12:00:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1706255,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of this study.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/2c7ae860aeb6adb4ae8accb6.png"},{"id":102311910,"identity":"f6549d2b-7ca7-482f-9423-becc594d9ca5","added_by":"auto","created_at":"2026-02-10 11:59:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":504757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and function enrichment of candidate genes\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) TMRG-scores between AMI and control groups in training dataset. (\u003cstrong\u003eB\u003c/strong\u003e) Samples clustering tree. (\u003cstrong\u003eC\u003c/strong\u003e) Evaluation of scale-free fit index (left) and mean connectivity (right) for distinct soft threshold powers. (\u003cstrong\u003eD\u003c/strong\u003e) Clustering dendrogram of genes based on topological overlapping. (\u003cstrong\u003eE\u003c/strong\u003e) Heatmap of the association between gene modules and TMRG-scores. (\u003cstrong\u003eF\u003c/strong\u003e) Volcanic map of differentially expressed genes (DEGs) from the training dataset. (\u003cstrong\u003eG\u003c/strong\u003e) 31 candidate genes were overlapped by DEGs and WGCNA-TMRGs. (\u003cstrong\u003eH\u003c/strong\u003e) GO enrichment analysis of candidate genes, including molecular functions (MF), cellular components (CC), and biological progresses (BP). (\u003cstrong\u003eI\u003c/strong\u003e) KEGG enrichment analysis of candidate genes. (\u003cstrong\u003eJ\u003c/strong\u003e) PPI network of candidate genes.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/458a2a4065eb7c1c72f7058d.png"},{"id":102312154,"identity":"c0cd4d49-d05d-44aa-8ae5-db11f5db2a55","added_by":"auto","created_at":"2026-02-10 12:00:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of biomarkers in AMI\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Cross-validation curve of LASSO model. (\u003cstrong\u003eB\u003c/strong\u003e) Coefficient profile of genes in LASSO regression. (\u003cstrong\u003eC\u003c/strong\u003e) Top 20 features ranked by gain value in XGBoost. (\u003cstrong\u003eD\u003c/strong\u003e) Top 20 features ranked by mean Gini importance in RF. (\u003cstrong\u003eE\u003c/strong\u003e) Expression validation of features by SVM-RFE algorithm. (\u003cstrong\u003eF\u003c/strong\u003e) Four algorithmic Venn diagram screening genes. (\u003cstrong\u003eG\u003c/strong\u003e) Screening gene expression tendency in training dataset. (\u003cstrong\u003eH\u003c/strong\u003e) The expression levels of screening genes between AMI and non-AMI groups in validation dataset. (\u003cstrong\u003eI\u003c/strong\u003e) The mRNA expression levels of biomarkers between AMI patients and healthy controls in clinical samples. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/cb1b4f6588b3b30ebd30b48c.png"},{"id":102311614,"identity":"ddf94cef-44e6-4780-85e3-4d686ba72267","added_by":"auto","created_at":"2026-02-10 11:58:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2836635,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of a logistic regression-based diagnostic model for AMI\u003c/strong\u003e. (\u003cstrong\u003eA-B\u003c/strong\u003e) ROC curve demonstrating the model's discriminative performance in the training dataset (\u003cstrong\u003eA\u003c/strong\u003e) and validation dataset (\u003cstrong\u003eB\u003c/strong\u003e). (\u003cstrong\u003eC\u003c/strong\u003e) Nomogram incorporating three biomarkers for individual AMI risk prediction. (\u003cstrong\u003eD\u003c/strong\u003e) Confusion matrix summarizing the model's classification accuracy, sensitivity, and specificity. (\u003cstrong\u003eE\u003c/strong\u003e) Decision curve analysis (DCA) evaluating the net clinical benefit across different probability thresholds. (\u003cstrong\u003eF\u003c/strong\u003e) Calibration curve indicating consistency between predicted and actual outcomes.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/07221ca0ce249bc16b4eacac.png"},{"id":102311758,"identity":"bcce477f-4292-4304-b9a3-b2b9c4645eab","added_by":"auto","created_at":"2026-02-10 11:59:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":482928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis\u003c/strong\u003e. (\u003cstrong\u003eA, C, E\u003c/strong\u003e) GO BP term of ADM (\u003cstrong\u003eA\u003c/strong\u003e), MCEMP1 (\u003cstrong\u003eC\u003c/strong\u003e), TSPO (\u003cstrong\u003eE\u003c/strong\u003e). (\u003cstrong\u003eB, D, F\u003c/strong\u003e) KEGG enrichment pathways of ADM (\u003cstrong\u003eB\u003c/strong\u003e), MCEMP1 (\u003cstrong\u003eD\u003c/strong\u003e), TSPO (\u003cstrong\u003eF\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/18efe7bd402ad0b7c86063ff.png"},{"id":102312034,"identity":"23f4b7de-b7e6-4f08-91e6-0815c585cb6c","added_by":"auto","created_at":"2026-02-10 12:00:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":536806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of regulatory networks and computational drug prediction for AMI biomarkers\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Venn diagram illustrating the identification of six consensus miRNAs through integration of predictions from Microcosm and miRanda databases. (\u003cstrong\u003eB\u003c/strong\u003e) Comprehensive ceRNA regulatory network depicting interactions among mRNAs (biomarkers), miRNAs, and lncRNAs. (\u003cstrong\u003eC\u003c/strong\u003e) Gene-gene interaction network for biomarkers, featuring three core biomarker genes (center) and their top 20 functionally associated partners, with node coloration indicating functional categories. (\u003cstrong\u003eD\u003c/strong\u003e) Drug-biomarker network. The orange node represents biomarker, and the blue node represents drug. (\u003cstrong\u003eE\u003c/strong\u003e) Visualization of docked posed of ONO-2952 with their protein target.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/bf6799a635c7984a7c20e828.png"},{"id":102311851,"identity":"9b5beac4-be1b-424b-af4d-71e82d7f0044","added_by":"auto","created_at":"2026-02-10 11:59:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":457407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMD analysis\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) RMSD (nm) variation between small molecules and proteins (Cα atoms) during a 100 ns simulation. (\u003cstrong\u003eB\u003c/strong\u003e) RMSF (nm) variation between proteins and small molecules (ID 170) over a 100 ns simulation. (\u003cstrong\u003eC\u003c/strong\u003e) Changes in SASA (nm2) of the small molecule during the 100 ns simulation. (\u003cstrong\u003eD\u003c/strong\u003e) Changes in radius of gyration (nm) of the small molecule during the 100 ns simulation. (\u003cstrong\u003eE-F\u003c/strong\u003e) HDBSCAN clustering results. \u003cstrong\u003eE\u003c/strong\u003e shows the distribution of conformational counts within each cluster. \u003cstrong\u003eF\u003c/strong\u003e shows the distances between clusters (RMSD between representative conformations of molecules and pockets). (\u003cstrong\u003eG-H\u003c/strong\u003e) Two Primary Binding Conformations of Small Molecule-Protein Complexes: Cluster 3 (\u003cstrong\u003eG\u003c/strong\u003e) and Cluster 4 (\u003cstrong\u003eH\u003c/strong\u003e). (\u003cstrong\u003eI\u003c/strong\u003e) Contact frequency and average distance of residues interacting with small molecules. (\u003cstrong\u003eJ\u003c/strong\u003e) Types of interactions between contact residues and small molecules. (\u003cstrong\u003eK-L\u003c/strong\u003e) Time evolution of key distances between the ligand and active site residues in simulated trajectory 1 (\u003cstrong\u003eK\u003c/strong\u003e) and simulated trajectory 2 (\u003cstrong\u003eL\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/cab3db43643dc840e7ea21d7.png"},{"id":102312099,"identity":"5a7fc3eb-9d02-4212-b1b4-56ca14b26542","added_by":"auto","created_at":"2026-02-10 12:00:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":457675,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune microenvironment in AMI\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Landscape of immune cell infiltration in GSE59867 samples, as quantified by CIBERSORT analysis. (\u003cstrong\u003eB\u003c/strong\u003e) Comparative analysis of 22 immune cell types infiltration between AMI and non-AMI groups (CIBERSORT). (\u003cstrong\u003eC\u003c/strong\u003e) Comparative analysis of 8 immune cell types infiltration between AMI and non-AMI groups (MCP-counter). (\u003cstrong\u003eD\u003c/strong\u003e) Correlation between biomarkers expression and the abundance of 22 immune-related cells (CIBERSORT). (\u003cstrong\u003eE\u003c/strong\u003e) Correlation between biomarkers expression and the abundance of 8 immune-related cells (MCP-counter). *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, *p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/365e5a432d19367185f6c8b4.png"},{"id":102311881,"identity":"6c84acbf-3453-428f-8c5b-4d371e228ec3","added_by":"auto","created_at":"2026-02-10 11:59:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":415497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell data processing and cell type annotation\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Quality control metrics showing distributions of nFeature_RNA, nCount_RNA, and percent_MT of single-cell data before (top) and after (bottom) quality filtering. (\u003cstrong\u003eB\u003c/strong\u003e) The variance plot showed 12,499 genes in all cells, with top 3,000 highly variable genes highlighted in red. (\u003cstrong\u003eC\u003c/strong\u003e) UMAP visualization of 15 identified cell clusters. (\u003cstrong\u003eD\u003c/strong\u003e) The heatmap displaying expression patterns of top five marker genes across the 15 cell populations. (\u003cstrong\u003eE\u003c/strong\u003e) The distribution of identifying nine cell populations were visualized in UMAP.\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/09521d59ea670c2866438dc7.png"},{"id":102312089,"identity":"a03bc7ed-227e-406e-ae83-d53e71bac8e3","added_by":"auto","created_at":"2026-02-10 12:00:02","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":308170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonocyte-specific transcriptional regulation of AMI biomarkers\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Boxplot showed the AUC scores of different cell populations. (\u003cstrong\u003eB\u003c/strong\u003e) UMAP displayed the distribution of AUC score in different cell populations. (\u003cstrong\u003eC\u003c/strong\u003e) TF-biomarker regulatory network. The orange nodes represent biomarkers, and the blue nodes represent TFs. (\u003cstrong\u003eD\u003c/strong\u003e) The heatmap of TFs activities in each cell population. (\u003cstrong\u003eE\u003c/strong\u003e) UMAP showed the expression distribution of TFEC (left) and CEBPD (right) in all cell populations. (\u003cstrong\u003eF\u003c/strong\u003e) Boxplot showed the expression of TFEC and CEBPD in bulk RNA-seq data between AMI and non-AMI groups. **p \u0026lt; 0.01, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/89069395282cda48b009a86e.png"},{"id":106402920,"identity":"33472072-361a-4f18-90d3-8ce1ff2f9f40","added_by":"auto","created_at":"2026-04-08 09:13:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11398709,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/9fa1b9aa-9b30-4e79-bdc9-8969533d5970.pdf"},{"id":102311960,"identity":"8dcf0088-0a02-4af7-a7ba-1022e5330f10","added_by":"auto","created_at":"2026-02-10 11:59:30","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":441771,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.png","url":"https://assets-eu.researchsquare.com/files/rs-8783966/v1/4b9737f28e7c76521eb857f8.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tryptophan metabolism-related biomarkers ADM, MCEMP1, and TSPO in acute myocardial infarction: insights from bioinformatics and machine learning","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute myocardial infarction (AMI) is caused by an insufficient or total interruption of coronary artery blood flow due to various elements, leading to severe and persistent AMI\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Clinical studies have indicated that many risk elements increase the risk of AMI, such as myocardial fibrosis, coronary artery atherosclerosis, and thrombosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Besides, the primary cause of death from cardiovascular disease is malignant arrhythmias and cardiogenic shock caused by AMI\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Currently, several diagnostic methods are available for AMI. Electrocardiography can detect the occurrence and progression of AMI, but this method may have limited diagnostic value for early-stage AMI patients\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Furthermore, changes in high-sensitivity troponin levels are also used for AMI diagnosis, but this method is prone to false-positive results, leading to inaccurate diagnoses\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies demonstrate that serum levels of tryptophan (Trp) and its metabolites in AMI patients differ significantly compared with healthy controls, suggesting their potential as novel biomarkers\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Tryptophan is a vital amino acid that the human body cannot synthesize on its own. It plays a key role in protein synthesis and the synthesis of various active compounds\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Increasing evidence showed that tryptophan metabolism (TrM) regulates multiple physiological processes including immune function and gut microbiome balance\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The 5-hydroxytryptamine (5-HT), indole, and kynurenine (Kyn) pathways collectively represent the TrM\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Among these, the Kyn pathway plays a dominant role, with its metabolites capable of regulating inflammatory responses and promoting myocardial cell apoptosis, thereby increasing the risk of AMI\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Therefore, identifying new diagnostic markers for AMI based on tryptophan metabolism and its metabolites could both improve early AMI diagnosis accuracy and offer new strategies for personalized therapy.\u003c/p\u003e \u003cp\u003eIn this study, we systematically explored the role of the tryptophan metabolic pathway in AMI by integrating bulk and single-cell RNA-seq data. First, candidate genes were screened using differential expression analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, four machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE), were used to further screen biomarkers, and their expression levels in clinical samples were validated by qPCR. Based on these biomarkers, we constructed and validated a diagnostic model. Furthermore, enrichment analysis, immune cell infiltration analysis, and regulatory networks construction explored the potential mechanisms of these biomarkers in AMI. Finally, we identified key cell populations and transcription factors associated with AMI through single-cell RNA sequencing analysis. Overall, our findings provide new insights into the precise diagnosis and personalized treatment of AMI. The investigational flowchart is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eAMI-related datasets were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which included training dataset GSE59867, validation dataset GSE123342, and single-cell RNA sequencing (scRNA-seq) dataset GSE269269. The detail information of datasets above is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Furthermore, a total of 50 tryptophan metabolism-related genes (TMRGs) were obtained from three gene sets, namely KEGG_TRYPTOPHAN_METABOLISM.v2024.1.Hs.gmt, REACTOME_TRYPTOPHAN_CATABOLISM.v2024.1.Hs.gmt, and WP_TRYPTOPHAN_METABOLISM.v2024.1.Hs.gmt, using the MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Table S1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of datasets utilized in this research.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample (controls/patients)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequencing platform\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE59867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46/111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL6244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE123342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL17586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE269269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 AMI patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL24676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Weighted gene co-expression network analysis\u003c/h2\u003e \u003cp\u003eTo identify key gene modules, we constructed gene co-expression modules using the GSE59867 dataset. First, we used single-sample gene set enrichment analysis (ssGSEA) to compute the enrichment scores of samples and TMRGs in the dataset. TMRG scores were then incorporated as phenotypic features into subsequent WGCNA analysis. The optimal soft threshold (β\u0026thinsp;=\u0026thinsp;24) was determined based on scale-free topology criteria (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.9). Using the topological overlap matrix (TOM) to assess the interaction strength between genes, we employed hierarchical clustering with average linkage and dynamic tree cutting (minimum module size\u0026thinsp;=\u0026thinsp;50 genes) to screen co-expression modules. Key module exhibiting the strongest significant correlation with TMRG-scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was designated as the key module. Genes within this module were classified as WGCNA-TMRGs for downstream analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Screening and functional enrichment analysis of candidate genes\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was employed by the R package limma (v 3.62.1) to screen differentially expressed genes (DEGs) in AMI samples and control samples\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The threshold for DEGs was set at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 fold change (FC)| \u0026gt; 0.5. Candidate genes were obtained by intersecting the DEGs with WGCNA-TMRGs through intersecting analysis. Functional enrichment analysis of candidate genes was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Separately, the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to explore the interaction relationships between candidate genes. Interaction relationships with a confidence level\u0026thinsp;\u0026le;\u0026thinsp;0.4 (medium confidence) were removed, and a protein-protein interaction network was constructed using Cytoscape (v 4.2.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification of biomarkers and construction of diagnostic models\u003c/h2\u003e \u003cp\u003eTo further screen for biomarkers of AMI, we employed four machine learning algorithms, comprising least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE). We used 10-fold cross-validation to determine the regularization parameter (λ) for LASSO regression analysis and removed genes with zero coefficients. Feature importance was calculated using the XGBoost and RF algorithms, and the top 20 genes ranked by gain and Gini values were retained. For SVM-RFE, features with the lowest weights were iteratively eliminated to retain genes associated with the minimum cross-validation error. Next, we extracted overlapping key genes common to all four algorithms. To ensure robustness, we evaluated these genes\u0026rsquo; expression levels in both training and validation datasets, retaining only genes with consistent trends as biomarkers. Subsequently, we developed a logistic regression-based diagnostic model for AMI to evaluate potential applicative value of biomarkers. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and confusion matrices were used to evaluate model performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene set enrichment analysis and construction of regulatory networks\u003c/h2\u003e \u003cp\u003eWe employed gene set enrichment analysis (GSEA) using the R package clusteProfiler (v4.7.1.003)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e to characterize the biological functions of AMI-associated biomarkers. Significantly enriched terms were selected with threshold of adjust p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To identify potential regulatory interactions, we predicted miRNAs targeting the biomarkers using the microcosm database\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and miRanda database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirtoolsgallery.tech/mirtoolsgallery/node/1055\u003c/span\u003e\u003cspan address=\"http://mirtoolsgallery.tech/mirtoolsgallery/node/1055\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Only miRNAs identified by both prediction methods were retained for subsequent analysis. Then, we predicted the lncRNAs based on the intersecting miRNAs using the StarBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A comprehensive competing endogenous RNA (ceRNA) network was constructed using the R package multiMiR (v 1.20.0)\u003csup\u003e18\u003c/sup\u003e. Finally, we also utilized GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genemania.org/\u003c/span\u003e\u003cspan address=\"http://www.genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to forecast other genes associated with biomarkers and investigate the pathways in which they are collectively involved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Drug prediction and immune infiltration analysis\u003c/h2\u003e \u003cp\u003eThe Drug-Gene Interaction Database (DGIdb, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgidb.org\u003c/span\u003e\u003cspan address=\"https://dgidb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict drugs targeting biomarkers, and the drug-biomarker interaction network was constructed using Cytoscape (v 3.9.1). Among these, the drug with the highest interaction score was selected for molecular docking. Incidentally, we performed molecular docking using the CB-Dock2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock2/php/index.php\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb-dock2/php/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to investigate the binding of drugs. Specifically, the 3D structures of proteins containing biomarkers were downloaded from the PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the molecular structures of drugs were obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Then, we conducted molecular docking analysis from the CB-Dock2. To explore the immune microenvironment in AMI, CIBERSORT and MCP-counter were conducted to quantifying infiltration levels of immune cell types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular dynamics simulation\u003c/h2\u003e \u003cp\u003eTo assess the interaction strength and stability of the receptor\u0026ndash;ligand complex, MD simulations were performed. The receptor was obtained from the PDB and parameterized using the Amber99SB-ILDN force field \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The ligand was protonated and pre-optimized in Avogadro \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, followed by charge calculation in Gaussian (v6) and GAFF parameter generation via Sobtop (v1.0). The complex was solvated in a TIP3P triangular-prism water box with a 10 \u0026Aring; buffer\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and Na\u003csup\u003e+\u003c/sup\u003e/Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e ions were added for neutralization. Energy minimization involved restrained solvent/ion relaxation followed by full-system minimization. Equilibration included 100 ps NVT heating (0-300 K) and 100 ps NPT density equilibration. A 100-ns production run was then conducted under NPT with a 2-fs step and trajectories saved every 10 ps. PME handled long-range electrostatics, SHAKE constrained hydrogen bonds \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and temperature/pressure were controlled by standard thermostat/barostat schemes. System stability was examined via root mean square deviation (RMSD), flexibility via root mean square fluctuation (RMSF), and compactness/solvent exposure using radius of gyration (Rg) and solvent-accessible surface area (SASA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Single-cell data processing\u003c/h2\u003e \u003cp\u003eSingle-cell transcriptomic analysis was primarily performed using the R package Seurat (v 4.3.0). Data quality control measures included nFeature_RNA less than 100 and larger than 5,000, nCount_RNA less than 100 and larger than 50,000, and the cell with a percentage of mitochondrial genes lower than 20%. Next, perform standardized analysis (standardization function) on the quality-controlled data and identify highly variable genes (FindVariableFeatures function). Dimensionality reduction was employed through principal component analysis (top 25 PCs). Batch-effect removal method was conducted by RunHarmony function. Subsequently, unsupervised clustering was conducted via the FindNeighbors, FindClusters, and RunUMAP functions to delineate cell populations in an unbiased manner. Cell type annotation was performed using established marker genes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. To prioritize biologically relevant populations, AUCell scores were calculated to identify cell population most strongly associated with the biomarkers of interest. Separately, the exploration of transcriptional regulatory mechanisms in key cell populations was facilitated by single-cell regulatory network inference and clustering (SCENIC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 qPCR validation\u003c/h2\u003e \u003cp\u003eTo validate the expression of biomarkers in clinical samples, we collected blood samples from five patients with AMI and five healthy controls at the Second Affiliated Hospital of Wannan Medical College (Approval No. WYEFYLS2025033). All samples were immediately stored at -80\u0026deg;C until further processing. Total RNA was extracted from each sample using TRIzol reagent (Vazyme, R401-01, China). Reverse transcription was subsequently performed with reverse transcriptase (Yugong Bio, Cat. No. EG15133S, China) to synthesize cDNA, and concentrations were quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). To ensure consistency in amplification, all cDNA samples were diluted to a final concentration of 150 ng/mL. Quantitative PCR was then carried out using gene-specific primers and Hieff\u0026reg; qPCR SYBR Green Master Mix (ROX-free) (GOONIE, Cat. No. 500102) on a StepOnePlus Real-Time PCR System (BIO-RAD, USA). Primer sequences synthesized by Qingke Bio (Nanjing, China) are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. β-actin served as the internal reference gene for normalization, and relative expression levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. All experiments were performed in triplicate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe list of primer sequences.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: 5\u0026rsquo;-ATGAAGCTGGTTTCCGTCG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;-GACATCCGCAGTTCCCTCTT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMECEMP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: 5\u0026rsquo;-GGGGTCTCAGCCAAGAATCAA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;-ACCACCCTTTGCATGGTCC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: 5\u0026rsquo;-CCTGCTCTACCCCTACCTGG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;-GCCATACGCAGTAGTTGAGTG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPARG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: 5\u0026rsquo;-TACTGTCGGTTTCAGAAATGCC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;-GTCAGCGGACTCTGGATTCAG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: 5\u0026rsquo;-TAACCCAACTATAACCTGCCCT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;-GTCGTTGATCCCTGTTGTCAC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-actin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: 5\u0026rsquo;-CATGTACGTTGCTATCCAGGC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse: 5\u0026rsquo;-CTCCTTAATGTCACGCACGAT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (v 4.2.2) and GraphPad Prism (v 10.1.2), while Cytoscape (v 3.9.1) was used for network visualization. For comparisons between two groups, normally distributed data were analyzed using Student's \u003cem\u003et\u003c/em\u003e-test, while non-normally distributed data were assessed using the Mann-Whitney U test. The threshold for statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of 31 candidate genes in AMI\u003c/h2\u003e \u003cp\u003eWe compared the differences in TMRG scores between the AMI and non-AMI groups to assess the usability of the training set. The results showed that there was a statistically significant difference between the two groups (p\u0026thinsp;=\u0026thinsp;0.0008, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), indicating that the dataset was appropriate for subsequent analyses. Next, we performed quality control to assess sample availability, confirming the absence of outlier samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Using an optimal soft threshold (β\u0026thinsp;=\u0026thinsp;24, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), we constructed a weighted gene co-expression network, which revealed 11 distinct gene modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Pearson\u0026rsquo;s correlation analysis demonstrated that the green module exhibited a strong correlation with TMRG-scores (r\u0026thinsp;=\u0026thinsp;0.82, p\u0026thinsp;=\u0026thinsp;3.0E-40, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). This module contained 305 genes, hereafter designated as WGCNA-TMRGs for further investigation. Subsequently, we intersected the 166 differentially expressed genes (DEGs) from the training dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) with the 305 WGCNA-TMRGs, yielding 31 candidate genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Functional enrichment analysis revealed that these genes were primarily associated with inflammatory responses and immune cell dysregulation pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH-I), suggesting a pivotal role of immunity in AMI pathogenesis. Moreover, PPI network analysis identified key functional relationships among these candidate genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Screening of three biomarkers using four machine learning algorithms\u003c/h2\u003e \u003cp\u003eTo systematically identify robust AMI-related biomarkers from the 31 candidate genes, we implemented four machine learning algorithms: LASSO regression, XGBoost, RF, and SVM-RFE. In LASSO regression, we identified 16 genes based on optimal lambda values selected via 10-flod cross-validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B, Table S2). XGBoost and RF models were conducted to evaluate the importance of each gene by gain value and Gini score, each yielding 20 top-ranking features (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, Table S2). SVM-RFE algorithm yielded seven high-priority genes based on variable importance metrics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Table S2). By intersecting the outputs from all four models, five overlapping genes-ADM, ECRP, MCEMP1, TSPO, and PPARG-were identified as robust biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). In the training dataset, all five genes showed significant upregulation in AMI patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Additionally, the validation dataset confirmed that ADM, MCEMP1, and TSPO were significantly upregulated in the AMI group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Furthermore, qPCR validation using clinical samples revealed significant upregulation of ECRP in AMI patients (p\u0026thinsp;=\u0026thinsp;0.0145; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI), while the remaining four genes exhibited consistent trends with the training dataset, though not statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction and evaluation of logistic regression-based diagnostic model\u003c/h2\u003e \u003cp\u003eTo assess the clinical utility of the identified biomarkers in AMI diagnosis, we developed a logistic regression-based diagnostic model based on three validated genes (ADM, MCEMP1, and TSPO). The risk of AMI was calculated using the following equation: logit(P)\u0026thinsp;=\u0026thinsp;2.4763 + (1.5574 \u0026times; expression value of ADM) + (1.7825 \u0026times; expression value of MCEMP1) + (0.8808 \u0026times; expression value of TSPO). The model's performance was validated using ROC curves on both the training set and validation set. The area under the receiver operating characteristic curve (AUC) reached 0.944 on the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), indicating that the model exhibits strong discriminative ability. Additionally, the AUC reached 0.730 on the validation set, confirming the model\u0026rsquo;s generalizability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). To facilitate clinical translation, a nomogram integrating the three biomarkers was developed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Further performance evaluation via confusion matrix analysis revealed an overall accuracy of 0.90, with a sensitivity of 0.78, specificity of 0.95, and an F1 score of 0.82 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). DCA indicated a favorable net clinical benefit across a broad spectrum of threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Additionally, the calibration curve exhibited excellent agreement between predicted and observed probabilities, with minimal bias (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Collectively, these results indicate that the logistic regression model based on ADM, MCEMP1, and TSPO possesses high diagnostic accuracy and holds promise for incorporation into clinical decision-making processes for AMI detection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Function enrichment analysis of biomarkers in AMI\u003c/h2\u003e \u003cp\u003eBeyond identifying biomarkers for AMI, we also explored their potential biological functions. Gene Ontology (GO) Biological Process (BP) enrichment analysis indicated that they can promote reactive oxygen species metabolism, while positive regulation of natural killer (NK) cell-mediated immune processes was significantly negatively correlated with the biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, C, E). Additionally, KEGG pathway enrichment analysis revealed that their significant enrichment in multiple pathways, such as lysosome-related pathways and neutrophil extracellular trap (NET) formation pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, D, F). Notably, negative associations were observed with pathways related to cell cycle regulation, T cell receptor signaling, and nucleocytoplasmic transport. The above results recommend that these biomarkers may play a key role in the pathogenesis of AMI by regulating the immune system and oxidative stress pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Regulatory networks construction and drug prediction in AMI\u003c/h2\u003e \u003cp\u003eTo investigate the regulatory mechanisms of biomarkers in AMI, we constructed a competing endogenous RNA (ceRNA) network. By intersecting 62 miRNAs from the Microcosm database with 30 miRNAs from the miRanda database, we identified six overlapping miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequent analysis predicted five lncRNAs targeting two of these miRNAs. Integrating miRNA-mRNA and lncRNA-miRNA interactions, we established a comprehensive ceRNA network, revealing key regulatory axes implicated in AMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Notably, this network demonstrated that five lncRNAs modulate ADM expression by targeting two miRNAs. Additionally, we generated a co-expression network of biomarkers using GeneMANIA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), which comprised three central genes and 20 peripheral predicted genes. This network demonstrated that they may be associated with functions like hormone biosynthetic process and cAMP-mediated signaling. To identify potential therapeutic agents, we performed drug prediction analysis. TSPO was associated with 10 candidate drugs, while ADM corresponded to five (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Among these, ONO-2952 (a known TSPO antagonist) were selected for molecular docking. Computational analysis revealed strong binding affinity between ONO-2952 and TSPO (docking score = -5.8 kcal/mol; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These computational findings position ONO-2952 as promising therapeutic candidate warranting further investigation for AMI treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Molecular dynamic simulation\u003c/h2\u003e \u003cp\u003eIn the 100 ns MD simulation, the protein\u0026ndash;ligand complex remained structurally stable. RMSD rose in the first 10 ns, plateaued after 20 ns, and stabilized at 0.4\u0026ndash;0.5 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA); ligand RMSD also remained steady (~\u0026thinsp;0.48 nm), indicating a consistent binding mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). RMSF analysis showed localized flexibility in loop and terminal regions (0.5\u0026ndash;0.6 nm, up to 1 nm at the N-terminus), minimal fluctuations in α-helices (\u0026lt;\u0026thinsp;0.2 nm), and enhanced rigidity at binding residues (~\u0026thinsp;0.15 nm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The ligand\u0026rsquo;s SASA and radius of gyration remained essentially constant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), supporting a compact, stable complex. HDBSCAN (\u0026le;\u0026thinsp;5 \u0026Aring;) identified 12 states (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Clusters 3 and 4 dominated (27.71% and 38.81%), with an RMSD of 11.149 \u0026Aring; between them and \u0026gt;\u0026thinsp;20 \u0026Aring; relative to others, indicating cluster 3 as a distinct metastable state and cluster 4 as the dominant conformation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). Together, these two clusters accounted for more than 66% of the trajectory, revealing a dynamic equilibrium between the dominant and functionally relevant metastable states. Cluster 3 exhibited dense H-bonding (Trp53, Asn151), hydrophobic stacking (Tyr34, Trp95), and water mediation, while cluster 4 retained key contacts but showed altered H-bond geometry (min 2.63 \u0026Aring;) and water distribution, likely due to microenvironment-driven side-chain shifts (Thr147, Cys19) or slight ligand reorientation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-H). Contact-frequency and PLIP analyses highlighted Trp53, Trp95, Pro96, Phe100, Cys19, and Gly50 (\u0026gt;\u0026thinsp;70% frequency) as core interaction anchors, with contributions from Trp93 and Tyr57 and occasional His43/Phe99 involvement (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI-J). Hydrophobic, π-π, H-bond, and halogen interactions collectively stabilized binding and suggested possible allosteric effects. Two independent 100 ns simulations showed consistent key inter-residue distances (F20, W53, W95, N151), with representative structures displaying comparable geometry and distances across runs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK-L), confirming reproducible binding behavior.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Immune microenvironment in AMI\u003c/h2\u003e \u003cp\u003eTo characterize the immune microenvironment in AMI pathogenesis, we performed comprehensive immune infiltration analysis using both CIBERSORT and MCP-counter algorithms. CIBERSORT revealed monocytes as the predominant immune cell population among the 22 cell types analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Meanwhile, comparative analysis demonstrated monocytes and neutrophils were significantly upregulated in AMI group compared to non-AMI group, while CD8 T cells were significantly downregulated in AMI group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB-C). To elucidate biomarker-immune cell relationships, we conducted Spearman correlation analysis. Both of CIBERSORT and MCP-counter analyses consistently revealed all three biomarkers were positively correlated with monocytes and neutrophils (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-E). These findings highlight monocytes and neutrophils as key mediators of immune microenvironment remodeling in AMI, providing mechanistic insights into disease-associated inflammation and establishing a framework for future single-cell resolution studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Single-cell data analysis\u003c/h2\u003e \u003cp\u003eSingle-cell RNA sequencing analysis was performed using the GSE269269 dataset, comprising 95,176 cells initially captured. After stringent quality control filtering, 92,876 high-quality cells were retained for downstream analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). We identified the top 3,000 highly variable genes and performed batch effect correction to eliminate systemic variations across samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, Fig. S1A-B). Principal component analysis revealed 15 distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC, Fig. S1C), with top five cluster-specific marker genes visualized in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). Using established marker genes, we successfully annotated nine major immune cell populations: B cells, eosinophils, erythrocytes, megakaryocytes, monocytes, neutrophils, NK cells, plasma cells, and T cells (Fig. S1D, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Transcriptional regulatory network analysis reveals key mechanisms of monocytes in AMI immune microenvironment\u003c/h2\u003e \u003cp\u003eTo identify cell population most strongly associated with AMI biomarkers, we performed AUCell analysis. Monocytes demonstrated the highest AUC score among all cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-B), suggesting their predominant role in biomarker expression dynamics during AMI pathogenesis. Subsequently, we employed SCENIC to identify key transcription factors (TFs) regulating biomarker expression in monocytes. This analysis revealed 15 candidate TFs potentially regulating TSPO and ADM expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Comparative analysis of TF activity across cell populations identified TFEC and CEBPD as exhibiting particularly high regulatory activity in monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). Comparative analysis of TF activity across cell populations identified TFEC and CEBPD as exhibiting particularly high regulatory activity in monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). Validation in bulk transcriptomic data confirmed significant upregulation of both TFEC and CEBPD in AMI patients compared to controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF), indicating its potential as both a diagnostic biomarker and a therapeutic target for AMI intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAcute myocardial infarction is one of the leading causes of death worldwide, significantly impacting public health\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Studies have shown that the concentrations of multiple tryptophan metabolites in the plasma of AMI patients are elevated, suggesting that tryptophan metabolism may serve as a monitoring pathway for AMI\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Therefore, we conducted an in-depth exploration of the role of TrM in AMI. In this study, we comprehensively analyzed the association between tryptophan metabolism-related genes and the pathological mechanisms of AMI, ultimately identifying three biomarkers (ADM, MCEMP1, and TSPO). On this basis, we observed a significant association of these biomarkers with the immune-inflammatory response. Furthermore, we found that monocytes play a vital role in the immune microenvironment of AMI. Single-cell data analysis results further validated the role of monocytes in AMI and identified potential regulatory transcription factors TFEC and CEBPD.\u003c/p\u003e \u003cp\u003eIntegrating WGCNA and differential expression analysis, we screened 31 candidate genes. Subsequently, five candidate biomarkers were obtained from multiple machine learning algorithms. We further confirmed the expression levels of these genes in the training and validation sets, and eventually recognized three biomarkers (ADM, MCEMP1, and TSPO). ADM, also known as adrenomedullin, is a multifunctional peptide hormone. Its main function involves vasodilation, regulation of blood pressure, and protection against oxidative stress\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In AMI, ADM can reduce size by promoting angiogenesis and inhibiting cardiomyocyte apoptosis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Additionally, elevated ADM levels may serve as a compensatory mechanism in patients following AMI to counteract the adverse effects of ischemia-reperfusion, thereby improving cardiac function\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Mast cell expressed membrane protein 1 (MCEMP1) primarily functions in mast cell activation and immune response regulation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The role of MCEMP1 in AMI is unclear. Given the involvement of mast cells in the inflammatory response during AMI, MCEMP1 may influence the inflammatory process associated with AMI by regulating mast cell degranulation and inflammatory mediator release\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The transporter protein TSPO, a mitochondrial external membrane protein, plays a vital role in regulating cholesterol transport and steroidogenesis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. TSPO protects cardiomyocytes from apoptosis as well as enhances mitochondrial function to reduce oxidative stress, thereby inhibiting the progression of AMI\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, TSPO may modulate the immune response in the heart by interacting with immune cells, thus influencing the post-AMI remodeling process and overall cardiac function\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMolecular docking results revealed good binding affinity between ONO-2952 and TSPO, which was subsequently validated by 100 ns molecular dynamics (MD) simulations. The results demonstrated that the interaction between ONO-2952 and TSPO also resulted in good stability in the protein's overall structure, internal stability, and compactness. MD is currently widely used in drug discovery and in understanding protein-drug interactions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This computational tool can reduce experimental resources and enable rapid molecular screening\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Overall, this approach is contributing to a deeper understanding of the pathological mechanisms of AMI.\u003c/p\u003e \u003cp\u003eThe immune infiltration analysis results clearly demonstrate significant alterations in the immune microenvironment of patients with AMI. The three identified biomarkers are predominantly associated with the infiltration of monocytes and neutrophils, highlighting their crucial roles within the immune microenvironment. Monocytes, a type of leukocyte that circulates in the bloodstream, play a key role in the innate immune response by phagocytosis and digestion of pathogens\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In AMI, dangerous molecules released by damaged myocardial cells allure monocytes to migrate to the injury area, where they distinguish into macrophages to clear dead cells\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. However, this leads to immoderate accumulation of monocytes at the injury area, thereby increasing the magnitude of infarction damage in patients\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Additionally, monocytes secrete certain cytokines to expand the inflammatory response\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Neutrophils, as the first responders to the injured site, combat bacteria by releasing antimicrobial substances and phagocytosing pathogens\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Therefore, a key feature of the early immune response in AMI is an elevation in neutrophil levels. However, while clearing pathogens, they also damage normal myocardial tissue, leading to ventricular remodeling and heart failure\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe analyzed the GSE269269 dataset to identify a total of nine cell types, and monocytes were confirmed as a key cell population in AMI by AUC scores. The SCENIC analysis was also applied to identify the possible regulatory TFs TFEC and CEBPD for monocytes. TFEC is a member of the microphthalmia-associated TF family, and plays a role in cell-specific gene regulation\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Significant upregulation of TFEC in monocytes suggests an enhanced immune response in AMI, exacerbating tissue damage\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. CEBPD, a member of the CCAAT/enhancer-binding protein family, primarily regulates cell proliferation, differentiation, and inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. CEBPD upregulation increases proinflammatory cytokine secretion by monocytes, subsequently contributing to systemic inflammation in AMI patients\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Moreover, it also affects the long-term prognosis of AMI patients by regulating the balance between cell survival and apoptosis in cardiac tissue\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study elucidates the role of tryptophan metabolism in the pathogenesis of AMI and identifies three diagnostic biomarkers. However, several limitations must be acknowledged. First, as our analysis relies on publicly available datasets, reliance on publicly available datasets entails inherent population bias, while the relatively small sample size may introduce result bias. Second, due to sample size limitations, our qPCR results are not universally applicable. Therefore, our future research will focus more on in vivo and in vitro experiments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we integrated bulk and single-cell transcriptomic data to systematically elucidate the critical involvement of tryptophan metabolism-related genes in AMI pathogenesis. Our study identified three robust diagnostic biomarkers-ADM, MCEMP1, and TSPO-that demonstrate high clinical discriminative power. Immune infiltration analysis demonstrated monocytes and neutrophils as key cellular mediators of AMI progression, with parallel transcriptional regulatory analysis identifying TFEC and CEBPD as master regulators targeting TSPO. By drug prediction and molecular docking, potential therapeutic targets and candidate drugs were screened, providing a novel insight for AMI treatment.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMRGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etryptophan metabolism-related genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eweighted gene co-expression network analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eXGBoost\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextreme gradient boosting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erandom forest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSVM-RFE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esupport vector machine-recursive feature elimination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e \u003cstrong\u003eLei Wang\u003c/strong\u003e, Conceptualization, Design, Methodology, Data curation, Writing - original draft, Writing - review and editing. \u003cstrong\u003eChengmin Tao\u003c/strong\u003e, Design, Methodology, writing - review and editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Anonymized clinical samples were analyzed in this study, and the need for informed consent was waived by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e All the RNA-sequencing data and single-cell RNA-sequencing data of acute myocardial infarction patients were acquired from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiao PD, Chen KJ, Ge JB, Zhang MZ. 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Cell Death Discov. 2025;11(1):102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JL, Xiao D, Liu YJ, Wang Z, Chen ZH, Li R, et al. Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy. Sci Rep. 2025;15(1):15017.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tryptophan metabolism, Acute myocardial infarction, Machine learning, Immunity, Molecular dynamics, Single-cell transcriptomics","lastPublishedDoi":"10.21203/rs.3.rs-8783966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8783966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEmerging evidence indicates that intermediates of tryptophan metabolism were decreased in acute myocardial infarction (AMI) patients. However, underlying mechanisms linking tryptophan metabolism to AMI pathogenesis remain poorly characterized. This study systematically investigates the role of tryptophan metabolism in AMI through multi-omics integration.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTryptophan metabolism-related genes (TMRGs) were retrieved from the MSigDB database and analyzed using weighted gene co-expression network analysis and differential expression analysis to identify AMI-associated candidates. Four machine-learning algorithms (LASSO regression, XGBoost, RF, and SVM-RFE) were applied to screen biomarkers and construct a diagnostic model, which was subsequently validated by qPCR. Gene set enrichment, immune infiltration, and regulatory network analyses were performed to elucidate biomarker functions. Molecular docking identified potential target drugs, followed by 100 ns molecular dynamics simulations of drug molecules and target proteins using the GAFF force field. Single-cell data were employed to identify key cell populations and transcriptional regulators.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive candidate biomarkers were identified, among which ADM, MCEMP1, and TSPO were selected to establish a diagnostic model with potential clinical utility. Immune infiltration analysis implicated monocytes and neutrophils in AMI progression and demonstrated their significant correlation with these biomarkers. Molecular docking revealed a strong binding affinity between TSPO and ONO-2952, which was confirmed as stable by molecular dynamics simulations. Single-cell and SCENIC analyses further highlighted monocytes as central players in AMI and identified TFEC and CEBPD as key transcription factors regulating biomarker expression.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eOur findings suggest that dysregulation of tryptophan metabolism contributes to AMI progression mainly through immune cell activation and inflammatory remodeling. The identified biomarkers-ADM, MCEMP1, and TSPO-may bridge metabolic disturbances and immune dysfunction, providing mechanistic insights into AMI pathology. Furthermore, the interaction between TSPO and ONO-2952 highlights the therapeutic potential of targeting metabolic-immune crosstalk in cardiovascular disease.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study comprehensively investigates the association between tryptophan metabolism and acute myocardial infarction, identifying three biomarkers and two therapeutic targets. Our findings provide a novel perception of AMI pathogenesis and give to the diagnosis of AMI.\u003c/p\u003e","manuscriptTitle":"Tryptophan metabolism-related biomarkers ADM, MCEMP1, and TSPO in acute myocardial infarction: insights from bioinformatics and machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:51:07","doi":"10.21203/rs.3.rs-8783966/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1510d7a-51bc-4b9e-a0e0-12941d27ce90","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T06:56:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 11:51:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8783966","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8783966","identity":"rs-8783966","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}