Identification of marophage-related biomarkers in acute myocardial infarction (AMI) by bioinformatic analysis and clinical validation

Identification of marophage-related biomarkers in acute myocardial infarction (AMI) by bioinformatic analysis and clinical validation | 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 Identification of marophage-related biomarkers in acute myocardial infarction (AMI) by bioinformatic analysis and clinical validation Xiangwen Xi, Yu Chen, Zhipeng Qian, Xianwei Xie, Jiangtian Tian, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3986880/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 Although reperfusion therapy is widely performed in patients with acute myocardial infarction (AMI), the residual risk of poor prognosis remains substantial. As important immune cells involved in the body's inflammatory response, macrophages are differentiated from monocytes that have been recruited to tissues, and their polarisation status has a significant impact on the development and prognosis of AMI. There are no recognised macrophage-associated key regulators that play an important role in the development of AMI. Objective The study aimed to identify potential biomarkers associated with macrophages for the early recognition and intervention of AMI. Methods and results Three datasets which can be obtained publicly (GSE48060, GSE66360, and GSE97320 datasets) from the Gene Expression Omnibus (GEO) database were analysed to identify differentially expressed genes (DEGs) using peripheral blood tissue samples from 83 AMI patients and 74 normal individuals. Subsequent WGCNA analysis was performed and 387 genes with the most significant correlations with macrophages were identified. Then, intersecting 192 DEGs with 387 genes from WGCNA, a total of 151 overlapping genes were found. Protein-protein interaction (PPI) network analysis were performed to identify the hub genes. Further we recruited 44 individuals and colleted blood samples to validate the stability and reliability of the predicted hub tragets toll-like receptor 2 (TLR2), toll-like receptor 2 (TLR4), toll-like receptor 8 (TLR8), matrix metalloproteinase 9 (MMP9) and tyrosine kinase binding protein (TYROBP) using qRT-PCR assay. As a result, TLR2, TLR4, TLR8, MMP9 and TYROBP were identified as the marophage-related biomarkers in AMI. Conclusions The macrophage-related genes TLR2, TLR4, TLR8, MMP9 and TYROBP may enable timely detection of AMI, leading to prompt intervention and better prognosis. AMI Biomarkers Macrophage TYROBP MMP9 TLRs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Acute myocardial infarction is one of the leading cause of morbidity and mortality[ 1 ]. The World Health Organization's top 10 causes of death in 2019 analysis shows that ischemic heart disease tops the list of 55.4 million deaths worldwide, accounting for 16% of the world’s total deaths[ 2 ]. The mortality rate of patients diagnosed or likely to have AMI is much higher than that of patients diagnosed or likely to have other coronary heart diseases. Although thrombolysis, percutaneous coronary intervention (PCI) and coronary artery bypass surgery can be actively used to restore myocardial reperfusion, which has led to a decrease in hospital complications and 28-day mortality in AMI patients, residual risk after AMI remains high[ 2 , 3 ]. Some myocardial injury markers in AMI, including N-terminal brain natriuretic peptide (NT-proBNP) and cardiac troponin I (cTnI) have been used for the assessment of the prognosis of AMI. However ,the biomarkers which can reveal the immune status after AMI remains unkown[ 4 ]. In recent years, there has been a growing understanding of the role of the immune system in AMI. Studies have shown that when AMI occurs, immune cells communicate with each other through processes such as activation, differentiation, and migration, forming a network that connects various organs[ 5 ], which activates a strong defense function during myocardial infarction and delays the progression of AMI to a certain extent[ 6 – 9 ]. The affordable and readily accessible blood white blood cells (WBCs) and their categorical counts have the potential to be a useful predictor and prognostic marker. Previous studies have established that WBCs and their counts are independent predictors of myocardial infarction development. Recent studies have consistently demonstrated that increased neutrophil and monocyte counts are risk factors for the development and prognosis of AMI[ 10 , 11 ] Moreover, it has been established that neutrophil to lymphocyte ratio (NLR) is a reliable indicator for inflammation and stress levels. Nonetheless, limited studies have explored the association between macrophage ratio in peripheral blood and AMI. The polarization status of macrophages is significantly linked to the stability of atherosclerotic plaques, particularly during the proliferative phase of post-infarction myocardial recovery. Given the plasticity of macrophages and their pivotal role in atherosclerosis, exploration of macrophage-associated biomarkers may uncover fresh perspectives for timely identification and targeted treatment of AMI. In this study, we downloaded three datasets from the GEO database and analysed them to identify DEGs between AMI and healthy individuals. A weighted gene co-expression network (WGCNA) analysis was performed to identify potential biomarkers associated with macrophages in AMI. The degree of immune cell infiltration and the modules with the highest macrophage correlation coefficients were assessed using single sample gene set enrichment analysis (ssGSEA). The macrophage-associated hub genes involved in the progression of AMI were then identified using the protein–protein interaction (PPI) network constructed based on the STRING database. Validation at the mRNA level was subsequently performed on peripheral blood samples collected from clinical individuals. Materials and Methods Data collection and processing The gene expression profile data of AMI were downloaded from Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ). The expression profiling of three datasets GSE48060, GSE66360, and GSE97320 were based on the Affymetrix platform and the GSE60993 data set based on the Illumina platform. The data in these four databases are derived from peripheral blood samples collected from individuals with AMI and from healthy individuals. This helps to minimise the impact of variable sampling sites on the results of data analysis. Among them, the dataset GSE48060 comprised 21 normal samples and 31 AMI samples, GSE66360 comprised 50 normal samples and 49 AMI samples, GSE97320 comprised 3 normal samples and 3 AMI samples, and GSE60993 comprised 7 normal samples and 17 AMI samples. The GSE48060, GSE66360, and GSE97320 datasets were integrated and standardized through the "sva" R package. The resultant amalgamated dataset was labelled as the metadata dataset (74 normal samples and 83 AMI samples in total) and employed for subsequent analysis. In addition, the GSE60993 dataset served as the validation cohort. Identification of differentially expressed genes (DEGs) Differentially expressed genes (DEGs) were identified through implementation of the "limma" package in R software. Only values with a P-value 3/2 or 0.5849625) were recognised as statistically significant. Single sample Gene Set Enrichment Analysis (ssGSEA) The distributional characteristics of various immune cells in each sample of the metadata cohort were quantified using the Gene Enrichment Score (GSE) obtained with the Single Sample Genome Enrichment Analysis (ssGSEA) function, as provided in the R package GSVA. The relative abundance of immune cell types was utilized in subsequent analyses. Functional enrichment analysis To investigate the biological functions of genes intersected by modules and differentially expressed genes (DEGs), we conducted functional enrichment analysis using Metascape ( http://metascape.org ) in three categories: molecular functions, cellular components, and biological processes. Additionally, we created network diagrams to illustrate the relationships among the top 20 enriched pathways. Weighted gene co-expression network analysis (WGCNA) Firstly, the network topology analysis is utilised to determine the optimum soft threshold for scale-free networks. Following this, co-expression similarity is converted into a weighted network neighbourhood. Subsequently, the function "pick Soft Threshold" is utilized to determine the power of the optimal soft threshold β, which is subsequently employed to construct the co-expression network. This network is then employed to segment the genes into different gene modules, each comprising a distinct set of expression-related genes, which are assigned unique colours. Then, gene modules with the most significant correlation with macrophages were identified by heatmap. Soft threshold power was set to 7 (scale-free topology threshold = 0.8), cut height to 0.2, and minimum module size to 50 in identifying the key modules in the study. Protein–protein interaction (PPI) network establishment and hub gene identification The protein-protein interaction network was created using the STRING database to investigate the interaction relationship between overlapping genes. The top 5 hub genes were identified based on their degree value. Collection of clinical information After verifying the absence of all of the following conditions (1.) malignant tumours, (2.) severe infections, (3.) severe immune system disorders, and (4.) severe renal insufficiency, baseline information as well as blood samples of patients diagnosed with acute myocardial infarction (AMI) in the the Cardiology Intensive Care Unit, Second Affiliated Hospital of Harbin Medical University during the period from January 2022 to March 2022 were collected by the researchers. Informed consent were obtained from all participants, and diagnostic criteria for AMI were based on the fourth edition of the universal definition of myocardial infarction published in 2018 [ 12 ]. All blood samples from the above patients were taken within 12 hours of the acute myocardial infarction (AMI). We also obtained basic information and blood samples from non-AMI patients who were hospitalised in the cardiology department during the same time period, with informed consent, to serve as a control group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) Total RNA was extracted from samples using TRIZOL (Invitrogen) and the concentration and purity of the RNAs extracted were subsequently assessed. Then according to the instructions of the reverse transcription kit (Takara, China), the reverse transcription was conducted. QRT-PCR was performed according to the protocol (Takara) on Applied Biosystems StepOne-Plus Real-Time PCR System and β-actin was applied as a reference gene to normalize the level of mRNA. The sequence of these primers was list in Table 1 . Table 1 The primer sequences of 5 selected genes. Hub gene Forward primer Reverse primer TLR2 ATGCCTGGCCCTCTCTACAA CTTGGGAATGCAGCCTGTTA TLR4 CAAGAACCTGGACCTGAGCTTTA GATTTGTCTCCACAGCCACCAG TLR8 TGGGAGAAATAGCCTCTGGG TCAGGGGCTGGAAATCATCT MMP9 GCTACCACCTCG AACTTTGAC TCAGTGAAGCGGTACATA GGG TYROBP TGTAAGTGATTGCAGTTGCTCT ATACGGCCTCTGTGTGTTGA β-actin TTTCCAGCCTTCCTTCTTGG GGCATAGAGGTCTTTACGGATG The sequence of these primers is from 5’ to 3’. Statistical analysis Statistical analyses were conducted using R 3.6.1 software and SPSS 26.0. For continuous data, normality was assessed using the Shapiro-Wilk test. Variables that were normally distributed were presented as mean ± standard deviation, and comparisons between two groups were made using the Student's t-test for independent samples. For non-normally distributed continuous variables, median and interquartile range were used, and comparisons were made with the non-parametric Mann-Whitney U test. Categorical variables were presented as a proportion and compared using Pearson's chi-square test and Fisher's exact probability test. Pearson’s or Spearman’s correlation coefficients were used to evaluate correlations between inflammation-related blood markers and specific hub gene. The diagnostic value of diverse hub gene mRNA expressions was identified through receiver operating characteristic curve (ROC) analyses in distinguishing samples diagnosed with AMI from normal samples. Statistical significance was considered at P < 0.05 for all analyses. Results Identification of DEGs in AMI In the present study, differential expression analysis was conducted on 83 acute myocardial infarction (AMI) samples and 74 normal samples in the metadata set. The cut-off criteria were set as a fold change rate > 3/2 or 0.5849625) and a p-value < 0.05. A total of 192 DEGs were identified, of which 170 genes were up-regulated and 22 genes were down-regulated. Volcano plots (Fig. 1 A) were used to represent all DEGs. Additionally, the heat map (Fig. 1 B) depicted the expression levels of DEGs. Construction of weighted gene co-expression network and module identification To identify immune-related genes within the metadata, ssGSEA analysis was performed. The heat map (Fig. 2 A) displays the distribution of various immune subpopulations across all samples, with distinct colours and heights representing the diverse types and percentages of immune cells in each sample. The heatmap displays the relative infiltration abundance of immune cells in each sample. Coloured bars at the top indicate phenotypic variability, and the colouring within the plot ranges from green to red, signifying low to high infiltration abundance. The heatmap indicates that activated CD8 + T cells, central memory CD4 + T cells and myeloid-derived suppressor cells (MDSC) are the primary infiltrating cells. Furthermore, there were distinct variations in the ratio of immune cells between the two groups. Higher levels of infiltrating abundance of macrophages, mast cells and neutrophils were identified in AMI samples when compared with the controls, as demonstrated in Fig. 2 A. Subsequently, we conducted a network topology analysis to determine the optimal soft threshold. The findings indicate that a scale-free fit index of 0.8 was achieved at an optimal soft power threshold of 7 in WGCNA (Fig. 2 B). To determine the immunopathogenesis, it is crucial to conduct clustering analysis of infiltrating immune cells in both AMI and control samples. Through hierarchical clustering, genes were categorised into various modules. Each module, given a specific colour, represents a group of expression-associated genes (Fig. 2 C). Furthermore, after analyzing the degree of correlation and difference between macrophages and each gene module displayed on the heat map, we ultimately excluded the brown module which had the highest correlation with macrophages (r = 0.77, P < 0.05) and encompassed 387 genes (Fig. 2 D). Gene Ontology (GO) and pathway enrichment analysis Intersecting the 192 differentially expressed genes with the 387 genes in the brown module, a total of 151 overlapping genes were identified (Fig. 3 A). The matascape database was then utilised to investigate the biological functions of these 151 overlapping genes. The top 20 most enriched terms were all associated with immune pathways and gene functions and the three most enriched were myeloid leukocyte activation, response to lipopolysaccharide and inflammatory response (Fig. 3 B). A network diagram displayed the relationships between the top 20 terms (Fig. 3 C) . Screening and validation of hub genes To investigate the protein-level connections between gene fragments, we created a PPI network using the online STRING database and visualised it with Cytoscape. Upregulated and downregulated nodes are marked in red and blue, correspondingly (Supplementary Fig. 1). The total network encompasses 135 nodes and 882 edges, where the nodes indicate proteins and the edges correspond to their interactions. Identify and select the top five key highest-scoring key genes, including TLR2 and TLR4 with a score of 57, TLR8 with a score of 49, IL-1β with a score of 46, MMP9 with a score of 45, and TYROBP with a score of 39 (Fig. 4 A). The expression levels of six hub genes were assessed using the GSE60993 dataset to ensure reliable and accurate results. The findings demonstrate a significant increase in TLR2, TLR4, TLR8, MMP9, and TYROBP expression in AMI samples compared to controls (Figs. 4 B-D and 4 F-G, all P 0.05) (Fig. 4 E). Eventually, TLR2, TLR4, TLR8, MMP9, and TYROBP were identified as hub genes. Associations of Inflammation-related blood markers with TYROBP A total of 22 patients with acute myocardial infarction (AMI) and 22 controls were included in this study. The baseline characteristics of all participants are displayed in Table 2 . The findings revealed no significant difference in age and gender between the two groups. However, the percentage of smoking, serum total cholesterol level (TG), and fasting blood glucose (FBG) level were significantly higher in the AMI group than in the control group. Myocardial injury markers like creatine kinase isoenzymes (CK-MB) and cardiac troponin I (cTnI), as well as inflammatory markers such as C-reactive protein (CRP), white blood cell count (WBC), and neutrophil count, were found to be significantly higher in the AMI group when compared to the control group. Correlation analysis showed that TYROBP was significantly associated with both CRP (r = 0.67, p < 0.05) and lymphocytes (r = 0.48, p < 0.05) (Fig. 4 H-I). Table 2 Baseline Characteristics of AMI patients and controls. Parameters All(n = 44) AMI(n = 22) Control(n = 22) P value Age(years) 63.77 ± 10.32 62.14 ± 8.64 65.41 ± 11.74 0.298 Male(%) 25(56.81%) 14(63.64%) 11(50%) 0.367 Smoking(%) 17(38.64%) 13(59.09%) 4(18.18%) 0.006 Diabetes Mellitus(%) 11(25.00%) 7 (31.82%) 4(18.18%) 0.302 Hypertension(%) 23(52.27%) 13(59.09%) 10(45.45%) 0.371 TC(mmol/l) 4.71 ± 1.30 5.13 ± 1.38 4.28 ± 1.08 0.028 TG(mmol/l) 1.47 ± 0.56 1.42 ± 0.60 1.50 ± 0.53 0.635 LDL-C(mmol/l) 2.97 ± 1.06 3.26 ± 1.14 2.68 ± 0.91 0.069 HDL-C(mmol/l) 1.30 ± 0.36 1.40 ± 0.39 1.19 ± 0.30 0.053 FBG(mmol/l) 6.86(5.55,7.99) 7.18(6.83,9.65) 5.55(4.92,6.68) 0.000 HbA1C(Hb%) 6.00(5.50,6.90) 6.05(5.58,7.43) 5.90(5.50,6.75) 0.295 CRP(mg/l) 3.71(1.04,9.65) 5.43(2.08,10.47) 1.04(0.23,4.13) 0.004 CK-MB(ng/ml) 1.40(0.50,9.80) 6.30(0.50,53.83) 0.80(0.30,1.60) 0.009 cTnI(ng/ml) 0.07(0.00,0.57) 0.52(0.06,17.25) 0.00(0.00,0.08) 0.000 LVEF(%) 61.00(55.00,63.00) 61.00(52.75,63.00) 63.00(57.00,63.00) 0.100 WBC(×10 9 ) 9.59 ± 3.28 10.97 ± 3.35 8.21 ± 2.62 0.004 Neutrophils(×10 9 ) 6.14(4.89,10.29) 8.65(5.75,11.90) 4.91(4.25,6.84) 0.005 Lymphocyte(×10 9 ) 1.91 ± 0.78 1.92 ± 0.82 1.90 ± 0.76 0.955 Monocyte(×10 9 ) 0.40(0.28,0.54) 0.39(0.27,0.53) 0.43(0.30,0.56) 0.664 Eosinophils(×10 9 ) 0.06(0.01,0.13) 0.03(0.00,0.13) 0.07(0.03,0.16) 0.203 Basophils(×10 9 ) 0.00(0.00,0.00) 0.00(0.00,0.00) 0.00(0.00,0.00) 0.974 AMI = acute myocardial infarction; TC = total cholesterol; TG = triglyceride; LDL-C = low-density lipoprotein cholesterol; HDL-C = high-density lipoprotein cholesterol; FBG = fasting blood glucose; HbA1C = hemoglobin A1C; CRP = c-reactive protein; CK-MB = creatine kinase isoenzymes; cTnI = cardiac troponin I; LVEF = left ventricle ejection fraction; WBC = white blood cell. Bold values means statistically significant. Receiver operating characteristic (ROC) curve analysis To determine whether these hub genes possess diagnostic worth for acute myocardial infarction, ROC analyses were carried out in the metadata cohort and validation dataset GSE60993, respectively. The results showed that three genes had area under the curve (AUC) values exceeding 0.70 (TLR2 = 0.763, TLR4 = 0.770, MMP9 = 0.834), suggesting that they possess exceptional specificity and sensitivity in the identification of AMI patients. Moreover, TLR8 and TYROBP had respective AUC values of 0.668 and 0.646 (Fig. 5 A-E). In the validation dataset GSE60993, the AUCs of TLR2, TLR4, TLR8, MMP9, and TYROBP were 0.849, 0.866, 0.899, 0.882, and 0.807, respectively (Fig. 5 G-K). In addition, as shown in Figs. 5 F and 5 L, the combined model of these five hub genes produces an AUC of 0.883 and 0.958 for the metadata cohort and GSE60993, respectively. These findings suggest that these genes have the potential to be regarded as candidate biomarkers for the diagnosis of AMI. Verification of potential biomarker expression in clinical samples by qRT-PCR To evaluate the expression of targeted genes in peripheral blood, we obtained samples from 22 individuals with AMI and 22 controls for qRT-PCR analysis. Subsequently, the expression levels of TLR2 (P = 0.0286), TLR4 (P = 0.0159), TLR8 (P = 0.0067), MMP9 (P = 0.0467) and TYROBP (P = 0.0023) were significantly up-regulated in the plasma of AMI patients compared to control subjects (Fig. 6 A-E), in accordance with the validation dataset findings, proposing that the results were convincing. Discussion Acute myocardial infarction (AMI) has garnered significant research attention in the cardiovascular field due to its high morbidity, lethality, and poor prognosis, leading to a substantial worldwide economic burden. Nevertheless, there is a lack of agreement on the optimal cardiac biomarkers. The immune response plays an essential role in the development and repair of AMI[ 13 ]., and macrophages, as an important component of intrinsic immunity, can be divided into pro-inflammatory M1-type macrophages and anti-inflammatory M2-type macrophages[ 14 ]. Different subtypes of macrophages are capable of performing essential functions in tissue repair after AMI, including phagocytosis of necrotic tissue cell debris, regulation of angiogenesis, and influence on fibrosis and scar formation. Investigating biomarkers related to macrophages is crucial in enhancing the prognosis, diagnosis and treatment of myocardial infarction. In this study, we performed a bioinformatic analysis of the GSE48060, GSE66360 and GSE97320 datasets from the Gene Expression Omnibus (GEO) database and identified 192 significantly altered DEGs in AMI. Next, the extent of immune cell infiltration in both AMI and normal individuals were quantified using Single-Sample Enrichment Analysis (ssGSEA). Weighted gene correlation network analysis (WGCNA) was then utilised to identify the brown gene modules (387 genes) with the strongest correlation coefficients with macrophages. There are a total of 151 overlapping genes in the brown gene module and DEGs. These 151 overlapping genes underwent Gene Ontology (GO) analysis and were found to be enriched in pathways related to immune inflammation such as: myeloid leukocyte activation, response to lipopolysaccharide and inflammatory response. Subsequently, six hub genes (TLR2, TLR4, TLR8, IL-1β, MMP9 and TYROBP) were screened for the top five degree values using the PPI network. Validation of the GSE60993 dataset and clinical blood samples using qRT-PCR resulted in the identification of five hub genes (TLR2, TLR4, TLR8, MMP9 and TYROBP) with significantly increased expression levels in AMI, which were identified as potential hub genes associated with macrophages. Furthermore, all five hub genes demonstrated considerable diagnostic value (all AUC > 0.8) in the validation set GSE60993 in relation to normal controls, and hence, they are considered potential novel prognostic biomarkers for AMI. Compared to prior research, our study offers fresh perspectives on the potential pathogenesis of AMI. Toll-like receptors (Toll-like receptors, TLRs) are the main pattern recognition receptors (PRRs) on mammalian cells[ 15 ]. It is expressed in numerous parenchymal cells, including cardiomyocytes, fibroblasts, and endothelial cells. It is predominantly present on cells that participate in host defence functions[ 16 ]. In recent studies, the signals of two forms of human TLR (TLR2 and TLR4) have been proved to play a pivotal role in the occurrence and development of coronary artery disease (CAD)[ 15 , 17 ]. TLR4 is significantly expressed and activated in human atherosclerotic plaques distributed by lipid-rich macrophages[ 17 ], while the level of TLR2 is reported to regulate the severity of experimental atherosclerosis[ 18 ]. Timmer et al. demonstrated that the binding of Toll-like receptor 4 (TLR4) to ligands leads to activation of NF-κB and subsequent production of proinflammatory factors such as IL-1β, IL-2, IL-6, among others. TLR4 plays a significant role in ventricular remodelling after acute myocardial infarction (AMI) by promoting inflammatory responses and degradation of extracellular matrix[ 19 ]. Several prior studies[ 20 – 22 ] also used bioinformatics technology to analyze the key genes in the occurrence and development of AMI and found that the expression levels of TLR2 and TLR4 in AMI patients were significantly higher than in normal samples. The results of our study are in accordance with those of the predecessors, but in addition to making full use of bioinformatics technology to screen out differentially expressed genes, we further rely on the blood samples of clinical patients in two divided groups to verify our results and the verification results recommend that TLR2 and TLR4 play crucial roles in the onset of AMI. Our investigation indicates that mRNA levels of matrix metalloproteinase 9 (MMP9) are significantly raised in patients with AMI. MMP9 is a member of the matrix metalloproteinase family (MMPs) and is widely distributed in the cardiovascular system[ 23 , 24 ]. Studies have shown that it may induce adverse cardiovascular events such as AMI by promoting the thinning of the fiber cap and destroying the stability of the plaque[ 25 – 27 ]. Previous studies have found that the elevated serum levels of MMP9 mainly come from coronary plaques in AMI patients[ 28 ]. MMP9 polymorphism and its expression level can be used as clinical biomarkers for early diagnosis of atherosclerosis and predicting future coronary revascularization that can affect the outcome of AMI[ 29 – 31 ]. In addition, Zhu et al. proposed that higher MMP9 levels are an independent predictor of hospital death in AMI patients undergoing emergency PCI[ 32 ]. The study from the present investigation merges bioinformatics analysis and clinical validation, revealing that MMP9 represents a vital immune-related up-regulated target in AMI patients. This finding is compatible with prior research and may be linked to MMPs' role in extracellular degradation of the extracellular matrix (ECM) proteins. The degradation of proteins, including elastin and collagen fibres, in the ECM components contributes to the formation of atherosclerotic plaques. This, in turn, leads to plaque rupture and subsequent AMI events. Consequently, elevated MMP9 levels can be detected in the peripheral blood of AMI patients. Furthermore, Timmer et al. demonstrated a decrease in MMP9 activity, a reduction in extracellular matrix degradation in the infarct zone, and a decrease in ventricular wall expansion in TLR4-deficient mice[ 19 ]. These findings correspond with the trend observed in our study and further confirm the association between TLR4, MMP9, and extracellular matrix after AMI. In this study, by combining bioinformatics analysis and clinical validation, we found that MMP9 was one of the predominant immune-associated target up-regulated in AMI patients. Protein tyrosine kinase binding protein (TYROBP), also called DAP12, encodes a transmembrane signaling molecule polypeptide. The protein encoded by it is mainly involved in bone remodeling, brain myelination, signal transduction and inflammatory response[ 33 – 35 ]. Studies have shown that TYROBP can bind to activated receptors on the surface of various immune cells in manner of non-covalent interaction, and then mediates signal transduction and cellular activation[ 36 – 38 ]. However, the previous report on TYROBP mainly focuses on Alzheimer’s disease[ 39 , 40 ]. Notably, we initially found that the expression level of TYROBP in AMI patients is significantly elevated, which suggests that TYROBP may plays a substantial role in the process. Previous research has indicated that during endogenous inflammatory responses, LPS stimulation results in enhanced secretion of TREM-1 receptors expressed on macrophages, culminating in an increase in TYROBP. This suggests that KARAP/DAP12-dependent signalling might amplify TLR-dependent inflammatory responses, which could be a potential mechanism to account for the rise in TYROBP following AMI. The study by Dai et al. showed that: TYROBP played an important role in the occurrence and progression of non-alcoholic fatty liver disease and AMI[ 41 ], which further verified our results, and provides a basis for us to explore the activation of immune-related signals and possible pathways and related targets after myocardial infarction. Furthermore, our study revealed a significant association between TYROBP and clinical inflammation indicators, specifically CRP (r = 0.67, p < 0.05) and lymphocyte ratio (r = 0.48, p < 0.05). Our study delivers valuable insights regarding molecular events linked to AMI and identifies potential biomarkers for detection and prevention. Nevertheless, we acknowledge the limitations of our study due to the use of public databases and the collection of blood samples from a single centre. Therefore, it is necessary to obtain a larger sample size from multiple centres to verify the reliability of TLR2, TLR4, TLR8, MMP9, and TYROBP as potential biomarkers for AMI. Future studies should also take into account the properties, such as cost and convenience, of these biomarkers. Furthermore, it should be noted that our study was retrospective in design. Future research could involve the use of animal models to investigate the underlying mechanisms and enhance the prognostic risk stratification for AMI. Conclusion In summary, we successfully screened and confirmed five macrophage-associated genes (TLR2, TLR4, TLR8, MMP9, and TYROBP) based on bioinformatic analysis and clincial sample validation. The current study suggested that TLR2, TLR4, TLR8, MMP9, and TYROBP significantly increased post-AMI and can be exploited as promising targets in treating AMI patients. Abbreviations CAD coronary artery disease OCT optical coherence tomography LI lipid index TCFA thin-cap fibroatheroma CVD cardiovascular disease ACS acute coronary syndrome CAAS Cardiovascular Angiography Analysis System QCA quantitative coronary angiography RVD reference vessel diameter MLD minimal lumen diameter DS diameter stenosis PCI percutaneous coronary intervention FCT fibrous cap thickness SD standard deviation IVUS intravascular ultrasound STEM ST-segment elevation myocardial infarction Declarations Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Second Affliated Hospital of Harbin Medical University and informed consent was obtained from all of the participants. Ethical Approval : This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Second Affliated Hospital of Harbin Medical University and informed consent was obtained from all of the participants. Funding: This project was supported by the National Natural Science Foundation of China (Grant No.82100529), the Medical and Clinical Youth Scientific Research Project of Harbin Medical University(Grant No.2020-KYYWF-1459), and the Key Laboratory of Myocardial Ischemia, Harbin Medical University, Ministry of Education (KF201704 to X. Xi). Availability of data and materials: The datasets used in our study are available from the corresponding author upon reasonable request. Consent for publication: All authors have contributed significantly and the manuscript has been approved for publication by all authors. Competing interests: There is no conflict of potential competing interest in the submission of this manuscript. Authors' contributions: The study was conceived by GX, FQ and XXW1. The study was designed by CY and XXW1. Bioinformatics analyses are mainly done by QZP. Data analysis was performed by CY. The collection of clinical blood samples and PCR experiments are mainly carried out by XXW1 and CY. TJT is responsible for experimental guidance and supervisionand. The article was written by CY and commented and revised by GX, FQ and XXW. All coauthors reviewed and approved the manuscript prior to submission. Acknowledgements: The authors would like to thank all the investigators and support staff involved in the completion of this study. References Yusuf, Hawken, Ôunpuu, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The Lancet. 2004;9438:937-952. Reis-Dennis. 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Cells of the Immune System in Cardiac Remodeling: Main Players in Resolution of Inflammation and Repair After Myocardial Infarction. Front Immunol. 2021;664457. Prabhu and Frangogiannis. The Biological Basis for Cardiac Repair After Myocardial Infarction: From Inflammation to Fibrosis. Circ Res. 2016;1:91-112. Latet, Hoymans, Van Herck and Vrints. The cellular immune system in the post-myocardial infarction repair process. Int J Cardiol. 2015;240-247. Yan, Jin, Zhang, et al. Differential leukocyte counts and cardiovascular mortality in very old patients with acute myocardial infarction: a Chinese cohort study. BMC Cardiovasc Disord. 2020;1:465. Ji, Liu, Guo, et al. The Neutrophil-to-Lymphocyte Ratio Is an Important Indicator Predicting In-Hospital Death in AMI Patients. Front Cardiovasc Med. 2021;706852. Ammirati and Moslehi. Diagnosis and Treatment of Acute Myocarditis: A Review. Jama. 2023;13:1098-1113. Xie, Wang, Zhao, Wang and Fang. Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy. Bioengineered. 2021;1:2890-2905. Sager, Hulsmans, Lavine, et al. Proliferation and Recruitment Contribute to Myocardial Macrophage Expansion in Chronic Heart Failure. Circ Res. 2016;7:853-864. Takeda, Kaisho and Akira. Toll-like receptors. Annu Rev Immunol. 2003;335-376. van Zoelen, Yang, Florquin, et al. Role of toll-like receptors 2 and 4, and the receptor for advanced glycation end products in high-mobility group box 1-induced inflammation in vivo. Shock. 2009;3:280-284. Kiechl, Wiedermann and Willeit. Toll-like receptor 4 and atherogenesis. Ann Med. 2003;3:164-171. Mullick, Tobias and Curtiss. Modulation of atherosclerosis in mice by Toll-like receptor 2. J Clin Invest. 2005;11:3149-3156. Timmers, Sluijter, van Keulen, et al. Toll-like receptor 4 mediates maladaptive left ventricular remodeling and impairs cardiac function after myocardial infarction. Circ Res. 2008;2:257-264. Guo, Wu, Zhou, et al. Identification and analysis of key genes associated with acute myocardial infarction by integrated bioinformatics methods. Medicine (Baltimore). 2021;15:e25553. Zhang, Liu, Liu, Qi and Deng. Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis. Medicine (Baltimore). 2017;47:e8375. Ishikawa, Satoh, Itoh, Minami, Takahashi and Akamura. Local expression of Toll-like receptor 4 at the site of ruptured plaques in patients with acute myocardial infarction. Clin Sci (Lond). 2008;4:133-140. Becirovic-Agic, Chalise, Daseke, et al. Infarct in the Heart: What's MMP-9 Got to Do with It? Biomolecules. 2021;4: Zamilpa, Ibarra, de Castro Bras, et al. Transgenic overexpression of matrix metalloproteinase-9 in macrophages attenuates the inflammatory response and improves left ventricular function post-myocardial infarction. J Mol Cell Cardiol. 2012;5:599-608. Galis, Sukhova, Lark and Libby. Increased expression of matrix metalloproteinases and matrix degrading activity in vulnerable regions of human atherosclerotic plaques. J Clin Invest. 1994;6:2493-2503. Chen, Meinertzhagen and Shaw. Circadian rhythms in light-evoked responses of the fly's compound eye, and the effects of neuromodulators 5-HT and the peptide PDF. J Comp Physiol A. 1999;5:393-404. Lahdentausta, Leskela, Winkelmann, et al. Serum MMP-9 Diagnostics, Prognostics, and Activation in Acute Coronary Syndrome and Its Recurrence. J Cardiovasc Transl Res. 2018;3:210-220. Higo, Uematsu, Yamagishi, et al. Elevation of plasma matrix metalloproteinase-9 in the culprit coronary artery in patients with acute myocardial infarction: clinical evidence from distal protection. Circ J. 2005;10:1180-1185. Gostiljac, Dordevic, Djuric, et al. The importance of defining serum MMP-9 concentration in diabetics as an early marker of the rupture of atheromatous plaque in acute coronary syndrome. Acta Physiol Hung. 2011;1:91-97. El-Aziz and Mohamed. Matrix metalloproteinase -9 polymorphism and outcome after acute myocardial infarction. Int J Cardiol. 2017;524-528. Wang, Huang, Chiang, et al. Usefulness of plasma matrix metalloproteinase-9 level in predicting future coronary revascularization in patients after acute myocardial infarction. Coron Artery Dis. 2013;1:23-28. Zhu, Zhao, Qu, et al. Usefulness of plasma matrix metalloproteinase-9 levels in prediction of in-hospital mortality in patients who received emergent percutaneous coronary artery intervention following myocardial infarction. Oncotarget. 2017;62:105809-105818. Turnbull and Colonna. Activating and inhibitory functions of DAP12. Nat Rev Immunol. 2007;2:155-161. Tomasello and Vivier. KARAP/DAP12/TYROBP: three names and a multiplicity of biological functions. Eur J Immunol. 2005;6:1670-1677. Colonna. DAP12 signaling: from immune cells to bone modeling and brain myelination. J Clin Invest. 2003;3:313-314. Lanier, Corliss, Wu and Phillips. Association of DAP12 with activating CD94/NKG2C NK cell receptors. Immunity. 1998;6:693-701. Lanier, Corliss, Wu, Leong and Phillips. Immunoreceptor DAP12 bearing a tyrosine-based activation motif is involved in activating NK cells. Nature. 1998;6668:703-707. Dietrich, Cella, Seiffert, Buhring and Colonna. Cutting edge: signal-regulatory protein beta 1 is a DAP12-associated activating receptor expressed in myeloid cells. J Immunol. 2000;1:9-12. Ma, Jiang, Tan and Yu. TYROBP in Alzheimer's disease. Mol Neurobiol. 2015;2:820-826. Zhang, Gaiteri, Bodea, et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell. 2013;3:707-720. Dai, Sun, Jiang, Du, Xia and Zhong. Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction. Med Sci Monit. 2020;e922492. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3986880","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276887534,"identity":"a46d91c8-da72-4989-a304-e56fecc9e93d","order_by":0,"name":"Xiangwen Xi","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangwen","middleName":"","lastName":"Xi","suffix":""},{"id":276887535,"identity":"a8840961-c1c7-4a8e-830d-5d5a50c9f7e0","order_by":1,"name":"Yu Chen","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Chen","suffix":""},{"id":276887536,"identity":"531520ed-14a4-4722-890e-23d994432986","order_by":2,"name":"Zhipeng Qian","email":"","orcid":"","institution":"College of Bioinformatics Science and Technology, Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhipeng","middleName":"","lastName":"Qian","suffix":""},{"id":276887537,"identity":"92daa313-04f8-474d-8604-1605022c7099","order_by":3,"name":"Xianwei Xie","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianwei","middleName":"","lastName":"Xie","suffix":""},{"id":276887538,"identity":"c603d30f-5baf-404c-b3cf-4dd5c0f44a9c","order_by":4,"name":"Jiangtian Tian","email":"","orcid":"","institution":"Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin","correspondingAuthor":false,"prefix":"","firstName":"Jiangtian","middleName":"","lastName":"Tian","suffix":""},{"id":276887539,"identity":"446cfbce-0de7-4379-af8a-76950e1809ed","order_by":5,"name":"Qiang Fu","email":"","orcid":"","institution":"Medical Diagnosis Teaching and Research Room, The College of Basic Medicine of Heilongjiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Fu","suffix":""},{"id":276887540,"identity":"ae7fdaed-ef09-4bc7-a346-f9c56b4f8fdd","order_by":6,"name":"Xia Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie2RMUsDMRTHEwLn8qRrDg7uK7xOUiw9P0pCobd0EFw6Bk46+QFO+j2sY44DuwRdA3WIi5PDdaubUcFBaM5RaH5Dhsf/9/g/Qkgk8h/hVKHQBJAQ6sRiDIMT9XeFoTOzLL3Rfcrn40NeSdKX63aM9iJs5KtKXTrTZmd86rhUT0Asod1ufljB58YXsy2M6hmivN8CXSmW3t4FFL8ZRdcCWoFCmi2wTCfsNKDk9Y9SdlouHyHhIqwQK7+LoZ0PlVxqgD4FvxRTApq3KyLMFDg0VfCWvC5fh/uH8wI35fp9v5gUxaZqul2omP8O/DWgKpj3MNeXiEQikSPnAzi6XBoDlhbTAAAAAElFTkSuQmCC","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xia","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2024-02-25 05:00:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3986880/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3986880/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52453051,"identity":"f06bd300-805e-478d-8ee4-89f58adc24a6","added_by":"auto","created_at":"2024-03-11 19:18:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":468141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes between AMI samples and normal samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot of differentially expressed genes (DEGs); Orange represents differentially expressed genes that are up-regulated, gray represents genes that are not differentially expressed, and green represents differentially expressed genes that are down-regulated. (B) Heatmap of DEGs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/6228775fe83f5d1e890fb732.png"},{"id":52453050,"identity":"2ee132c8-12e1-496b-a9e5-05340c082ad7","added_by":"auto","created_at":"2024-03-11 19:18:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":251305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-expression network analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)Heatmap of single-sample geneset enrichment analysis (ssGSEA) results. (B)Analysis of the network topology showed that the scale-free topology threshold of 0.8 was met when β = 7. (C)Cluster dendrogram between genes based on topological overlap. Each module represents a set of expression related genes while being assigned a specific color. (D)Heatmap shows the correlation and degree of difference between the gene modules and macrophage. Based on the correlation and p-value in each lattice, the brown module was selected\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/b19acb20d34d4d24b6070fd0.png"},{"id":52453053,"identity":"e6931b1c-b735-4c4c-8447-d5dce5fbab85","added_by":"auto","created_at":"2024-03-11 19:18:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":740861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)The Venn diagram shows the intersection between the genes in the brown module and the differentially expressed genes. (B)Bar plot of the top 20 terms enriched. (C)A network diagram displaying the relations of the 20 terms.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/404fc6dcf03fab9c6dc537f8.png"},{"id":52453055,"identity":"283012aa-1131-4aed-b0da-dccda5b3a13e","added_by":"auto","created_at":"2024-03-11 19:18:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of hub genes in GSE60993 dataset and correlation analysis between TYROBP and inflammatory indicators.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTop five genes with degree values identified by the PPI network. (B-G)Expression of hub genes in the GSE60993 dataset. (B)TLR2 (C)TLR4 (D)TLR8 (E) IL-1β (F)MMP9 (G) TYROBP. (H-I)Correlation analysis of TYROBP with clinical inflammatory indicators. (H)TYROBP and CRP. (I)TYROBP and LYM. LYM=lymphocyte. P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/20f357e4f6e3945724f51a49.png"},{"id":52453056,"identity":"df4a7f9a-4dfd-4dc1-9781-d45093c0ac80","added_by":"auto","created_at":"2024-03-11 19:18:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192233,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves for judging the diagnostic efficacy of the five hub genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the metadata cohort, TLR2 (A), TLR4 (B),TLR8 (C), MMP9 (D), TYROBP (E) as well as five diagnostic markers were fitted to the ROC curve for one variable (F) . In the GSE60993 dataset, TLR2 (G), TLR4 (H), TLR8 (I),MMP9 (J), TYROBP (K) as well as five diagnostic markers were fitted to the ROC curve for one variable (L). AUC=area under curve.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/fce329f2d48edf7f9f933248.png"},{"id":52453057,"identity":"399071b0-76e4-4916-9147-a813c17e8c83","added_by":"auto","created_at":"2024-03-11 19:18:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":558294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRT-qPCR validation of the five hub genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) TLR2. (B)TLR4. (C)TLR8. (D) MMP9. (E)TYROBP. P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/b9a1919f688f4c22cef1f97c.png"},{"id":52545462,"identity":"09276b6b-8eef-4168-9261-49916e9cd4ba","added_by":"auto","created_at":"2024-03-12 18:21:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2924727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/9e56f993-7f35-4a3b-b188-29a6d2073959.pdf"},{"id":52453052,"identity":"97c75ffe-52cb-481e-b03a-1bf36fded73e","added_by":"auto","created_at":"2024-03-11 19:18:58","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2289316,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.tif","url":"https://assets-eu.researchsquare.com/files/rs-3986880/v1/fec806c33aac7b72ac709f72.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of marophage-related biomarkers in acute myocardial infarction (AMI) by bioinformatic analysis and clinical validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myocardial infarction is one of the leading cause of morbidity and mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The World Health Organization's top 10 causes of death in 2019 analysis shows that ischemic heart disease tops the list of 55.4\u0026nbsp;million deaths worldwide, accounting for 16% of the world\u0026rsquo;s total deaths[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The mortality rate of patients diagnosed or likely to have AMI is much higher than that of patients diagnosed or likely to have other coronary heart diseases. Although thrombolysis, percutaneous coronary intervention (PCI) and coronary artery bypass surgery can be actively used to restore myocardial reperfusion, which has led to a decrease in hospital complications and 28-day mortality in AMI patients, residual risk after AMI remains high[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome myocardial injury markers in AMI, including N-terminal brain natriuretic peptide (NT-proBNP) and cardiac troponin I (cTnI) have been used for the assessment of the prognosis of AMI. However ,the biomarkers which can reveal the immune status after AMI remains unkown[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, there has been a growing understanding of the role of the immune system in AMI. Studies have shown that when AMI occurs, immune cells communicate with each other through processes such as activation, differentiation, and migration, forming a network that connects various organs[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which activates a strong defense function during myocardial infarction and delays the progression of AMI to a certain extent[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The affordable and readily accessible blood white blood cells (WBCs) and their categorical counts have the potential to be a useful predictor and prognostic marker. Previous studies have established that WBCs and their counts are independent predictors of myocardial infarction development. Recent studies have consistently demonstrated that increased neutrophil and monocyte counts are risk factors for the development and prognosis of AMI[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Moreover, it has been established that neutrophil to lymphocyte ratio (NLR) is a reliable indicator for inflammation and stress levels. Nonetheless, limited studies have explored the association between macrophage ratio in peripheral blood and AMI. The polarization status of macrophages is significantly linked to the stability of atherosclerotic plaques, particularly during the proliferative phase of post-infarction myocardial recovery. Given the plasticity of macrophages and their pivotal role in atherosclerosis, exploration of macrophage-associated biomarkers may uncover fresh perspectives for timely identification and targeted treatment of AMI.\u003c/p\u003e \u003cp\u003eIn this study, we downloaded three datasets from the GEO database and analysed them to identify DEGs between AMI and healthy individuals. A weighted gene co-expression network (WGCNA) analysis was performed to identify potential biomarkers associated with macrophages in AMI. The degree of immune cell infiltration and the modules with the highest macrophage correlation coefficients were assessed using single sample gene set enrichment analysis (ssGSEA). The macrophage-associated hub genes involved in the progression of AMI were then identified using the protein\u0026ndash;protein interaction (PPI) network constructed based on the STRING database. Validation at the mRNA level was subsequently performed on peripheral blood samples collected from clinical individuals.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and processing\u003c/h2\u003e \u003cp\u003eThe gene expression profile data of AMI were downloaded from Gene Expression Omnibus (GEO; \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). The expression profiling of three datasets GSE48060, GSE66360, and GSE97320 were based on the Affymetrix platform and the GSE60993 data set based on the Illumina platform. The data in these four databases are derived from peripheral blood samples collected from individuals with AMI and from healthy individuals. This helps to minimise the impact of variable sampling sites on the results of data analysis. Among them, the dataset GSE48060 comprised 21 normal samples and 31 AMI samples, GSE66360 comprised 50 normal samples and 49 AMI samples, GSE97320 comprised 3 normal samples and 3 AMI samples, and GSE60993 comprised 7 normal samples and 17 AMI samples. The GSE48060, GSE66360, and GSE97320 datasets were integrated and standardized through the \"sva\" R package. The resultant amalgamated dataset was labelled as the metadata dataset (74 normal samples and 83 AMI samples in total) and employed for subsequent analysis. In addition, the GSE60993 dataset served as the validation cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were identified through implementation of the \"limma\" package in R software. Only values with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 coupled with a fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;3/2 or \u0026lt;\u0026thinsp;2/3 (|log FC| \u0026gt; 0.5849625) were recognised as statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSingle sample Gene Set Enrichment Analysis (ssGSEA)\u003c/h2\u003e \u003cp\u003eThe distributional characteristics of various immune cells in each sample of the metadata cohort were quantified using the Gene Enrichment Score (GSE) obtained with the Single Sample Genome Enrichment Analysis (ssGSEA) function, as provided in the R package GSVA. The relative abundance of immune cell types was utilized in subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo investigate the biological functions of genes intersected by modules and differentially expressed genes (DEGs), we conducted functional enrichment analysis using Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org\u003c/span\u003e\u003cspan address=\"http://metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in three categories: molecular functions, cellular components, and biological processes. Additionally, we created network diagrams to illustrate the relationships among the top 20 enriched pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eWeighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eFirstly, the network topology analysis is utilised to determine the optimum soft threshold for scale-free networks. Following this, co-expression similarity is converted into a weighted network neighbourhood. Subsequently, the function \"pick Soft Threshold\" is utilized to determine the power of the optimal soft threshold β, which is subsequently employed to construct the co-expression network. This network is then employed to segment the genes into different gene modules, each comprising a distinct set of expression-related genes, which are assigned unique colours. Then, gene modules with the most significant correlation with macrophages were identified by heatmap. Soft threshold power was set to 7 (scale-free topology threshold\u0026thinsp;=\u0026thinsp;0.8), cut height to 0.2, and minimum module size to 50 in identifying the key modules in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtein\u0026ndash;protein interaction (PPI) network establishment and hub gene identification\u003c/h2\u003e \u003cp\u003eThe protein-protein interaction network was created using the STRING database to investigate the interaction relationship between overlapping genes. The top 5 hub genes were identified based on their degree value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCollection of clinical information\u003c/h2\u003e \u003cp\u003eAfter verifying the absence of all of the following conditions (1.) malignant tumours, (2.) severe infections, (3.) severe immune system disorders, and (4.) severe renal insufficiency, baseline information as well as blood samples of patients diagnosed with acute myocardial infarction (AMI) in the the Cardiology Intensive Care Unit, Second Affiliated Hospital of Harbin Medical University during the period from January 2022 to March 2022 were collected by the researchers. Informed consent were obtained from all participants, and diagnostic criteria for AMI were based on the fourth edition of the universal definition of myocardial infarction published in 2018 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. All blood samples from the above patients were taken within 12 hours of the acute myocardial infarction (AMI). We also obtained basic information and blood samples from non-AMI patients who were hospitalised in the cardiology department during the same time period, with informed consent, to serve as a control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Real-Time Polymerase Chain Reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from samples using TRIZOL (Invitrogen) and the concentration and purity of the RNAs extracted were subsequently assessed. Then according to the instructions of the reverse transcription kit (Takara, China), the reverse transcription was conducted. QRT-PCR was performed according to the protocol (Takara) on Applied Biosystems StepOne-Plus Real-Time PCR System and β-actin was applied as a reference gene to normalize the level of mRNA. The sequence of these primers was list in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eThe primer sequences of 5 selected genes.\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\u003eHub gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward primer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse primer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGCCTGGCCCTCTCTACAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTTGGGAATGCAGCCTGTTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAAGAACCTGGACCTGAGCTTTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATTTGTCTCCACAGCCACCAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGGAGAAATAGCCTCTGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCAGGGGCTGGAAATCATCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCTACCACCTCG AACTTTGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCAGTGAAGCGGTACATA GGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTAAGTGATTGCAGTTGCTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATACGGCCTCTGTGTGTTGA\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\u003eTTTCCAGCCTTCCTTCTTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCATAGAGGTCTTTACGGATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe sequence of these primers is from 5\u0026rsquo; to 3\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using R 3.6.1 software and SPSS 26.0. For continuous data, normality was assessed using the Shapiro-Wilk test. Variables that were normally distributed were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and comparisons between two groups were made using the Student's t-test for independent samples. For non-normally distributed continuous variables, median and interquartile range were used, and comparisons were made with the non-parametric Mann-Whitney U test. Categorical variables were presented as a proportion and compared using Pearson's chi-square test and Fisher's exact probability test. Pearson\u0026rsquo;s or Spearman\u0026rsquo;s correlation coefficients were used to evaluate correlations between inflammation-related blood markers and specific hub gene. The diagnostic value of diverse hub gene mRNA expressions was identified through receiver operating characteristic curve (ROC) analyses in distinguishing samples diagnosed with AMI from normal samples. Statistical significance was considered at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs in AMI\u003c/h2\u003e \u003cp\u003eIn the present study, differential expression analysis was conducted on 83 acute myocardial infarction (AMI) samples and 74 normal samples in the metadata set. The cut-off criteria were set as a fold change rate\u0026thinsp;\u0026gt;\u0026thinsp;3/2 or \u0026lt;\u0026thinsp;2/3 (|log2(FC)| \u0026gt; 0.5849625) and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 192 DEGs were identified, of which 170 genes were up-regulated and 22 genes were down-regulated. Volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) were used to represent all DEGs. Additionally, the heat map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) depicted the expression levels of DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of weighted gene co-expression network and module identification\u003c/h2\u003e \u003cp\u003eTo identify immune-related genes within the metadata, ssGSEA analysis was performed. The heat map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) displays the distribution of various immune subpopulations across all samples, with distinct colours and heights representing the diverse types and percentages of immune cells in each sample. The heatmap displays the relative infiltration abundance of immune cells in each sample. Coloured bars at the top indicate phenotypic variability, and the colouring within the plot ranges from green to red, signifying low to high infiltration abundance. The heatmap indicates that activated CD8\u003csup\u003e+\u003c/sup\u003e T cells, central memory CD4\u003csup\u003e+\u003c/sup\u003e T cells and myeloid-derived suppressor cells (MDSC) are the primary infiltrating cells. Furthermore, there were distinct variations in the ratio of immune cells between the two groups. Higher levels of infiltrating abundance of macrophages, mast cells and neutrophils were identified in AMI samples when compared with the controls, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. Subsequently, we conducted a network topology analysis to determine the optimal soft threshold. The findings indicate that a scale-free fit index of 0.8 was achieved at an optimal soft power threshold of 7 in WGCNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo determine the immunopathogenesis, it is crucial to conduct clustering analysis of infiltrating immune cells in both AMI and control samples. Through hierarchical clustering, genes were categorised into various modules. Each module, given a specific colour, represents a group of expression-associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Furthermore, after analyzing the degree of correlation and difference between macrophages and each gene module displayed on the heat map, we ultimately excluded the brown module which had the highest correlation with macrophages (r\u0026thinsp;=\u0026thinsp;0.77, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and encompassed 387 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology (GO) and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eIntersecting the 192 differentially expressed genes with the 387 genes in the brown module, a total of 151 overlapping genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The matascape database was then utilised to investigate the biological functions of these 151 overlapping genes. The top 20 most enriched terms were all associated with immune pathways and gene functions and the three most enriched were myeloid leukocyte activation, response to lipopolysaccharide and inflammatory response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). A network diagram displayed the relationships between the top 20 terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eScreening and validation of hub genes\u003c/h2\u003e \u003cp\u003eTo investigate the protein-level connections between gene fragments, we created a PPI network using the online STRING database and visualised it with Cytoscape. Upregulated and downregulated nodes are marked in red and blue, correspondingly (Supplementary Fig.\u0026nbsp;1). The total network encompasses 135 nodes and 882 edges, where the nodes indicate proteins and the edges correspond to their interactions. Identify and select the top five key highest-scoring key genes, including TLR2 and TLR4 with a score of 57, TLR8 with a score of 49, IL-1β with a score of 46, MMP9 with a score of 45, and TYROBP with a score of 39 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The expression levels of six hub genes were assessed using the GSE60993 dataset to ensure reliable and accurate results. The findings demonstrate a significant increase in TLR2, TLR4, TLR8, MMP9, and TYROBP expression in AMI samples compared to controls (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Despite the expression of IL-1β in AMI, there was no significant difference in its expression between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Eventually, TLR2, TLR4, TLR8, MMP9, and TYROBP were identified as hub genes.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of Inflammation-related blood markers with TYROBP\u003c/h2\u003e \u003cp\u003eA total of 22 patients with acute myocardial infarction (AMI) and 22 controls were included in this study. The baseline characteristics of all participants are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The findings revealed no significant difference in age and gender between the two groups. However, the percentage of smoking, serum total cholesterol level (TG), and fasting blood glucose (FBG) level were significantly higher in the AMI group than in the control group. Myocardial injury markers like creatine kinase isoenzymes (CK-MB) and cardiac troponin I (cTnI), as well as inflammatory markers such as C-reactive protein (CRP), white blood cell count (WBC), and neutrophil count, were found to be significantly higher in the AMI group when compared to the control group. Correlation analysis showed that TYROBP was significantly associated with both CRP (r\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and lymphocytes (r\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH-I).\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\u003eBaseline Characteristics of AMI patients and controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAMI(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.77\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.41\u0026thinsp;\u0026plusmn;\u0026thinsp;11.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(56.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(63.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(38.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(59.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(18.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(25.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (31.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(18.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(52.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(59.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(45.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG(mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.86(5.55,7.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.18(6.83,9.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.55(4.92,6.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C(Hb%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00(5.50,6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.05(5.58,7.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.90(5.50,6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP(mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.71(1.04,9.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43(2.08,10.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04(0.23,4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40(0.50,9.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.30(0.50,53.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80(0.30,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecTnI(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07(0.00,0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52(0.06,17.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(0.00,0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.00(55.00,63.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.00(52.75,63.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.00(57.00,63.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC(\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils(\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.14(4.89,10.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.65(5.75,11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.91(4.25,6.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte(\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte(\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40(0.28,0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39(0.27,0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43(0.30,0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophils(\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06(0.01,0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03(0.00,0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07(0.03,0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils(\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAMI\u0026thinsp;=\u0026thinsp;acute myocardial infarction; TC\u0026thinsp;=\u0026thinsp;total cholesterol; TG\u0026thinsp;=\u0026thinsp;triglyceride; LDL-C\u0026thinsp;=\u0026thinsp;low-density lipoprotein cholesterol; HDL-C\u0026thinsp;=\u0026thinsp;high-density lipoprotein cholesterol; FBG\u0026thinsp;=\u0026thinsp;fasting blood glucose; HbA1C\u0026thinsp;=\u0026thinsp;hemoglobin A1C; CRP\u0026thinsp;=\u0026thinsp;c-reactive protein; CK-MB\u0026thinsp;=\u0026thinsp;creatine kinase isoenzymes; cTnI\u0026thinsp;=\u0026thinsp;cardiac troponin I; LVEF\u0026thinsp;=\u0026thinsp;left ventricle ejection fraction; WBC\u0026thinsp;=\u0026thinsp;white blood cell. Bold values means statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eReceiver operating characteristic (ROC) curve analysis\u003c/h2\u003e \u003cp\u003eTo determine whether these hub genes possess diagnostic worth for acute myocardial infarction, ROC analyses were carried out in the metadata cohort and validation dataset GSE60993, respectively. The results showed that three genes had area under the curve (AUC) values exceeding 0.70 (TLR2\u0026thinsp;=\u0026thinsp;0.763, TLR4\u0026thinsp;=\u0026thinsp;0.770, MMP9\u0026thinsp;=\u0026thinsp;0.834), suggesting that they possess exceptional specificity and sensitivity in the identification of AMI patients. Moreover, TLR8 and TYROBP had respective AUC values of 0.668 and 0.646 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-E). In the validation dataset GSE60993, the AUCs of TLR2, TLR4, TLR8, MMP9, and TYROBP were 0.849, 0.866, 0.899, 0.882, and 0.807, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-K). In addition, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL, the combined model of these five hub genes produces an AUC of 0.883 and 0.958 for the metadata cohort and GSE60993, respectively. These findings suggest that these genes have the potential to be regarded as candidate biomarkers for the diagnosis of AMI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eVerification of potential biomarker expression in clinical samples by qRT-PCR\u003c/h2\u003e \u003cp\u003eTo evaluate the expression of targeted genes in peripheral blood, we obtained samples from 22 individuals with AMI and 22 controls for qRT-PCR analysis. Subsequently, the expression levels of TLR2 (P\u0026thinsp;=\u0026thinsp;0.0286), TLR4 (P\u0026thinsp;=\u0026thinsp;0.0159), TLR8 (P\u0026thinsp;=\u0026thinsp;0.0067), MMP9 (P\u0026thinsp;=\u0026thinsp;0.0467) and TYROBP (P\u0026thinsp;=\u0026thinsp;0.0023) were significantly up-regulated in the plasma of AMI patients compared to control subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-E), in accordance with the validation dataset findings, proposing that the results were convincing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAcute myocardial infarction (AMI) has garnered significant research attention in the cardiovascular field due to its high morbidity, lethality, and poor prognosis, leading to a substantial worldwide economic burden. Nevertheless, there is a lack of agreement on the optimal cardiac biomarkers. The immune response plays an essential role in the development and repair of AMI[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]., and macrophages, as an important component of intrinsic immunity, can be divided into pro-inflammatory M1-type macrophages and anti-inflammatory M2-type macrophages[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Different subtypes of macrophages are capable of performing essential functions in tissue repair after AMI, including phagocytosis of necrotic tissue cell debris, regulation of angiogenesis, and influence on fibrosis and scar formation. Investigating biomarkers related to macrophages is crucial in enhancing the prognosis, diagnosis and treatment of myocardial infarction.\u003c/p\u003e \u003cp\u003eIn this study, we performed a bioinformatic analysis of the GSE48060, GSE66360 and GSE97320 datasets from the Gene Expression Omnibus (GEO) database and identified 192 significantly altered DEGs in AMI. Next, the extent of immune cell infiltration in both AMI and normal individuals were quantified using Single-Sample Enrichment Analysis (ssGSEA). Weighted gene correlation network analysis (WGCNA) was then utilised to identify the brown gene modules (387 genes) with the strongest correlation coefficients with macrophages. There are a total of 151 overlapping genes in the brown gene module and DEGs. These 151 overlapping genes underwent Gene Ontology (GO) analysis and were found to be enriched in pathways related to immune inflammation such as: myeloid leukocyte activation, response to lipopolysaccharide and inflammatory response.\u003c/p\u003e \u003cp\u003eSubsequently, six hub genes (TLR2, TLR4, TLR8, IL-1β, MMP9 and TYROBP) were screened for the top five degree values using the PPI network. Validation of the GSE60993 dataset and clinical blood samples using qRT-PCR resulted in the identification of five hub genes (TLR2, TLR4, TLR8, MMP9 and TYROBP) with significantly increased expression levels in AMI, which were identified as potential hub genes associated with macrophages. Furthermore, all five hub genes demonstrated considerable diagnostic value (all AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8) in the validation set GSE60993 in relation to normal controls, and hence, they are considered potential novel prognostic biomarkers for AMI. Compared to prior research, our study offers fresh perspectives on the potential pathogenesis of AMI.\u003c/p\u003e \u003cp\u003eToll-like receptors (Toll-like receptors, TLRs) are the main pattern recognition receptors (PRRs) on mammalian cells[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is expressed in numerous parenchymal cells, including cardiomyocytes, fibroblasts, and endothelial cells. It is predominantly present on cells that participate in host defence functions[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In recent studies, the signals of two forms of human TLR (TLR2 and TLR4) have been proved to play a pivotal role in the occurrence and development of coronary artery disease (CAD)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. TLR4 is significantly expressed and activated in human atherosclerotic plaques distributed by lipid-rich macrophages[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], while the level of TLR2 is reported to regulate the severity of experimental atherosclerosis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Timmer et al. demonstrated that the binding of Toll-like receptor 4 (TLR4) to ligands leads to activation of NF-κB and subsequent production of proinflammatory factors such as IL-1β, IL-2, IL-6, among others. TLR4 plays a significant role in ventricular remodelling after acute myocardial infarction (AMI) by promoting inflammatory responses and degradation of extracellular matrix[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Several prior studies[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] also used bioinformatics technology to analyze the key genes in the occurrence and development of AMI and found that the expression levels of TLR2 and TLR4 in AMI patients were significantly higher than in normal samples. The results of our study are in accordance with those of the predecessors, but in addition to making full use of bioinformatics technology to screen out differentially expressed genes, we further rely on the blood samples of clinical patients in two divided groups to verify our results and the verification results recommend that TLR2 and TLR4 play crucial roles in the onset of AMI.\u003c/p\u003e \u003cp\u003eOur investigation indicates that mRNA levels of matrix metalloproteinase 9 (MMP9) are significantly raised in patients with AMI. MMP9 is a member of the matrix metalloproteinase family (MMPs) and is widely distributed in the cardiovascular system[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Studies have shown that it may induce adverse cardiovascular events such as AMI by promoting the thinning of the fiber cap and destroying the stability of the plaque[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have found that the elevated serum levels of MMP9 mainly come from coronary plaques in AMI patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. MMP9 polymorphism and its expression level can be used as clinical biomarkers for early diagnosis of atherosclerosis and predicting future coronary revascularization that can affect the outcome of AMI[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, Zhu et al. proposed that higher MMP9 levels are an independent predictor of hospital death in AMI patients undergoing emergency PCI[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The study from the present investigation merges bioinformatics analysis and clinical validation, revealing that MMP9 represents a vital immune-related up-regulated target in AMI patients. This finding is compatible with prior research and may be linked to MMPs' role in extracellular degradation of the extracellular matrix (ECM) proteins. The degradation of proteins, including elastin and collagen fibres, in the ECM components contributes to the formation of atherosclerotic plaques. This, in turn, leads to plaque rupture and subsequent AMI events. Consequently, elevated MMP9 levels can be detected in the peripheral blood of AMI patients. Furthermore, Timmer et al. demonstrated a decrease in MMP9 activity, a reduction in extracellular matrix degradation in the infarct zone, and a decrease in ventricular wall expansion in TLR4-deficient mice[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These findings correspond with the trend observed in our study and further confirm the association between TLR4, MMP9, and extracellular matrix after AMI.\u003c/p\u003e \u003cp\u003eIn this study, by combining bioinformatics analysis and clinical validation, we found that MMP9 was one of the predominant immune-associated target up-regulated in AMI patients.\u003c/p\u003e \u003cp\u003eProtein tyrosine kinase binding protein (TYROBP), also called DAP12, encodes a transmembrane signaling molecule polypeptide. The protein encoded by it is mainly involved in bone remodeling, brain myelination, signal transduction and inflammatory response[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies have shown that TYROBP can bind to activated receptors on the surface of various immune cells in manner of non-covalent interaction, and then mediates signal transduction and cellular activation[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, the previous report on TYROBP mainly focuses on Alzheimer\u0026rsquo;s disease[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Notably, we initially found that the expression level of TYROBP in AMI patients is significantly elevated, which suggests that TYROBP may plays a substantial role in the process. Previous research has indicated that during endogenous inflammatory responses, LPS stimulation results in enhanced secretion of TREM-1 receptors expressed on macrophages, culminating in an increase in TYROBP. This suggests that KARAP/DAP12-dependent signalling might amplify TLR-dependent inflammatory responses, which could be a potential mechanism to account for the rise in TYROBP following AMI. The study by Dai et al. showed that: TYROBP played an important role in the occurrence and progression of non-alcoholic fatty liver disease and AMI[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], which further verified our results, and provides a basis for us to explore the activation of immune-related signals and possible pathways and related targets after myocardial infarction. Furthermore, our study revealed a significant association between TYROBP and clinical inflammation indicators, specifically CRP (r\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and lymphocyte ratio (r\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOur study delivers valuable insights regarding molecular events linked to AMI and identifies potential biomarkers for detection and prevention. Nevertheless, we acknowledge the limitations of our study due to the use of public databases and the collection of blood samples from a single centre. Therefore, it is necessary to obtain a larger sample size from multiple centres to verify the reliability of TLR2, TLR4, TLR8, MMP9, and TYROBP as potential biomarkers for AMI. Future studies should also take into account the properties, such as cost and convenience, of these biomarkers. Furthermore, it should be noted that our study was retrospective in design. Future research could involve the use of animal models to investigate the underlying mechanisms and enhance the prognostic risk stratification for AMI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we successfully screened and confirmed five macrophage-associated genes (TLR2, TLR4, TLR8, MMP9, and TYROBP) based on bioinformatic analysis and clincial sample validation. The current study suggested that TLR2, TLR4, TLR8, MMP9, and TYROBP significantly increased post-AMI and can be exploited as promising targets in treating AMI patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoronary artery disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoptical coherence tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elipid index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethin-cap fibroatheroma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute coronary syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Angiography Analysis System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equantitative coronary angiography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereference vessel diameter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eminimal lumen diameter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediameter stenosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epercutaneous coronary intervention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efibrous cap thickness\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintravascular ultrasound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eST-segment elevation myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate:\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Second Affliated Hospital of Harbin Medical University and informed consent was obtained from all of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Second Affliated Hospital of Harbin Medical University and informed consent was obtained from all of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the National Natural Science Foundation of China (Grant No.82100529), the Medical and Clinical Youth Scientific Research Project of Harbin Medical University(Grant No.2020-KYYWF-1459), and the Key Laboratory of Myocardial Ischemia, Harbin Medical University, Ministry of Education (KF201704 to X. Xi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in our study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have contributed significantly and the manuscript has been approved for publication by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of potential competing interest in the submission of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conceived by GX, FQ and XXW1. The study was designed by CY and XXW1. Bioinformatics analyses are mainly done by QZP. Data analysis was performed by CY. The collection of clinical blood samples and PCR experiments are mainly carried out by XXW1 and CY. TJT is responsible for experimental guidance and supervisionand. The article was written by CY and commented and revised by GX, FQ and XXW. All coauthors reviewed and approved the manuscript prior to submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the investigators and support staff involved in the completion of this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYusuf, Hawken, \u0026Ocirc;unpuu, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The Lancet. 2004;9438:937-952.\u003c/li\u003e\n\u003cli\u003eReis-Dennis. 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J Cardiol. 2016;6:498-503.\u003c/li\u003e\n\u003cli\u003eKologrivova, Shtatolkina, Suslova and Ryabov. Cells of the Immune System in Cardiac Remodeling: Main Players in Resolution of Inflammation and Repair After Myocardial Infarction. Front Immunol. 2021;664457.\u003c/li\u003e\n\u003cli\u003ePrabhu and Frangogiannis. The Biological Basis for Cardiac Repair After Myocardial Infarction: From Inflammation to Fibrosis. Circ Res. 2016;1:91-112.\u003c/li\u003e\n\u003cli\u003eLatet, Hoymans, Van Herck and Vrints. The cellular immune system in the post-myocardial infarction repair process. Int J Cardiol. 2015;240-247.\u003c/li\u003e\n\u003cli\u003eYan, Jin, Zhang, et al. Differential leukocyte counts and cardiovascular mortality in very old patients with acute myocardial infarction: a Chinese cohort study. BMC Cardiovasc Disord. 2020;1:465.\u003c/li\u003e\n\u003cli\u003eJi, Liu, Guo, et al. The Neutrophil-to-Lymphocyte Ratio Is an Important Indicator Predicting In-Hospital Death in AMI Patients. Front Cardiovasc Med. 2021;706852.\u003c/li\u003e\n\u003cli\u003eAmmirati and Moslehi. Diagnosis and Treatment of Acute Myocarditis: A Review. Jama. 2023;13:1098-1113.\u003c/li\u003e\n\u003cli\u003eXie, Wang, Zhao, Wang and Fang. Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy. Bioengineered. 2021;1:2890-2905.\u003c/li\u003e\n\u003cli\u003eSager, Hulsmans, Lavine, et al. Proliferation and Recruitment Contribute to Myocardial Macrophage Expansion in Chronic Heart Failure. Circ Res. 2016;7:853-864.\u003c/li\u003e\n\u003cli\u003eTakeda, Kaisho and Akira. Toll-like receptors. Annu Rev Immunol. 2003;335-376.\u003c/li\u003e\n\u003cli\u003evan Zoelen, Yang, Florquin, et al. Role of toll-like receptors 2 and 4, and the receptor for advanced glycation end products in high-mobility group box 1-induced inflammation in vivo. Shock. 2009;3:280-284.\u003c/li\u003e\n\u003cli\u003eKiechl, Wiedermann and Willeit. Toll-like receptor 4 and atherogenesis. Ann Med. 2003;3:164-171.\u003c/li\u003e\n\u003cli\u003eMullick, Tobias and Curtiss. Modulation of atherosclerosis in mice by Toll-like receptor 2. J Clin Invest. 2005;11:3149-3156.\u003c/li\u003e\n\u003cli\u003eTimmers, Sluijter, van Keulen, et al. Toll-like receptor 4 mediates maladaptive left ventricular remodeling and impairs cardiac function after myocardial infarction. Circ Res. 2008;2:257-264.\u003c/li\u003e\n\u003cli\u003eGuo, Wu, Zhou, et al. Identification and analysis of key genes associated with acute myocardial infarction by integrated bioinformatics methods. Medicine (Baltimore). 2021;15:e25553.\u003c/li\u003e\n\u003cli\u003eZhang, Liu, Liu, Qi and Deng. Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis. Medicine (Baltimore). 2017;47:e8375.\u003c/li\u003e\n\u003cli\u003eIshikawa, Satoh, Itoh, Minami, Takahashi and Akamura. Local expression of Toll-like receptor 4 at the site of ruptured plaques in patients with acute myocardial infarction. Clin Sci (Lond). 2008;4:133-140.\u003c/li\u003e\n\u003cli\u003eBecirovic-Agic, Chalise, Daseke, et al. Infarct in the Heart: What\u0026apos;s MMP-9 Got to Do with It? Biomolecules. 2021;4:\u003c/li\u003e\n\u003cli\u003eZamilpa, Ibarra, de Castro Bras, et al. Transgenic overexpression of matrix metalloproteinase-9 in macrophages attenuates the inflammatory response and improves left ventricular function post-myocardial infarction. J Mol Cell Cardiol. 2012;5:599-608.\u003c/li\u003e\n\u003cli\u003eGalis, Sukhova, Lark and Libby. Increased expression of matrix metalloproteinases and matrix degrading activity in vulnerable regions of human atherosclerotic plaques. J Clin Invest. 1994;6:2493-2503.\u003c/li\u003e\n\u003cli\u003eChen, Meinertzhagen and Shaw. Circadian rhythms in light-evoked responses of the fly\u0026apos;s compound eye, and the effects of neuromodulators 5-HT and the peptide PDF. J Comp Physiol A. 1999;5:393-404.\u003c/li\u003e\n\u003cli\u003eLahdentausta, Leskela, Winkelmann, et al. Serum MMP-9 Diagnostics, Prognostics, and Activation in Acute Coronary Syndrome and Its Recurrence. J Cardiovasc Transl Res. 2018;3:210-220.\u003c/li\u003e\n\u003cli\u003eHigo, Uematsu, Yamagishi, et al. Elevation of plasma matrix metalloproteinase-9 in the culprit coronary artery in patients with acute myocardial infarction: clinical evidence from distal protection. Circ J. 2005;10:1180-1185.\u003c/li\u003e\n\u003cli\u003eGostiljac, Dordevic, Djuric, et al. The importance of defining serum MMP-9 concentration in diabetics as an early marker of the rupture of atheromatous plaque in acute coronary syndrome. Acta Physiol Hung. 2011;1:91-97.\u003c/li\u003e\n\u003cli\u003eEl-Aziz and Mohamed. Matrix metalloproteinase -9 polymorphism and outcome after acute myocardial infarction. Int J Cardiol. 2017;524-528.\u003c/li\u003e\n\u003cli\u003eWang, Huang, Chiang, et al. Usefulness of plasma matrix metalloproteinase-9 level in predicting future coronary revascularization in patients after acute myocardial infarction. Coron Artery Dis. 2013;1:23-28.\u003c/li\u003e\n\u003cli\u003eZhu, Zhao, Qu, et al. Usefulness of plasma matrix metalloproteinase-9 levels in prediction of in-hospital mortality in patients who received emergent percutaneous coronary artery intervention following myocardial infarction. Oncotarget. 2017;62:105809-105818.\u003c/li\u003e\n\u003cli\u003eTurnbull and Colonna. Activating and inhibitory functions of DAP12. Nat Rev Immunol. 2007;2:155-161.\u003c/li\u003e\n\u003cli\u003eTomasello and Vivier. KARAP/DAP12/TYROBP: three names and a multiplicity of biological functions. Eur J Immunol. 2005;6:1670-1677.\u003c/li\u003e\n\u003cli\u003eColonna. DAP12 signaling: from immune cells to bone modeling and brain myelination. J Clin Invest. 2003;3:313-314.\u003c/li\u003e\n\u003cli\u003eLanier, Corliss, Wu and Phillips. Association of DAP12 with activating CD94/NKG2C NK cell receptors. Immunity. 1998;6:693-701.\u003c/li\u003e\n\u003cli\u003eLanier, Corliss, Wu, Leong and Phillips. Immunoreceptor DAP12 bearing a tyrosine-based activation motif is involved in activating NK cells. Nature. 1998;6668:703-707.\u003c/li\u003e\n\u003cli\u003eDietrich, Cella, Seiffert, Buhring and Colonna. Cutting edge: signal-regulatory protein beta 1 is a DAP12-associated activating receptor expressed in myeloid cells. J Immunol. 2000;1:9-12.\u003c/li\u003e\n\u003cli\u003eMa, Jiang, Tan and Yu. TYROBP in Alzheimer\u0026apos;s disease. Mol Neurobiol. 2015;2:820-826.\u003c/li\u003e\n\u003cli\u003eZhang, Gaiteri, Bodea, et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer\u0026apos;s disease. Cell. 2013;3:707-720.\u003c/li\u003e\n\u003cli\u003eDai, Sun, Jiang, Du, Xia and Zhong. Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction. Med Sci Monit. 2020;e922492.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"AMI, Biomarkers, Macrophage, TYROBP, MMP9, TLRs","lastPublishedDoi":"10.21203/rs.3.rs-3986880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3986880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough reperfusion therapy is widely performed in patients with acute myocardial infarction (AMI), the residual risk of poor prognosis remains substantial. As important immune cells involved in the body's inflammatory response, macrophages are differentiated from monocytes that have been recruited to tissues, and their polarisation status has a significant impact on the development and prognosis of AMI. There are no recognised macrophage-associated key regulators that play an important role in the development of AMI.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe study aimed to identify potential biomarkers associated with macrophages for the early recognition and intervention of AMI.\u003c/p\u003e\u003ch2\u003eMethods and results\u003c/h2\u003e \u003cp\u003eThree datasets which can be obtained publicly (GSE48060, GSE66360, and GSE97320 datasets) from the Gene Expression Omnibus (GEO) database were analysed to identify differentially expressed genes (DEGs) using peripheral blood tissue samples from 83 AMI patients and 74 normal individuals. Subsequent WGCNA analysis was performed and 387 genes with the most significant correlations with macrophages were identified. Then, intersecting 192 DEGs with 387 genes from WGCNA, a total of 151 overlapping genes were found. Protein-protein interaction (PPI) network analysis were performed to identify the hub genes. Further we recruited 44 individuals and colleted blood samples to validate the stability and reliability of the predicted hub tragets toll-like receptor 2 (TLR2), toll-like receptor 2 (TLR4), toll-like receptor 8 (TLR8), matrix metalloproteinase 9 (MMP9) and tyrosine kinase binding protein (TYROBP) using qRT-PCR assay. As a result, TLR2, TLR4, TLR8, MMP9 and TYROBP were identified as the marophage-related biomarkers in AMI.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe macrophage-related genes TLR2, TLR4, TLR8, MMP9 and TYROBP may enable timely detection of AMI, leading to prompt intervention and better prognosis.\u003c/p\u003e","manuscriptTitle":"Identification of marophage-related biomarkers in acute myocardial infarction (AMI) by bioinformatic analysis and clinical validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 19:18:53","doi":"10.21203/rs.3.rs-3986880/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":"86a67d79-6c44-4a21-9e72-a3d4d6cc8cdb","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-16T00:47:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 19:18:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3986880","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3986880","identity":"rs-3986880","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}