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

doi:10.21203/rs.3.rs-3866722/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-3866722/v1

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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.

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.

The study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Harbin, China). Written informed consent was obtained from all patients.

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 < 0.05 coupled with a fold change (FC) > 3/2 or < 2/3 (|log FC| > 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.

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 < 2/3 (|log2(FC)| > 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. 1A) were used to represent all DEGs. Additionally, the heat map (Fig. 1B) 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. 2A) 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. 2A. 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. 2B).

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. 2C). 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. 2D).

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. 3A). 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. 3B). A network diagram displayed the relationships between the top 20 terms (Fig. 3C) .

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. 4A). 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. 4B-D and 4F-G, all P < 0.05). Despite the expression of IL-1β in AMI, there was no significant difference in its expression between the two groups (P > 0.05) (Fig. 4E). 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. 4H-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(×10⁹)	9.59 ± 3.28	10.97 ± 3.35	8.21 ± 2.62	0.004
Neutrophils(×10⁹)	6.14(4.89,10.29)	8.65(5.75,11.90)	4.91(4.25,6.84)	0.005
Lymphocyte(×10⁹)	1.91 ± 0.78	1.92 ± 0.82	1.90 ± 0.76	0.955
Monocyte(×10⁹)	0.40(0.28,0.54)	0.39(0.27,0.53)	0.43(0.30,0.56)	0.664
Eosinophils(×10⁹)	0.06(0.01,0.13)	0.03(0.00,0.13)	0.07(0.03,0.16)	0.203
Basophils(×10⁹)	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. 5A-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. 5G-K). In addition, as shown in Figs. 5F and 5L, 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. 6A-E), in accordance with the validation dataset findings, proposing that the results were convincing.

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.

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.

CAD

coronary artery disease

OCT

optical coherence tomography

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

diameter stenosis

PCI

percutaneous coronary intervention

FCT

fibrous cap thickness

standard deviation

IVUS

intravascular ultrasound

STEM

ST-segment elevation myocardial infarction

Author Contribution

Author 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.

Statement of competing interests

The authors report no competing interests.

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No competing interests reported.

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

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Abstract

Background

Objective

Methods and results

Conclusions

Figures

Introduction

Materials and Methods

Data collection and processing

Identification of differentially expressed genes (DEGs)

Single sample Gene Set Enrichment Analysis (ssGSEA)

Functional enrichment analysis

Weighted gene co-expression network analysis (WGCNA)

Protein–protein interaction (PPI) network establishment and hub gene identification

Collection of clinical information

Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)

Statistical analysis

Results

Identification of DEGs in AMI

Construction of weighted gene co-expression network and module identification

Gene Ontology (GO) and pathway enrichment analysis

Screening and validation of hub genes

Associations of Inflammation-related blood markers with TYROBP

Receiver operating characteristic (ROC) curve analysis

Verification of potential biomarker expression in clinical samples by qRT-PCR

Discussion

Conclusion

Abbreviations

Declarations

Author Contribution

Acknowledgements:

References

Additional Declarations

Supplementary Files

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