Identification of serum cytokines predicted the severity of coronary artery through neutrophil extracellular trap-related genes

Identification of serum cytokines predicted the severity of coronary artery through neutrophil extracellular trap-related genes | 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 Article Identification of serum cytokines predicted the severity of coronary artery through neutrophil extracellular trap-related genes Mengmeng Ren, Yanxin Ren, Yiming Wang, Zan Xie, Han Sun, Lin Zhong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8582008/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Acute Coronary Syndrome (ACS) threatens human health worldwide. Early noninvasive assessment of the severity of ACS is helpful for its screening, treatment and management. Neutrophil extracellular trapping nets (NETs) are networks produced by neutrophils which released after stimulation to capture and eliminate microorganisms. NETs have recently been found to have an important role in ACS. The aim of this study was to investigate NETs-associated genes (NRGs) during ACS and to identify their association with ACS severity in different populations. Methods Differential gene analysis and WGCNA analysis were performed using the data set in GEO database, and the genes obtained from the two analyses were intersected with NRGs to the key genes involved in regulating ACS. The resulting genes were subjected to protein-protein interaction network analysis and functional enrichment analysis. ACS and control patients were selected as the validation cohort, Elisa was used to detect the expression of key genes, univariate logistic regression analysis was performed, ROC curve was plotted, sensitivity, specificity, and optimal cut-off value (cut-off value) were calculated. Multivariate logistic regression analysis and subgroup analysis were performed according to the results of the difference analysis. Results In this study, CCL4, CXCL2, IL1β, IL8, CXCL1 and TNFAIP3 were selected as key NRGs in ACS by intersecting DEGs, WGCNA and NRGs. A total of 318 clinical samples (228 ACS and 90 controls) were collected as the validation cohort, and Elisa results showed that CCL4, CXCL2, IL1β, IL8, and CXCL1 was higher in ACS group, while TNFAIP3 expression was lower. Univariate logistic regression analysis showed that all six continuous variables were statistically significant for ACS. ROC curves showed that high expression of CCL4, CXCL2, IL1β, IL8, CXCL1 and low expression of TNFAIP3 were all associated with an increased risk of ACS. And IL1β, CXCL1, and TNFAIP3 were better predictive of ACS (AUC > 0.8). Multivariate logistics analysis of the overall and subgroup populations showed that these six NRGs were independent predictors of ACS in the overall population, but these six indicators showed different predictive effects in different subgroup populations. CCL4 and IL8 showed independent predictors of ACS in all subgroups, and the predictive effects were relatively stable. Conclunsion The key variables selected by NRGs can predict the severity of ACS, which provide some reference for the screening and treatment of ACS. Health sciences/Biomarkers Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Biological sciences/Immunology Health sciences/Risk factors coronary heart disease neutrophil extracellular trapping nets interleukin 8 CCL4 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute Coronary Syndrome (ACS) is currently the most common cause of death worldwide which incidence has risen from 27.3% to 31.4% in recent decades and continues to rise 1 – 2 . Although great progress has been made in the prevention and treatment of ACS over the past few years, it is still important to identify the factors influencing ACS to improve its prognosis because of the high mortality and morbidity causing a heavy burden on human health 3 . Neutrophil extracellular trapping nets (NETs) are extensive extracellular meshwork produced and released by activated neutrophils and are mainly composed of modified decondensed chromatin and granule proteins, including neutrophil elastase, myeloperoxidase, and histone 4 . NETs formation is mainly triggered by extracellular microbial and endogenous stimuli, such as pathogen-associated molecular patterns or damage-associated molecular patterns 5 – 6 . NETs were initially found to capture and kill extracellular pathogens and play a protective role in defense against pathogen infection. Recently, NETs have been demonstrated to play multiple roles in coronary atherosclerosis promoting plaque rupture and exacerbating intracoronary thrombus formation 7 – 10 . However, there is no comprehensive exploration of NETs-related genes (NRGs) in ACS. The aim of this study was to analyze the relationship between NRGs and ACS. First, the key genes in ACS were selected using differential gene analysis and WGCNA analysis using microarray data from the GEO database, and intersected with NRGs reported in the previous literature to identify the key NRGs associated with ACS. Subsequently, serum from control and ACS patients were clinically collected, and Elisa was used to detecte the NRGs and verified the correlation with ACS. The aim of this study was to explore the expression changes of NRGs in ACS and look forward to providing new targets for the treatment in the future. Methods Sources bioinformatics analysis data The datasets GSE113079 in this study were derived from the GEO database ( https://www.ncbi.nlm.nih.gov ), in which 93 patients were included in the ACS group and 48 patients in the control group. R 4.3.2 was applied to complete data download and preprocessing. Genes involved in NETs were identified based on previous literature 11 . Indentification of NRGs in ACS The "limma" package was applied to obtain the differential expressed genes (DEGs) in patients with ACS and controls. DEGs were considered when adj.P 1. And according to the “ggplot2”, “ggVolcano” and “pheatmap” package to draw volcano plot and heat map, respectively, to complete the visualization of DEGs. Weighted Gene Co-Expression Network Analysis (WGCNA) was also performed according to the “WGCNA” package. The genes were divided into different modules. The correlation between the modules and ACS is calculated, respectively, and the genes with MM > 0.8 in the modules strongly associated with ACS were defined as the hub genes. DEGs, hub genes and NETs genes were intersected to obtain NRGs involved in ACS. PPI network construction and enrichment of functional pathways According to the STRING website (Fig. https://cn.string-db.org/ ), protein – protein interaction (PPI) network analysis was performed and the results were visualized according to the "cytohubba" plugin in cytoscape software. In order to understand the potential biological functions and pathways involved in the NRGs, “clusterProfiler”, “org.Hs.eg.db”, “enrichplot” and “GOplot” packages were used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. Participants In this retrospective case–control study we enrolled 228 consecutive patients with first-admission acute coronary syndrome who had been admitted to Yantai Yuhuangding Hospital between October 2023 and March 2024 and whose leftover serum samples were stored in the hospital’s clinical biobank. All cases were confirmed by coronary angiography showing ≥ 30% stenosis of the left main trunk or ≥ 50% stenosis in one or more major coronary branches. Meanwhile, 90 patients who underwent coronary angiography or contrast-enhanced CT of the coronary arteries to rule out ACS were selected as controls. Exclusion criteria were: (1) congenital heart disease, cardiomyopathy, valvular disease, arrhythmia, heart failure and other heart diseases; (2) previous treatment in other hospital due to ACS and received treatment; (3) acute myocardial infarction or cerebral infarction in the past 6 months; (4) acute or chronic inflammatory diseases; (5) elevated CRP; (6) hematological diseases; (7) rheumatic system diseases, such as systemic lupus erythematosus, gout, etc.; (8) taking immunosuppressive agents, glucocorticoids; (8) severe liver and kidney dysfunction; (9) suffering from malignant tumors. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Ethics Committee of Yantai YuhuangdingHospital (No 2024 − 233). Informed consent was obtained from all patients. Serum NRGs detection and clinical data collection Five milliliters of fasting peripheral venous blood was drawn from the patients in the morning, and the expression of NRGs CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 was detected by enzyme-linked immunosorbent assay (Enzyme-linked Immunology Company, Shanghai, China). Serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL) and lipoprotein (a) level were measured by automatic biochemical analyzer. Red blood cell, hemoglobin, white blood cell, neutrophil, lymphocyte, monocyte and platelet levels were measured by automatic hematology analyzer (Sysmex XN-9000). Immunofluorescence analyzer was used to detect the contents of hsTnI, CK-MB and MYO. Automatic BNP detector detects the amount of BNP in blood. Color Doppler echocardiography was perfected using Philips EPIQ 7C color Doppler ultrasound within 24 hours after admission, and left ventricular ejection fraction, left ventricular end-systolic dimension and left ventricular end-diastolic dimension data were collected. Clinical data were collected, mainly including age, gender, history of hypertension, history of diabetes, history of hyperlipidemia, history of smoking and family history. Statistics For quantitative variables, the compliance with normality was first judged according to the normality test and Q-Q plot. Variables that met normality were described as mean ± standard deviation/ ± S, and the difference analysis was performed using the two-independent sample t-test. For variables that did not meet normality, they were described as median and quartiles/M (P25, P75), and the difference analysis was performed using the two-independent sample rank-sum test. Qualitative variables were described by frequency and constituent ratio (%), and differences were analyzed by Chi-square test. Univariate logistic regression analysis was performed for NRGs, and ROC curves including sensitivity, specificity, and optimal cutoff values (cut-off values) were calculated. Multivariate logistic regression analysis and subgroup analysis were performed according to the results of the differential analysis to adjust for confounding factors. Statistical analysis was performed according to SPSS 26. The test level is α = 0.05. Results To observe the expression of NRGs in ACS, the specific flow of data screening strategy and clinical sample validation is detailed in Fig. 1. A total of 650 DEGs were obtained by analyzing the genes in peripheral blood from ACS and control groups by the "limma" software package. Among them, there were 344 highly expressed genes and 306 lowly expressed genes (Fig. 2A), and heat maps were applied to visualize the 650 DEGs mentioned above (Fig. 2B). Outlier sample testing performed prior to performing WGCNA analysis revealed no outliers in the data, indicating that further analysis as described below could be performed (Fig. 3A). Defining the soft threshold of the data as 12 followed by module classification showed that genes were divided into eight modules (grey parts were unclassified genes), and seven of these modules were statistically significantly associated with ACS (P 0.8 within seven modules obtained a total of 1170 hub genes (Fig. 3B-E). Next, DEGs, WGCNA and NRGs were intersected to generate six hub genes, namely CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 (Fig. 4A). PPI analysis of these six hub genes showed potential interactions between them (Fig. 4B). These six hub genes were subjected to enrichment analysis, and GO enrichment analysis showed that they were mainly enriched in biological functions such as neutrophil chemotaxis, neutrophil migration, cytokine activity, and chemokine activity (Fig. 4C). KEGG enrichment analysis showed that NRGs were mainly enriched in NF-κB signaling pathway, IL-17 signaling pathway, and TNF signaling pathway (Fig. 4D-E). To further validate the significance of the six selected NRGs in ACS, 318 participants (228 ACS and 90 controls) were collected as the validation cohort. In the cohort, 184 (57.9%) were males, including 152 (66.7%) in the ACS group and 32 (35.6%) in the control which showed a significant difference in gender distribution between the two groups. The mean age of the study subjects was 63.36 years, with a mean age of 64.44 years in ACS group and 60.61 years in the control, and there was a significant difference in age between the two groups. Compared with controls, ACS patients had a higher proportion of smoking (34.2% vs 13.3%) and family history (18.0% vs 8.9%). There was no statistical difference in the distribution of hypertension and diabetes between the two groups (P > 0.05). Also there were statistically significant differences in the distribution of MONO, HDL, TnI, MYO, and IVS variables between the two groups (Table S1 ). The elisa results of NRGs showed that the distributions of CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 were statistically different between the ACS and control groups, that is, CCL4, CXCL2, IL1β, IL8, and CXCL1 were more highly expressed in the case group, while TNFAIP3 was less (Table 1 ). Table 1 The expression of NETs-associated genes in different groups Variables Total (n = 318) Control group (n = 90) ACS grooup (n = 228) t/Z P value CCL4 274.23 ± 161.84 219.66 ± 97.27 295.76 ± 176.70 3.859 < 0.001 * CXCL2 68.36 (36.61, 111.96) 50.82 (26.73, 85.00) 72.35 (40.92, 122.49) 2.986 0.003 * IL1β 53.82 (26.38, 62.81) 15.69 (11.38, 21.42) 57.37 (51.03, 67.17) 12.063 < 0.001 * IL8 32.03 ± 25.07 23.87 ± 28.12 35.25 ± 23.03 3.719 < 0.001 * CXCL1 1030.37 ± 566.94 572.46 ± 406.64 1211.12 ± 518.69 208.162 < 0.001 * TNFAIP3 706.85 ± 444.09 1286.48 ± 372.06 478.05 ± 188.00 107.416 < 0.001 * Notes: *indicates statistical significance. Univariate logistic regression analysis of NRGs showed that all six continuous variables were statistically significant for ACS (Table 2 ). ROC curves were also plotted using univariate analysis, and Table 3 showed that IL1β, CXCL1, and TNFAIP3 had a better predictive effect for coronary heart disease (AUC > 0.8). According to the cut-off value of ROC curve, NRGs were assigned as binary indicators, the OR value of categorical variables was calculated, and the degree of association between study variables and outcomes was quantified. Model 1 and Model 2 were constructed, respectively. Unadjusted model results showed that high expression of CCL4 (OR = 10.159), CXCL2 (OR = 2.226), IL1β (OR = 333.000), IL8 (OR = 4.896), CXCL1 (OR = 106.615) and low expression of TNFAIP3 (OR = 0.002) were all associated with an increased risk of ACS. And the adjusted model results were consistent with the unadjusted model results (Table 4 ). Table 2 Univariate logistic regression analysis Variables β OR (95%CI) P CCL4 0.009 1.009 (1.005, 1.013) < 0.001 * CXCL2 0.004 1.004 (1.001, 1.007) 0.023 * IL1β 0.140 1.150 (1.117, 1.184) < 0.001 * IL8 0.026 1.026 (1.012, 1.041) < 0.001 * CXCL1 0.005 1.005 (1.004, 1.007) < 0.001 * TNFAIP3 −0.007 0.993 (0.991, 0.994) < 0.001 * Notes: *indicates statistical significance. Table 3 ROC analysis of NETs-associated genes Variables AUC sensitivity specificity Cut-off value CCL4 0.72 (0.65,0.79) 0.895 0.544 191.58 CXCL2 0.61 (0.54,0.68) 0.640 0.556 53.99 IL1β 0.93 (0.89,0.98) 0.974 0.90 34.80 IL8 0.70 (0.64,0.77) 0.689 0.689 22.62 CXCL1 0.91 (0.86,0.96) 0.947 0.856 708.98 TNFAIP3 0.93 (0.89,0.98) 0.889 0.982 846.78 Table 4 Logistic regression Models for NETs-associated genes Variables Model 1 Model 2 OR (95%CI) P OR (95%CI) P CCL4 10.159 (5.617, 18.371) < 0.001 * 10.433 (5.171, 21.050) < 0.001 * CXCL2 2.226 (1.355, 3.655) 0.002 * 2.129 (1.199, 3.779) 0.010 * IL1β 333.000 (114.925, 964.880) < 0.001 * 760.363 (162.660, 3554.356) < 0.001 * IL8 4.896 (2.891, 8.294) < 0.001 * 4.537 (2.480, 8.299) < 0.001 * CXCL1 106.615 (46.647, 243.677) < 0.001 * 222.831 (67.413, 736.554) < 0.001 * TNFAIP3 0.002 (0.001, 0.007) < 0.001 * 0.001 (0.000, 0.005) < 0.001 * Model 1: unadjusted. Model 2: adjusting for gender, age, hypertention, diabetes, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P < 0.1). Notes: *indicates statistical significance. To further analyze the association between NRGs and ACS in different populations, subgroup analyses were performed. According to the results of gender grouping, Model 1 showed that NRGs were statistically significantly associated with ACS in male population, but after adjustment, only CCL4, CXCL2, IL8 and CXCL1 were statistically significantly associated with ACS in Model 2; CCL4, IL1β, IL8, CXCL1 and TNFAIP3 showed statistically significant association with ACS in both Model 1 and Model 2 in female population. If divided into two groups according to age < 65 years and ≥ 65 years for subgroup analysis, the results showed that in Age < 65 population, 6 NEGs were associated with ACS in both models; while in Age ≥ 65 population, only CCL4, IL1β, IL8, CXCL1, TNFAIP3 were associated with ACS in both models (Table 5 ). If subgroup analysis was performed according to the presence or absence of diabetes, the results showed that in the non-diabetic population, all 6 indicators were statistically significant in Model 1, and all except CXCL2 were statistically significant in Model 2; in the diabetic population, all 6 indicators were statistically significant in Model 1, and CCL4, CXCL2, and IL8 were statistically significant in Model 2. Finally, subgroup analysis was performed according to the presence or absence of hypertension, and the results showed that all six NRGs were statistically significant in Model 1 and all except CXCL1 in Model 2 in the non-hypertensive population; CCL4, IL1β, IL8, CXCL1, and TNFAIP3 showed statistically significant associations with ACS in both Model 1 and Model 2 in the hypertensive population (Table 6 ). In summary, six NRGs were independent predictors of ACS in the overall population, but these six indicators showed different predictive effects in different subgroups, of which CCL4 and IL8 showed independent predictors of ACS in all subgroups, and the predictive effects were relatively stable. Table 5 Logistic regression Models for NETs-associated genes in gender and age subgroups Variables Model 1 Model 2 OR (95%CI) P OR (95%CI) P Male CCL4 12.673 (5.210, 30.826) < 0.001 * 17.069 (5.857, 49.742) < 0.001 * CXCL2 3.566 (1.599, 7.953) 0.002 3.870 (1.576, 9.505) 0.003 * IL1β 1057.000 (113.873, 9811.363) < 0.001 * -- 0.993 IL8 5.762 (2.518, 13.184) < 0.001 * 5.880 (2.365, 14.619) < 0.001 * CXCL1 78.000 (24.998, 243.380) < 0.001 * 354.716 (50.859, 2473.979) < 0.001 * TNFAIP3 0.001 (0.000, 0.009) < 0.001 * -- 0.993 Female CCL4 7.578 (3.266, 17.584) < 0.001 * 7.119 (2.607, 19.440) < 0.001 * CXCL2 1.621 (0.811, 3.238) 0.172 1.371 (0.591, 3.185) 0.462 IL1β 150.520 (41.449, 546.610) < 0.001 * 1091.514 (76.726, 15527.943) < 0.001 * IL8 3.602 (1.747, 7.426) 0.001 * 3.594 (1.527, 8.460) 0.003 * CXCL1 131.143 (36.471, 471.567) < 0.001 * 305.404 (44.006, 2119.540) < 0.001 * TNFAIP3 0.005 (0.001, 0.020) < 0.001 * < 0.001 (0.000, 0.011) < 0.001 * Age < 65 CCL4 12.345 (5.505, 27.685) < 0.001 * 12.537 (4.676, 33.612) < 0.001 * CXCL2 2.451 (1.284, 4.678) 0.007 * 2.678 (1.194, 6.007) 0.017 * IL1β 315.000 (75.818, 1308.727) < 0.001 * 441.581 (65.530, 2975.628) < 0.001 * IL8 6.576 (3.260, 13.268) < 0.001 * 6.160 (2.653, 14.300) < 0.001 * CXCL1 196.857 (55.161, 702.542) < 0.001 * 1823.202 (116.124, 28625.220) < 0.001 * TNFAIP3 0.002 (0.001, 0.012) < 0.001 * 0.002 (0.000, 0.014) < 0.001 * Age ≥ 65 CCL4 7.202 (2.871, 18.067) < 0.001 * 9.057 (3.073, 26.700) < 0.001 * CXCL2 1.686 (0.750, 3.792) 0.207 2.094 (0.829, 5.293) 0.118 IL1β 351.000 (67.133, 1835.178) < 0.001 * 6290.521 (93.940, 42123.357) < 0.001 * IL8 3.328 (1.447, 7.655) 0.005 * 2.971 (1.178, 7.493) 0.021 * CXCL1 56.000 (17.791, 176.266) < 0.001 * 201.973 (31.461, 1296.642) < 0.001 * TNFAIP3 0.002 (0.000, 0.012) < 0.001 * < 0.001 (0.000, 0.009) < 0.001 * Model 1:unadjusted. Model 2 for gender subgroup: adjusting for age, hypertention, diabetes, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P < 0.1). Model 2 for age subgroup: adjusting for gender, hypertention, diabetes, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P < 0.1). Notes: *indicates statistical significance. Table 6 Logistic regression Models for NETs-associated genes in DM and HBP subgroups Variables Model 1 Model 2 OR (95%CI) P OR (95%CI) P Non-DM CCL4 11.089 (5.140, 23.922) < 0.001 * 12.210 (4.653, 32.039) < 0.001 * CXCL2 1.915 (1.047, 3.502) 0.035 * 1.707 (0.816, 3.573) 0.156 IL1β 270.750 (76.260, 961.263) < 0.001 * 1738.462 (123.994, 24374.500) < 0.001 * IL8 4.630 (2.456, 8.726) < 0.001 * 4.060 (1.924, 8.564) < 0.001 * CXCL1 77.693 (29.593, 203.975) < 0.001 * 201.417 (41.899, 968.262) < 0.001 * TNFAIP3 0.003 (0.001, 0.012) < 0.001 * 0.001 (0.000, 0.009) < 0.001 * DM CCL4 10.533 (3.889, 28.532) < 0.001 * 25.393 (4.708, 136.958) < 0.001 * CXCL2 3.600 (1.415, 9.159) 0.007 * 9.498 (2.143, 42.098) 0.003 * IL1β 534.000 (71.468, 3989.954) < 0.001 * -- 0.992 IL8 6.452 (2.350, 17.715) < 0.001 * 12.740 (2.889, 56.184) 0.001 * CXCL1 261.000 (45.061, 1511.753) < 0.001 * -- 0.070 TNFAIP3 0.001 (0.000, 0.011) < 0.001 * -- 0.990 Non-HBP CCL4 32.162 (9.952, 103.941) < 0.001 * 115.485 (15.976, 834.822) < 0.001 * CXCL2 3.278 (1.537, 6.992) 0.002 * 5.057 (1.785, 14.323) 0.002 * IL1β 1764.000 (155.473, 20014.331) < 0.001 * 23183.821 (47.938, 112121.900) 0.001 * IL8 5.143 (2.340, 11.303) < 0.001 * 6.384 (2.163, 18.846) 0.001 * CXCL1 1764.000 (155.473, 20014.331) < 0.001 * -- 0.989 TNFAIP3 0.001 (0.000, 0.009) < 0.001 * 0.000 (0.000, 0.014) < 0.001 * HBP CCL4 5.673 (2.671, 11.899) < 0.001 * 5.232 (2.188, 12.508) < 0.001 * CXCL2 1.695 (0.868, 3.308) 0.112 1.665 (0.772, 3.590) 0.194 IL1β 153.771 (46.249, 511.264) < 0.001 * 402.662 (63.760, 2542.916) < 0.001 * IL8 4.820 (2.347, 9.896) < 0.001 * 3.894 (1.745, 8.693) 0.001 * CXCL1 38.182 (15.292, 95.336) < 0.001 * 76.213 (21.256, 273.257) < 0.001 * TNFAIP3 0.004 (0.001, 0.016) < 0.001 * 0.002 (0.000, 0.012) < 0.001 * Model 1: unadjusted. Model 2 for DM subgroup: adjusting for gender, age, hypertention, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P < 0.1). Model 2 for HBP subgroup: adjusting for gender, age, hyperlipidemia, diabetes, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P < 0.1). Notes: *indicates statistical significance. Abbreviations: DM, diabetes mellitus; HBP, high blood pressure. Discussion In this study, DEGs analysis and WGCNA analysis were used to screen important genes involved in regulating ACS, and intersected with NRGs to clarify that CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 were key genes involved in regulating NETs in ACS. In the validation cohort, the distribution of NRGs CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 was statistically different between the ACS and control groups, and their expression changes were associated with an increased risk of ACS. The correlation analysis between NRGs and ACS in different populations showed that 6 NRGs were independent predictors of ACS in the overall population, but they showed different predictive effects in different subgroups, of which CCL4 and IL8 showed independent predictors of ACS in all subgroups, and the predictive effects were relatively stable. In this study, we focused on the expression changes of NETs-related genes in ACS to provide novel targets for the diagnosis and treatment of ACS. IL8, also known as C-X-C motif chemokine ligand 8 (CXCL8), is a proinflammatory chemokine of the CXC family 12 . IL8 is mainly secreted by macrophages and endothelial cells and plays a regulatory role by binding to receptors on monocytes, granulocytes, and endothelial cells 13 . Several studies have shown that single nucleotide polymorphisms (SNPs) in IL8 are associated with susceptibility to ACS 14 . IL8 expression increases during the development of ACS which plays diverse roles as key chemokines 15 . IL8 can chemotactic inflammatory cells to the lesion site, promote the growth and differentiation of inflammatory cells, regulate endothelial cell survival and affect angiogenesis 16 – 17 . Among them, chemotaxis and regulation of neutrophil function are the most important roles of IL8. At the beginning of inflammation, IL8 is able to recruit neutrophils and subsequently mediate NETs formation via the PI3K/AKT/ROS axis 18 . Recently, the role of IL8 in the formation of NETs in atherosclerosis (AS) has also been reported. Serum IL8 and NETs levels have been shown to be higher in AS patients. IL8 interacts with CXCR2, a receptor on midsex granulocytes, and regulates ERK and MAPK signaling pathways through Src and extracellular signals leading to the formation of NETs and aggravating AS progression in vivo 19 . In this study, six NRGs were selected to be involved in regulating NETs formation in CAD by DEGs analysis, WGCNA analysis and combined analysis of NRGs. Among them, IL8 showed an independent predictor of ACS in both the total population and the subgroup population. CC chemokines are a subfamily containing 27 chemotactic cytokines that are important components in mediating cell-to-cell communication 20 . Among them, CCL4 is thought to exacerbate AS by promoting endothelial and macrophage activation. It has been shown that CCL4 is able to induce oxidative stress responses in THP-1 cells via the PI3k/Rac1 pathway in vitro, exacerbating their adhesion to endothelial cells 21 . In addition, CCL4 stimulates macrophage MMP-2 and MMP-9 activity and the production of investigated factors TNF-α and IL-6 22 . In recent years, the effect of CCL4 on neutrophils has also been reported. It has been shown that the supernatant of breast cancer cells significantly increased IL-1β, CCL2-4, iNOS, and MMP-9 expression by neutrophils and formed NETs 23 . CCL4 can also play a role in chemotactic neutrophils and monocytes and other inflammatory cells 24 . In this study, based on the screening of NRGs in ACS, CCL4 expression was observed to be significantly increased in the serum of ACS patients which showed independent predictors of ACS in both the total population and the subgroup population. Inflammation plays a crucial role in the initiation, progression, and outcome of ACS. Several basic and clinical studies have shown that inhibition of inflammation significantly slows the progression of ACS and improves clinical cardiovascular outcomes 25 – 27 . Among them, neutrophils, as one of the core cells that generate inflammatory mediators, have been the focus of much attention. On the one hand, neutrophils themselves and their secretion of a variety of cytokines, can be used as serum markers to predict the severity and prognosis of ACS. High grade granulocyte count has been shown to be associated with AS and is a causal risk factor for ACS 28 . Biomarkers of neutrophil origin, including myeloperoxidase, could activate proteolytic destructive cascades involved in ACS-related immunopathologic events 29 . On the other hand, neutrophils may also be used as intervention targets to improve the prognosis of ACS. In the previous study, it has been shown that PDE4B leads to ischemia-reperfusion injury by promoting neutrophil inflammation, and selective inhibition of PDE4B suppresses the inflammatory response and protects cardiac function in patients with acute myocardial infarction receiving reperfusion therapy 30 . In this study, six key NRGs CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 were demonstrated to be statistically different in distribution between the ACS and control, Their expression changes were associated with an increased risk of ACS. All six NRGs mentioned above were independent predictors of ACS in the overall population and showed different predictive effects in different populations. At the same time, these key NRGs may also become new targets for the treatment of ACS. Declarations Conflict of interest All authors need to disclose no conflicts of interest related to this study. Funding This work was supported by the National Natural Science Foundation of China (grant number 81900310). Author Contribution All authors contributed to the study conception and design.CW was involved in the literature search and manuscript preparation for the entire study; YR was responsible for data collection; MR participated in the data analysis;YW, ZX, HS participated in writing review and editing; LZ was involved in the study design and manuscript review for the entire experiment. All authors have read and approved the final version for publication. Acknowledgement Thanks Professor Lei Gong for writing assistance. Data Availability All the data in the current study could be available from the corresponding author on reasonable request. References Timmis, A. et al. Ian Graham, Marcus Flather, Perry Elliott, Elias A Mossialos, Franz Weidinger, Stephan Achenbach; Atlas Writing Group, European Society of Cardiology. European Society of Cardiology: cardiovascular disease statistics 2021[J]. Eur Heart J . ;43(8):716–799. (2022). Frank Pega, B. et al. Tracey J Woodruff. Global, regional, and national burdens of ischemic heart disease and stroke attributable to exposure to long working hours for 194 countries, 2000–2016: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury[J]. Environ Int . Sep:154:106595. (2021). Andrew, E. et al. Temporal trends in ischemic heart disease mortality in 21 world regions, 1980 to 2010: the Global Burden of Disease 2010 study[J]. Circulation 129 (14), 1483–1492 (2014). Victoria Mutua, Laurel, J. & Gershwin A Review of Neutrophil Extracellular Traps (NETs) in Disease: Potential Anti-NETs Therapeutics[J]. Clin. Rev. Allergy Immunol. 61 (2), 194–211 (2021). Block, H., Rossaint, J. & Alexander Zarbock. The Fatal Circle of NETs and NET-Associated DAMPs Contributing to Organ Dysfunction[J]. Cells 11 (12), 1919 (2022). Shuang Ling, J. W. NETosis as a Pathogenic Factor for Heart Failure[J]. Oxid. Med. Cell. Longev. 23 , 20216687096 (2021 Feb). Grégory Franck, T. L. et al. Peter Libby. Roles of PAD4 and NETosis in Experimental Atherosclerosis and Arterial Injury: Implications for Superficial Erosion[J]. Circ. Res. 123 (1), 33–42 (2018). Gu, C. et al. Guijian Liu. Neutrophil extracellular traps contributing to atherosclerosis: From pathophysiology to clinical implications[J]. Exp Biol Med (Maywood) . ;248(15):1302–1312. (2023). Manovriti Thakur, C. V. C., Junho, S. M., Bernhard, M., Schindewolf, H. & Noels Yvonne Döring. NETs-Induced Thrombosis Impacts on Cardiovascular and Chronic Kidney Disease[J]. Circ. Res. 132 (8), 933–949 (2023). Fabrizio Semeraro, Concetta, T. et al. Charles T Esmon. Extracellular histones promote thrombin generation through platelet-dependent mechanisms: involvement of platelet TLR2 and TLR4[J]. Blood 118 (7), 1952–1961 (2011). Wu, J. et al. Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-related genes[J]. Front Immunol 2022 Dec. 1 :131047367 . IL-6 and IL-8. An Overview of Their Roles in Healthy and Pathological Pregnancies. IL-6 and IL-8: An Overview of Their Roles in Healthy and Pathological Pregnancies[J]. Int. J. Mol. Sci. 23 (23), 14574 (2022). Kristen Fousek, L. A., Horn, C. & Palena Interleukin-8: A chemokine at the intersection of cancer plasticity, angiogenesis, and immune suppression[J]. Pharmacol. Ther. Mar , 219:107692 (2021). Ying Wu, W. et al. Ru-Xing Wang. Strong association between the interleukin-8-251A/T polymorphism and coronary artery disease risk[J]. Med. (Baltim). 98 (10), e14715 (2019). Wilanee Dechkhajorn, Y., Maneerat, K., Prasongsukarn, P., Kanchanaphum, R. & Kumsiri Interleukin-8 in Hyperlipidemia and Coronary Heart Disease in Thai Patients Taking Statin Cholesterol-Lowering Medication While Undergoing Coronary Artery Bypass Grafting Treatment[J]. Scientifica (Cairo) . Jun 17:2020:5843958. (2020). Corre, I., Pineau, D. & Hermouet, S. Interleukin-8: an autocrine/paracrine growth factor for human hematopoietic progenitors acting in synergy with colony stimulating factor-1 to promote monocyte-macrophage growth and differentiation[J]. Exp. Hematol. 27 (1), 28–36 (1999). Li, A. et al. IL-8 directly enhanced endothelial cell survival, proliferation, and matrix metalloproteinases production and regulated angiogenesis[J]. J. Immunol. 170 (6), 3369–3376 (2003). Caijun Zha, X. et al. Neutrophil extracellular traps mediate the crosstalk between glioma progression and the tumor microenvironment via the HMGB1/RAGE/IL-8 axis[J]. Cancer Biol. Med. 17 (1), 154–168 (2020). Zhujun An, J. et al. Liang. Neutrophil extracellular traps induced by IL-8 aggravate atherosclerosis via activation NF-κB signaling in macrophages[J]. Cell Cycle . ;18(21):2928–2938. (2019). Korbecki, J. et al. CC Chemokines in a Tumor: A Review of Pro-Cancer and Anti-Cancer Properties of the Ligands of Receptors CCR1, CCR2, CCR3, and CCR4[J]. Int. J. Mol. Sci. 21 (21), 8412 (2020). Yuji Tatara, M. et al. Toshio Ogihara, Hiromi Rakugi. Macrophage inflammatory protein-1beta induced cell adhesion with increased intracellular reactive oxygen species[J]. J. Mol. Cell. Cardiol. 47 (1), 104–111 (2009). Chang, T. T., Yang, H. Y., Chen, C. & Chen, J. W. CCL4 Inhibition in Atherosclerosis: Effects on Plaque Stability, Endothelial Cell Adhesiveness, and Macrophages Activation[J]. Int. J. Mol. Sci. 21 (18), 6567 (2020). Lingyun Wu, S. et al. Breast Cancer Cell-Neutrophil Interactions Enhance Neutrophil Survival and Pro-Tumorigenic Activities[J]. Cancers (Basel) . 12 (10), 2884 (2020). Sato, E., Simpson, K. L., Grisham, M. B., Koyama, S. & Robbins, R. A. Inhibition of MIP-1alpha-induced human neutrophil and monocyte chemotactic activity by reactive oxygen and nitrogen metabolites[J]. J. Lab. Clin. Med. 135 (2), 161–169 (2000). Kyle Nelson, V., Fuster, P. M. & Ridker Low-Dose Colchicine for Secondary Prevention of Coronary Artery Disease: JACC Review Topic of the Week[J]. J. Am. Coll. Cardiol. 82 (7), 648–660 (2023). Yang Zheng, Y. et al. Wu. Mettl14 mediates the inflammatory response of macrophages in atherosclerosis through the NF-κB/IL-6 signaling pathway[J]. Cell Mol Life Sci . ;79(6):311. (2022). Paul, M. et al. Michael Davidson; RESCUE Investigators. IL-6 inhibition with ziltivekimab in patients at high atherosclerotic risk (RESCUE): a double-blind, randomised, placebo-controlled, phase 2 trial[J]. Lancet 397 (10289), 2060–2069 (2021). Luo, J., Thomassen, J. Q. & Nordestgaard, B. G. Anne Tybjærg-Hansen, Ruth Frikke-Schmidt. Neutrophil counts and cardiovascular disease[J]. Eur. Heart J. 44 (47), 4953–4964 (2023). Hatem Alfakry, E., Malle, C. N., Koyani, P. J., Pussinen, T. & Sorsa Neutrophil proteolytic activation cascades: a possible mechanistic link between chronic periodontitis and coronary heart disease[J]. Innate Immun. 22 (1), 85–99 (2016). Wan#, Q. & Xu, C. # , Liyuan Zhu, Yuze Zhang, Zekun Peng, Hong Chen, Haojie Rao, Erli Zhang, Hongyue Wang, Fei Chu, Xuan Ning, Xuejian Yang, Jinqing Yuan, Yongjian Wu, Yu Huang, Shengshou Hu, De-Pei Liu, Miao Wang. Targeting PDE4B (Phosphodiesterase-4 Subtype B) for Cardioprotection in Acute Myocardial Infarction via Neutrophils and Microcirculation[J]. Circ Res . ;131(5):442–455. (2022). Additional Declarations No competing interests reported. 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Zhong","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhong","suffix":""},{"id":617949616,"identity":"6c8d2416-5539-432a-a13d-62fd3f104bee","order_by":6,"name":"Chunxiao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCRBhAOV8MLCRI0kLY+OMgjRjIrVAAGMzz4fDiQR18M/uMXvMU2AnZ3CAx/yxjQFzAgP74aMb8Fpy54y54QyDZGOgFsPmHAO2PAaetLQb+LQYSOSYSXwwYE7cBtHCU8wgwWNGWEuCQT1Ei4WBRGIDUVo+GByGaGEwMCCsReJGWpnkDIPjxvYH2Apn9hgkGLMR8gv/jORt0jx/quUkG5g3fPjx578cP/vhY3i1IID8C0iEshGnHAzYH5CgeBSMglEwCkYSAADjqUN9KqaurAAAAABJRU5ErkJggg==","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chunxiao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-01-12 12:54:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8582008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8582008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106534616,"identity":"ef009e88-4b6d-4433-9b50-9b7d7a50fd9f","added_by":"auto","created_at":"2026-04-09 15:05:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":284088,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8582008/v1/d25016bac3cd0dccf591cab8.png"},{"id":106726574,"identity":"ebf7d2c5-92d7-4847-9950-c3abeb1d1060","added_by":"auto","created_at":"2026-04-12 18:36:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5766347,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not 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version\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8582008/v1/3b273604ea4b08a36b1c4b8b.png"},{"id":106727831,"identity":"0cd8b642-172e-4530-9927-4291f411cbf4","added_by":"auto","created_at":"2026-04-12 18:41:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15615033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8582008/v1/836e2807-00ba-4077-8c34-eeab7b322ee8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of serum cytokines predicted the severity of coronary artery through neutrophil extracellular trap-related genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute Coronary Syndrome (ACS) is currently the most common cause of death worldwide which incidence has risen from 27.3% to 31.4% in recent decades and continues to rise\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although great progress has been made in the prevention and treatment of ACS over the past few years, it is still important to identify the factors influencing ACS to improve its prognosis because of the high mortality and morbidity causing a heavy burden on human health\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNeutrophil extracellular trapping nets (NETs) are extensive extracellular meshwork produced and released by activated neutrophils and are mainly composed of modified decondensed chromatin and granule proteins, including neutrophil elastase, myeloperoxidase, and histone\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. NETs formation is mainly triggered by extracellular microbial and endogenous stimuli, such as pathogen-associated molecular patterns or damage-associated molecular patterns\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. NETs were initially found to capture and kill extracellular pathogens and play a protective role in defense against pathogen infection. Recently, NETs have been demonstrated to play multiple roles in coronary atherosclerosis promoting plaque rupture and exacerbating intracoronary thrombus formation\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, there is no comprehensive exploration of NETs-related genes (NRGs) in ACS.\u003c/p\u003e \u003cp\u003eThe aim of this study was to analyze the relationship between NRGs and ACS. First, the key genes in ACS were selected using differential gene analysis and WGCNA analysis using microarray data from the GEO database, and intersected with NRGs reported in the previous literature to identify the key NRGs associated with ACS. Subsequently, serum from control and ACS patients were clinically collected, and Elisa was used to detecte the NRGs and verified the correlation with ACS. The aim of this study was to explore the expression changes of NRGs in ACS and look forward to providing new targets for the treatment in the future.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSources bioinformatics analysis data\u003c/h2\u003e \u003cp\u003eThe datasets GSE113079 in this study were derived from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), in which 93 patients were included in the ACS group and 48 patients in the control group. R 4.3.2 was applied to complete data download and preprocessing. Genes involved in NETs were identified based on previous literature\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndentification of NRGs in ACS\u003c/h3\u003e\n\u003cp\u003eThe \"limma\" package was applied to obtain the differential expressed genes (DEGs) in patients with ACS and controls. DEGs were considered when adj.P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and | logFC | \u0026gt; 1. And according to the \u0026ldquo;ggplot2\u0026rdquo;, \u0026ldquo;ggVolcano\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; package to draw volcano plot and heat map, respectively, to complete the visualization of DEGs. Weighted Gene Co-Expression Network Analysis (WGCNA) was also performed according to the \u0026ldquo;WGCNA\u0026rdquo; package. The genes were divided into different modules. The correlation between the modules and ACS is calculated, respectively, and the genes with MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8 in the modules strongly associated with ACS were defined as the hub genes. DEGs, hub genes and NETs genes were intersected to obtain NRGs involved in ACS.\u003c/p\u003e\n\u003ch3\u003ePPI network construction and enrichment of functional pathways\u003c/h3\u003e\n\u003cp\u003eAccording to the STRING website (Fig. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), protein \u0026ndash; protein interaction (PPI) network analysis was performed and the results were visualized according to the \"cytohubba\" plugin in cytoscape software.\u003c/p\u003e \u003cp\u003eIn order to understand the potential biological functions and pathways involved in the NRGs, \u0026ldquo;clusterProfiler\u0026rdquo;, \u0026ldquo;org.Hs.eg.db\u0026rdquo;, \u0026ldquo;enrichplot\u0026rdquo; and \u0026ldquo;GOplot\u0026rdquo; packages were used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eIn this retrospective case\u0026ndash;control study we enrolled 228 consecutive patients with first-admission acute coronary syndrome who had been admitted to Yantai Yuhuangding Hospital between October 2023 and March 2024 and whose leftover serum samples were stored in the hospital\u0026rsquo;s clinical biobank. All cases were confirmed by coronary angiography showing\u0026thinsp;\u0026ge;\u0026thinsp;30% stenosis of the left main trunk or \u0026ge;\u0026thinsp;50% stenosis in one or more major coronary branches. Meanwhile, 90 patients who underwent coronary angiography or contrast-enhanced CT of the coronary arteries to rule out ACS were selected as controls. Exclusion criteria were: (1) congenital heart disease, cardiomyopathy, valvular disease, arrhythmia, heart failure and other heart diseases; (2) previous treatment in other hospital due to ACS and received treatment; (3) acute myocardial infarction or cerebral infarction in the past 6 months; (4) acute or chronic inflammatory diseases; (5) elevated CRP; (6) hematological diseases; (7) rheumatic system diseases, such as systemic lupus erythematosus, gout, etc.; (8) taking immunosuppressive agents, glucocorticoids; (8) severe liver and kidney dysfunction; (9) suffering from malignant tumors. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Ethics Committee of Yantai YuhuangdingHospital (No 2024\u0026thinsp;\u0026minus;\u0026thinsp;233). Informed consent was obtained from all patients.\u003c/p\u003e \u003cp\u003eSerum NRGs detection and clinical data collection\u003c/p\u003e \u003cp\u003eFive milliliters of fasting peripheral venous blood was drawn from the patients in the morning, and the expression of NRGs CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 was detected by enzyme-linked immunosorbent assay (Enzyme-linked Immunology Company, Shanghai, China). Serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL) and lipoprotein (a) level were measured by automatic biochemical analyzer. Red blood cell, hemoglobin, white blood cell, neutrophil, lymphocyte, monocyte and platelet levels were measured by automatic hematology analyzer (Sysmex XN-9000). Immunofluorescence analyzer was used to detect the contents of hsTnI, CK-MB and MYO. Automatic BNP detector detects the amount of BNP in blood. Color Doppler echocardiography was perfected using Philips EPIQ 7C color Doppler ultrasound within 24 hours after admission, and left ventricular ejection fraction, left ventricular end-systolic dimension and left ventricular end-diastolic dimension data were collected. Clinical data were collected, mainly including age, gender, history of hypertension, history of diabetes, history of hyperlipidemia, history of smoking and family history.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eFor quantitative variables, the compliance with normality was first judged according to the normality test and Q-Q plot. Variables that met normality were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation/\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e\u0026plusmn; S, and the difference analysis was performed using the two-independent sample t-test. For variables that did not meet normality, they were described as median and quartiles/M (P25, P75), and the difference analysis was performed using the two-independent sample rank-sum test. Qualitative variables were described by frequency and constituent ratio (%), and differences were analyzed by Chi-square test. Univariate logistic regression analysis was performed for NRGs, and ROC curves including sensitivity, specificity, and optimal cutoff values (cut-off values) were calculated. Multivariate logistic regression analysis and subgroup analysis were performed according to the results of the differential analysis to adjust for confounding factors. Statistical analysis was performed according to SPSS 26. The test level is α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo observe the expression of NRGs in ACS, the specific flow of data screening strategy and clinical sample validation is detailed in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e A total of 650 DEGs were obtained by analyzing the genes in peripheral blood from ACS and control groups by the \"limma\" software package. Among them, there were 344 highly expressed genes and 306 lowly expressed genes (Fig.\u0026nbsp;2A), and heat maps were applied to visualize the 650 DEGs mentioned above (Fig.\u0026nbsp;2B). Outlier sample testing performed prior to performing WGCNA analysis revealed no outliers in the data, indicating that further analysis as described below could be performed (Fig.\u0026nbsp;3A). Defining the soft threshold of the data as 12 followed by module classification showed that genes were divided into eight modules (grey parts were unclassified genes), and seven of these modules were statistically significantly associated with ACS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Genetic screening based on MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8 within seven modules obtained a total of 1170 hub genes (Fig.\u0026nbsp;3B-E). Next, DEGs, WGCNA and NRGs were intersected to generate six hub genes, namely CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 (Fig.\u0026nbsp;4A). PPI analysis of these six hub genes showed potential interactions between them (Fig.\u0026nbsp;4B). These six hub genes were subjected to enrichment analysis, and GO enrichment analysis showed that they were mainly enriched in biological functions such as neutrophil chemotaxis, neutrophil migration, cytokine activity, and chemokine activity (Fig.\u0026nbsp;4C). KEGG enrichment analysis showed that NRGs were mainly enriched in NF-κB signaling pathway, IL-17 signaling pathway, and TNF signaling pathway (Fig.\u0026nbsp;4D-E).\u003c/p\u003e \u003cp\u003eTo further validate the significance of the six selected NRGs in ACS, 318 participants (228 ACS and 90 controls) were collected as the validation cohort. In the cohort, 184 (57.9%) were males, including 152 (66.7%) in the ACS group and 32 (35.6%) in the control which showed a significant difference in gender distribution between the two groups. The mean age of the study subjects was 63.36 years, with a mean age of 64.44 years in ACS group and 60.61 years in the control, and there was a significant difference in age between the two groups. Compared with controls, ACS patients had a higher proportion of smoking (34.2% vs 13.3%) and family history (18.0% vs 8.9%). There was no statistical difference in the distribution of hypertension and diabetes between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Also there were statistically significant differences in the distribution of MONO, HDL, TnI, MYO, and IVS variables between the two groups (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The elisa results of NRGs showed that the distributions of CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 were statistically different between the ACS and control groups, that is, CCL4, CXCL2, IL1β, IL8, and CXCL1 were more highly expressed in the case group, while TNFAIP3 was less (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 expression of NETs-associated genes in different groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;318)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eACS grooup\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;228)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et/Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274.23\u0026thinsp;\u0026plusmn;\u0026thinsp;161.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219.66\u0026thinsp;\u0026plusmn;\u0026thinsp;97.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295.76\u0026thinsp;\u0026plusmn;\u0026thinsp;176.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.36\u003c/p\u003e \u003cp\u003e(36.61, 111.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.82\u003c/p\u003e \u003cp\u003e(26.73, 85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.35\u003c/p\u003e \u003cp\u003e(40.92, 122.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.82\u003c/p\u003e \u003cp\u003e(26.38, 62.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.69\u003c/p\u003e \u003cp\u003e(11.38, 21.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.37\u003c/p\u003e \u003cp\u003e(51.03, 67.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.03\u0026thinsp;\u0026plusmn;\u0026thinsp;25.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;28.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.25\u0026thinsp;\u0026plusmn;\u0026thinsp;23.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1030.37\u0026thinsp;\u0026plusmn;\u0026thinsp;566.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572.46\u0026thinsp;\u0026plusmn;\u0026thinsp;406.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1211.12\u0026thinsp;\u0026plusmn;\u0026thinsp;518.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e208.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e706.85\u0026thinsp;\u0026plusmn;\u0026thinsp;444.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1286.48\u0026thinsp;\u0026plusmn;\u0026thinsp;372.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478.05\u0026thinsp;\u0026plusmn;\u0026thinsp;188.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: *indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate logistic regression analysis of NRGs showed that all six continuous variables were statistically significant for ACS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ROC curves were also plotted using univariate analysis, and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed that IL1β, CXCL1, and TNFAIP3 had a better predictive effect for coronary heart disease (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8). According to the cut-off value of ROC curve, NRGs were assigned as binary indicators, the OR value of categorical variables was calculated, and the degree of association between study variables and outcomes was quantified. Model 1 and Model 2 were constructed, respectively. Unadjusted model results showed that high expression of CCL4 (OR\u0026thinsp;=\u0026thinsp;10.159), CXCL2 (OR\u0026thinsp;=\u0026thinsp;2.226), IL1β (OR\u0026thinsp;=\u0026thinsp;333.000), IL8 (OR\u0026thinsp;=\u0026thinsp;4.896), CXCL1 (OR\u0026thinsp;=\u0026thinsp;106.615) and low expression of TNFAIP3 (OR\u0026thinsp;=\u0026thinsp;0.002) were all associated with an increased risk of ACS. And the adjusted model results were consistent with the unadjusted model results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eUnivariate logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.009 (1.005, 1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004 (1.001, 1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.150 (1.117, 1.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.026 (1.012, 1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.005 (1.004, 1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.993 (0.991, 0.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: *indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC analysis of NETs-associated genes\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003especificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.65,0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e191.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.54,0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.89,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.64,0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.86,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e708.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.89,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e846.78\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression Models for NETs-associated genes\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.159 (5.617, 18.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.433 (5.171, 21.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.226 (1.355, 3.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.129 (1.199, 3.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e333.000 (114.925, 964.880)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e760.363 (162.660, 3554.356)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.896 (2.891, 8.294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.537 (2.480, 8.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106.615 (46.647, 243.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e222.831 (67.413, 736.554)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002 (0.001, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001 (0.000, 0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1: unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: adjusting for gender, age, hypertention, diabetes, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: *indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further analyze the association between NRGs and ACS in different populations, subgroup analyses were performed. According to the results of gender grouping, Model 1 showed that NRGs were statistically significantly associated with ACS in male population, but after adjustment, only CCL4, CXCL2, IL8 and CXCL1 were statistically significantly associated with ACS in Model 2; CCL4, IL1β, IL8, CXCL1 and TNFAIP3 showed statistically significant association with ACS in both Model 1 and Model 2 in female population. If divided into two groups according to age\u0026thinsp;\u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years for subgroup analysis, the results showed that in Age\u0026thinsp;\u0026lt;\u0026thinsp;65 population, 6 NEGs were associated with ACS in both models; while in Age\u0026thinsp;\u0026ge;\u0026thinsp;65 population, only CCL4, IL1β, IL8, CXCL1, TNFAIP3 were associated with ACS in both models (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). If subgroup analysis was performed according to the presence or absence of diabetes, the results showed that in the non-diabetic population, all 6 indicators were statistically significant in Model 1, and all except CXCL2 were statistically significant in Model 2; in the diabetic population, all 6 indicators were statistically significant in Model 1, and CCL4, CXCL2, and IL8 were statistically significant in Model 2. Finally, subgroup analysis was performed according to the presence or absence of hypertension, and the results showed that all six NRGs were statistically significant in Model 1 and all except CXCL1 in Model 2 in the non-hypertensive population; CCL4, IL1β, IL8, CXCL1, and TNFAIP3 showed statistically significant associations with ACS in both Model 1 and Model 2 in the hypertensive population (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In summary, six NRGs were independent predictors of ACS in the overall population, but these six indicators showed different predictive effects in different subgroups, of which CCL4 and IL8 showed independent predictors of ACS in all subgroups, and the predictive effects were relatively stable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression Models for NETs-associated genes in gender and age subgroups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.673 (5.210, 30.826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.069 (5.857, 49.742)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.566 (1.599, 7.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.870 (1.576, 9.505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1057.000 (113.873, 9811.363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.762 (2.518, 13.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.880 (2.365, 14.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.000 (24.998, 243.380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e354.716 (50.859, 2473.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001 (0.000, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.578 (3.266, 17.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.119 (2.607, 19.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.621 (0.811, 3.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.371 (0.591, 3.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150.520 (41.449, 546.610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1091.514 (76.726, 15527.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.602 (1.747, 7.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.594 (1.527, 8.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131.143 (36.471, 471.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305.404 (44.006, 2119.540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005 (0.001, 0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 (0.000, 0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.345 (5.505, 27.685)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.537 (4.676, 33.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.451 (1.284, 4.678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.678 (1.194, 6.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e315.000 (75.818, 1308.727)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e441.581 (65.530, 2975.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.576 (3.260, 13.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.160 (2.653, 14.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196.857 (55.161, 702.542)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1823.202 (116.124, 28625.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002 (0.001, 0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002 (0.000, 0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.202 (2.871, 18.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.057 (3.073, 26.700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.686 (0.750, 3.792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.094 (0.829, 5.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e351.000 (67.133, 1835.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6290.521 (93.940, 42123.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.328 (1.447, 7.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.971 (1.178, 7.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.000 (17.791, 176.266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201.973 (31.461, 1296.642)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002 (0.000, 0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 (0.000, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1:unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 for gender subgroup: adjusting for age, hypertention, diabetes, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 for age subgroup: adjusting for gender, hypertention, diabetes, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: *indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression Models for NETs-associated genes in DM and HBP subgroups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.089 (5.140, 23.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.210 (4.653, 32.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.915 (1.047, 3.502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.707 (0.816, 3.573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e270.750 (76.260, 961.263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1738.462 (123.994, 24374.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.630 (2.456, 8.726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.060 (1.924, 8.564)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.693 (29.593, 203.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201.417 (41.899, 968.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003 (0.001, 0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001 (0.000, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.533 (3.889, 28.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.393 (4.708, 136.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.600 (1.415, 9.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.498 (2.143, 42.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e534.000 (71.468, 3989.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.452 (2.350, 17.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.740 (2.889, 56.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261.000 (45.061, 1511.753)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001 (0.000, 0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.162 (9.952, 103.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.485 (15.976, 834.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.278 (1.537, 6.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.057 (1.785, 14.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1764.000 (155.473, 20014.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23183.821 (47.938, 112121.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.143 (2.340, 11.303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.384 (2.163, 18.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1764.000 (155.473, 20014.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001 (0.000, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000, 0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.673 (2.671, 11.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.232 (2.188, 12.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.695 (0.868, 3.308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.665 (0.772, 3.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153.771 (46.249, 511.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402.662 (63.760, 2542.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.820 (2.347, 9.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.894 (1.745, 8.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.182 (15.292, 95.336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.213 (21.256, 273.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFAIP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004 (0.001, 0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002 (0.000, 0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1: unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 for DM subgroup: adjusting for gender, age, hypertention, hyperlipidemia, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 for HBP subgroup: adjusting for gender, age, hyperlipidemia, diabetes, smoking, family history, HGB, MONO, HDL-C, hsTnI, MYO, BNP, IVS (index corrected for P\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: *indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: DM, diabetes mellitus; HBP, high blood pressure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, DEGs analysis and WGCNA analysis were used to screen important genes involved in regulating ACS, and intersected with NRGs to clarify that CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 were key genes involved in regulating NETs in ACS. In the validation cohort, the distribution of NRGs CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 was statistically different between the ACS and control groups, and their expression changes were associated with an increased risk of ACS. The correlation analysis between NRGs and ACS in different populations showed that 6 NRGs were independent predictors of ACS in the overall population, but they showed different predictive effects in different subgroups, of which CCL4 and IL8 showed independent predictors of ACS in all subgroups, and the predictive effects were relatively stable. In this study, we focused on the expression changes of NETs-related genes in ACS to provide novel targets for the diagnosis and treatment of ACS.\u003c/p\u003e \u003cp\u003eIL8, also known as C-X-C motif chemokine ligand 8 (CXCL8), is a proinflammatory chemokine of the CXC family\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. IL8 is mainly secreted by macrophages and endothelial cells and plays a regulatory role by binding to receptors on monocytes, granulocytes, and endothelial cells\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Several studies have shown that single nucleotide polymorphisms (SNPs) in IL8 are associated with susceptibility to ACS\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. IL8 expression increases during the development of ACS which plays diverse roles as key chemokines\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. IL8 can chemotactic inflammatory cells to the lesion site, promote the growth and differentiation of inflammatory cells, regulate endothelial cell survival and affect angiogenesis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Among them, chemotaxis and regulation of neutrophil function are the most important roles of IL8. At the beginning of inflammation, IL8 is able to recruit neutrophils and subsequently mediate NETs formation via the PI3K/AKT/ROS axis\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Recently, the role of IL8 in the formation of NETs in atherosclerosis (AS) has also been reported. Serum IL8 and NETs levels have been shown to be higher in AS patients. IL8 interacts with CXCR2, a receptor on midsex granulocytes, and regulates ERK and MAPK signaling pathways through Src and extracellular signals leading to the formation of NETs and aggravating AS progression in vivo\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In this study, six NRGs were selected to be involved in regulating NETs formation in CAD by DEGs analysis, WGCNA analysis and combined analysis of NRGs. Among them, IL8 showed an independent predictor of ACS in both the total population and the subgroup population.\u003c/p\u003e \u003cp\u003eCC chemokines are a subfamily containing 27 chemotactic cytokines that are important components in mediating cell-to-cell communication\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Among them, CCL4 is thought to exacerbate AS by promoting endothelial and macrophage activation. It has been shown that CCL4 is able to induce oxidative stress responses in THP-1 cells via the PI3k/Rac1 pathway in vitro, exacerbating their adhesion to endothelial cells\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition, CCL4 stimulates macrophage MMP-2 and MMP-9 activity and the production of investigated factors TNF-α and IL-6\u003csup\u003e22\u003c/sup\u003e. In recent years, the effect of CCL4 on neutrophils has also been reported. It has been shown that the supernatant of breast cancer cells significantly increased IL-1β, CCL2-4, iNOS, and MMP-9 expression by neutrophils and formed NETs\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. CCL4 can also play a role in chemotactic neutrophils and monocytes and other inflammatory cells\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In this study, based on the screening of NRGs in ACS, CCL4 expression was observed to be significantly increased in the serum of ACS patients which showed independent predictors of ACS in both the total population and the subgroup population.\u003c/p\u003e \u003cp\u003eInflammation plays a crucial role in the initiation, progression, and outcome of ACS. Several basic and clinical studies have shown that inhibition of inflammation significantly slows the progression of ACS and improves clinical cardiovascular outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Among them, neutrophils, as one of the core cells that generate inflammatory mediators, have been the focus of much attention. On the one hand, neutrophils themselves and their secretion of a variety of cytokines, can be used as serum markers to predict the severity and prognosis of ACS. High grade granulocyte count has been shown to be associated with AS and is a causal risk factor for ACS\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Biomarkers of neutrophil origin, including myeloperoxidase, could activate proteolytic destructive cascades involved in ACS-related immunopathologic events\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. On the other hand, neutrophils may also be used as intervention targets to improve the prognosis of ACS. In the previous study, it has been shown that PDE4B leads to ischemia-reperfusion injury by promoting neutrophil inflammation, and selective inhibition of PDE4B suppresses the inflammatory response and protects cardiac function in patients with acute myocardial infarction receiving reperfusion therapy\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In this study, six key NRGs CCL4, CXCL2, IL1β, IL8, CXCL1, and TNFAIP3 were demonstrated to be statistically different in distribution between the ACS and control, Their expression changes were associated with an increased risk of ACS. All six NRGs mentioned above were independent predictors of ACS in the overall population and showed different predictive effects in different populations. At the same time, these key NRGs may also become new targets for the treatment of ACS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eAll authors need to disclose no conflicts of interest related to this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant number 81900310).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design.CW was involved in the literature search and manuscript preparation for the entire study; YR was responsible for data collection; MR participated in the data analysis;YW, ZX, HS participated in writing review and editing; LZ was involved in the study design and manuscript review for the entire experiment. All authors have read and approved the final version for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThanks Professor Lei Gong for writing assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data in the current study could be available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTimmis, A. et al. Ian Graham, Marcus Flather, Perry Elliott, Elias A Mossialos, Franz Weidinger, Stephan Achenbach; Atlas Writing Group, European Society of Cardiology. European Society of Cardiology: cardiovascular disease statistics 2021[J]. \u003cem\u003eEur Heart J\u003c/em\u003e. ;43(8):716\u0026ndash;799. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrank Pega, B. et al. Tracey J Woodruff. 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(Baltim).\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e (10), e14715 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilanee Dechkhajorn, Y., Maneerat, K., Prasongsukarn, P., Kanchanaphum, R. \u0026amp; Kumsiri Interleukin-8 in Hyperlipidemia and Coronary Heart Disease in Thai Patients Taking Statin Cholesterol-Lowering Medication While Undergoing Coronary Artery Bypass Grafting Treatment[J]. \u003cem\u003eScientifica (Cairo)\u003c/em\u003e. Jun 17:2020:5843958. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorre, I., Pineau, D. \u0026amp; Hermouet, S. Interleukin-8: an autocrine/paracrine growth factor for human hematopoietic progenitors acting in synergy with colony stimulating factor-1 to promote monocyte-macrophage growth and differentiation[J]. \u003cem\u003eExp. Hematol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (1), 28\u0026ndash;36 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, A. et al. 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(2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"coronary heart disease, neutrophil extracellular trapping nets, interleukin 8, CCL4","lastPublishedDoi":"10.21203/rs.3.rs-8582008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8582008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute Coronary Syndrome (ACS) threatens human health worldwide. Early noninvasive assessment of the severity of ACS is helpful for its screening, treatment and management. Neutrophil extracellular trapping nets (NETs) are networks produced by neutrophils which released after stimulation to capture and eliminate microorganisms. NETs have recently been found to have an important role in ACS. The aim of this study was to investigate NETs-associated genes (NRGs) during ACS and to identify their association with ACS severity in different populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Differential gene analysis and WGCNA analysis were performed using the data set in GEO database, and the genes obtained from the two analyses were intersected with NRGs to the key genes involved in regulating ACS. The resulting genes were subjected to protein-protein interaction network analysis and functional enrichment analysis. ACS and control patients were selected as the validation cohort, Elisa was used to detect the expression of key genes, univariate logistic regression analysis was performed, ROC curve was plotted, sensitivity, specificity, and optimal cut-off value (cut-off value) were calculated. Multivariate logistic regression analysis and subgroup analysis were performed according to the results of the difference analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, CCL4, CXCL2, IL1β, IL8, CXCL1 and TNFAIP3 were selected as key NRGs in ACS by intersecting DEGs, WGCNA and NRGs. A total of 318 clinical samples (228 ACS and 90 controls) were collected as the validation cohort, and Elisa results showed that CCL4, CXCL2, IL1β, IL8, and CXCL1 was higher in ACS group, while TNFAIP3 expression was lower. Univariate logistic regression analysis showed that all six continuous variables were statistically significant for ACS. ROC curves showed that high expression of CCL4, CXCL2, IL1β, IL8, CXCL1 and low expression of TNFAIP3 were all associated with an increased risk of ACS. And IL1β, CXCL1, and TNFAIP3 were better predictive of ACS (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8). Multivariate logistics analysis of the overall and subgroup populations showed that these six NRGs were independent predictors of ACS in the overall population, but these six indicators showed different predictive effects in different subgroup populations. CCL4 and IL8 showed independent predictors of ACS in all subgroups, and the predictive effects were relatively stable.\u003c/p\u003e\u003ch2\u003eConclunsion\u003c/h2\u003e \u003cp\u003eThe key variables selected by NRGs can predict the severity of ACS, which provide some reference for the screening and treatment of ACS.\u003c/p\u003e","manuscriptTitle":"Identification of serum cytokines predicted the severity of coronary artery through neutrophil extracellular trap-related genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 15:05:19","doi":"10.21203/rs.3.rs-8582008/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T03:36:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T10:25:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T01:58:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167255383771114179465186746226922379165","date":"2026-04-03T23:01:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T17:41:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114065905963386688476926881341186921023","date":"2026-04-03T17:35:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22409467055598953434432289400873478457","date":"2026-04-02T14:52:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T14:37:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T07:54:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T11:42:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T11:40:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-12T12:46:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27b6df8c-1a8f-455a-b57f-588c6206ce61","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":65758551,"name":"Health sciences/Biomarkers"},{"id":65758552,"name":"Health sciences/Cardiology"},{"id":65758553,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65758554,"name":"Health sciences/Diseases"},{"id":65758555,"name":"Biological sciences/Genetics"},{"id":65758556,"name":"Biological sciences/Immunology"},{"id":65758557,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-20T03:38:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 15:05:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8582008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8582008","identity":"rs-8582008","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}