Abstract
Background
Cardiovascular diseases, particularly atherosclerosis, remain a leading cause of global mortality, presenting significant challenges with non-ST-segment elevation myocardial infarction (NSTEMI). Novel biomarkers such as cluster of differentiation 47 (CD47) and adenylate cyclase-associated protein 1(CAP1) have emerged as potential candidates for improving early diagnosis and risk stratification in NSTEMI patients.
Methods
This prospective cohort study was conducted at Tianjin Chest Hospital from November 2023 to June 2024, involving a total of 270 patients categorized into NSTEMI and unstable angina (UA) groups. We used multivariable logistic regression analysis to elucidate the relationship between CD47, CAP1, and the onset of NSTEMI. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of CD47 and CAP1 as biomarkers for the early diagnosis of NSTEMI in the population.Subsequently, Cox regression models were utilized to conduct short-term (median follow-up of 146 days) assessments of patients, evaluating the role of CD47 and CAP1 in early risk stratification.
Results
In our study, CD47 and CAP1 exhibited strong correlations with NSTEMI patients. Furthermore, in ROC analysis, CAP1 (AUC = 0.827, 95% CI: 0.778–0.875, P<0.001) and CD47 (AUC = 0.807, 95% CI: 0.756–0.859, P<0.001) demonstrated robust diagnostic value. Cox regression analysis identified CD47 (HR, 1.059; 95% CI 1.010–1.110; P = 0.018) and CAP1(HR, 5.385; 95% CI 1.769–16.388; P = 0.003) as independent predictors of short-term major adverse cardiovascular events (MACE) in NSTEMI patients. After adjusting some variables, high CD47 group (HR: 4.017, 95%CI 1.320−12.224, P = 0.014) and high CAP1 group (HR: 3.893, 95% CI 1.366–11.090, P = 0.011) the risk of developing MACE was significantly increased in the lower group.
Conclusion
CD47 and CAP1 demonstrated robust diagnostic value for early NSTEMI and great predictive power for short-term MACE in NSTEMI patients.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-025-04939-7.
Keywords: CD47, CAP1, Diagnosis, NSTEMI, MACE
Introduction
Cardiovascular disease is a leading global cause of mortality, ranking prominently among the causes of death worldwide [1]. Central to these ailments is atherosclerosis, which continues to drive high mortality rates in cardiovascular disease patients [2]. The differentiation between non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) often relies more on laboratory markers and clinical presentation than on initial ECG findings, which can lead to diagnostic discrepancies [3]. A large-scale study across 11 countries found that NSTEMI constitutes 25% of acute coronary syndrome (ACS) cases [4]. In comparison to delayed invasive strategies, early invasive approaches have been shown to be safe and are associated with a reduced risk of refractory ischemia and shorter hospital stays [5]. The disparity in mortality and recurrent cardiac events between early and delayed intervention highlights the critical importance of timely risk stratification for NSTEMI patients. The benefits of proactive therapeutic strategies are proportional to their associated risks [6]. Therefore, ongoing research into diverse management strategies remains crucial to improving outcomes for these patients [7].
Cluster of differentiation 47 (CD47), also known as integrin-associated protein or Rh-related antigen, is a surface protein that is widely expressed on all vascular cells. It primarily mediates its effects through interactions with its ligands, thrombospondin-1 (TSP-1) and signal regulatory protein alpha (SIRPα) [8]. In a 2016 Nature study, Kojima et al. elucidated the role of CD47 in atherosclerosis and proposed that targeting CD47 with antibodies could offer a novel therapeutic approach for coronary artery disease by enhancing phagocytosis [9].
Adenylate cyclase-associated protein 1(CAP1) is a highly conserved actin-binding protein expressed across a range of human cell types. It is implicated in the regulation of inflammatory factors, with evidence suggesting that its expression markedly accelerates the progression of atherosclerosis. Consequently, CAP1 is emerging as a significant target for therapeutic interventions in inflammatory cardiac metabolic diseases, including obesity and atherosclerosis [10].
Recent years have seen the emergence of numerous biomarkers linked to the formation of atherosclerotic plaques. Among these, CD47 and CAP1 have been identified for further investigation. Our literature review revealed a notable paucity of cardiovascular-related clinical studies focusing on these markers. The diagnosis of NSTEMI hinges on the identification of chest pain, dynamic electrocardiographic changes, and a progressive increase in high-sensitivity cardiac troponin. Distinguishing NSTEMI from UA and other non-coronary ischemic conditions, which lack biochemical evidence of myocardial necrosis, is critical. CD47 exacerbates atherosclerotic plaque inflammation and instability by inhibiting macrophage-mediated phagocytosis, while CAP1 amplifies endothelial damage through its regulation of lipid metabolism and inflammatory pathways. Together, these molecules exert a synergistic effect in NSTEMI pathogenesis: CD47-mediated inflammatory and oxidative stress responses, coupled with CAP1-driven monocyte infiltration and dysregulated LDL metabolism, facilitate plaque rupture and myocardial injury.
We propose that combined detection of CD47 and CAP1 not only enhances the sensitivity of early NSTEMI diagnosis but also refines risk stratification by evaluating inflammatory burden and plaque vulnerability, providing novel targets for precision-based therapeutic strategies.
Methods
Study population
This was a single-center prospective cohort study. From November 2023 to February 2024, a total of 270 patients were recruited from Tianjin Chest Hospital (Tianjin, China), comprising 158 individuals in the NSTEMI group and 112 individuals in the UA group. All recruited participants belonged to the Han ethnic group. Inclusion criteria: According to the 2023 ESC Guidelines for the Management of Acute Coronary Syndromes [3], patients with NSTEMI were enrolled based on the following criteria: (1) typical ischemic chest pain lasting ≥ 10 min within 24 h of admission; (2) electrocardiographic (ECG) evidence of transient or persistent ST-segment depression (≥ 0.5 mm) or T-wave inversion in ≥ 2 contiguous leads, without persistent ST-segment elevation; (3) serial elevation of high-sensitivity cardiac troponin (hs-cTn) above the 99th percentile upper reference limit (URL), demonstrating a dynamic rise/fall pattern (≥ 20% change between baseline and 3-hour repeat measurements); (4) confirmation of coronary artery disease (CAD) via coronary angiography (CAG) or CT angiography (CTA), showing ≥ 50% stenosis or plaque rupture. Competing diagnoses (e.g., myocarditis, pulmonary embolism) were excluded through echocardiography or advanced imaging. In contrast, unstable angina (UA) was defined by: (1) rest or worsening angina (Canadian Cardiovascular Society class III-IV); (2) transient ECG changes (e.g., ST-segment depression or T-wave inversion) without persistent abnormalities; (3) hs-cTn levels persistently below the 99th percentile URL or minimal fluctuations (< 20% change); (4) non-obstructive CAD (< 50% stenosis) on imaging. Key distinctions included the absence of myocardial necrosis in UA (vs. biomarker-confirmed necrosis in NSTEMI) and differences in revascularization urgency (early intervention for NSTEMI vs. medical optimization for UA) [3, 5]. All diagnoses adhered to ESC criteria to ensure cohort homogeneity and reduce confounding bias. Exclusion criteria: Patients with concomitant cardiac valve disease, rheumatic heart disease, dilated cardiomyopathy, hypertrophic cardiomyopathy; presence of coronary myocardial bridging; concomitant malignant arrhythmias, organ failure, or severe lesions (such as cerebral, pulmonary, hepatic, and renal); allergic diseases, tumors, autoimmune diseases, connective tissue diseases, blood disorders, injuries, pulmonary tuberculosis, etc.; pregnant women or individuals with mental disorders such as depression who could not cooperate with the study. Informed consent was obtained from all subjects, and approval was granted by the Ethics Committee of Tianjin Chest Hospital(Ethics Approval:2024YS-013-01) in accordance with the Helsinki Declaration.The specific process of this study can be seen Fig. 1.
Fig. 1.
Patients flowchart. NSTEMI Non-ST-segment elevation myocardial infarction, UA Unstable angina
Sample and data collection
Within 2 h of admission, each patient provided 2 ml of peripheral blood, which was placed in standard tubes containing EDTA anticoagulant. After gentle mixing for 10–20 min, samples were centrifuged at 2000–3000 rpm for 20 min at 2–8 °C. The supernatant was carefully collected and transferred to RNase-free tubes, then immediately frozen at −80 °C to prevent repeated freeze-thaw cycles. After collecting all samples, CD47 and CAP1 levels were quantified using enzyme-linked immunosorbent assay (ELISA) kits purchased from Quanzhou Jiubang Biotechnology Co., Ltd. All other baseline clinical and laboratory data were collected by trained researchers from electronic medical records, blinded to the study objectives. Clinical data included age, gender, history of diabetes, smoking, hypertension, prior myocardial infarction (MI), ejection fraction (EF), left atrial volume (LA), and left ventricular end-diastolic diameter (LVEDD). Laboratory tests included total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), creatinine, glomerular filtration rate (GFR), high-sensitivity C-reactive protein (hs-CRP), B-type natriuretic peptide (BNP), and high-sensitivity troponin (hs-Tn).All readmitted individuals were followed up via telephone or outpatient visits, with a median follow-up time of 146 days. The primary endpoint was defined as the composite of major adverse cardiovascular events (MACE) and short-term readmission events. MACE included all-cause mortality, MI, stroke, and unplanned coronary revascularization. Readmission encompassed all subsequent hospitalizations of the patients.
Statistical analysis
The normality of measurements was assessed using the Kolmogorov-Smirnov test. Normally distributed data are presented as mean ± standard deviation, with intergroup differences assessed using Student’s t-test. Non-normally distributed data are presented as median M (P25, P75), and intergroup differences were compared using the Mann-Whitney U test. Categorical variables were expressed as frequencies and percentages and were analyzed using the chi-square test or Fisher’s exact test. Multivariable logistic regression analysis was employed to identify risk factors based on the relationships between variables. Receiver operating characteristic (ROC) curve analysis was used to evaluate CAP1 and CD47 as biomarkers for the diagnosis of NSTEMI patients. Using Cox regression models, we assessed the correlation between CD47 and CAP1 levels and short-term clinical outcomes. We employed univariate Cox proportional hazards regression models to analyze independent predictors of cardiovascular composite endpoints. Univariate Cox regression analysis identified predictors associated with MACE. Event-free survival rates between two groups were evaluated using the Kaplan-Meier method and log-rank test. The same approach was applied to evaluate short-term patient readmission.
All study data were analyzed using SPSS (version 25.0, IBM, Chicago), R (version 4.2.0, R) and GraphPad Prism 9 (GraphPad Software Inc.) statistical software. All statistical tests were two-tailed, with a P-value < 0.05 considered statistically significant.
Results
Baseline characteristics
The study was divided into NSTEMI and normal groups, with 158 patients in the NSTEMI group and 112 participants in the UA group (Table 1). The NSTEMI (n = 158) and control (n = 112) groups showed comparable gender (73.4% vs. 66.1% male, P = 0.224), age (62 ± 12 vs. 65 ± 11 years, P = 0.086), and diabetes prevalence (32.9% vs. 34.8%, P = 0.794), indicating balanced demographics. Comparative analysis between the two groups revealed statistically significant differences (P < 0.05) in Previous MI, EF, TC, hs-CRP, BNP, CK-MB, hs-cTn, CD47, and CAP1. Compared to the UA group, the NSTEMI group exhibited a lower proportion of patients with a history of MI, lower EF, and higher levels of total cholesterol, hs-CRP, BNP, CK-MB, hs-cTn, CD47, and CAP1.
Table 1.
Baseline characteristics of the study population
| Overall population (n = 270) |
UA (n = 112) |
NSTEMI (n = 158) |
P | |
|---|---|---|---|---|
| Age, years | 65 ± 11 | 65 ± 11 | 62 ± 12 | 0.086 |
| Male | 190(70.4%) | 74(66.1%) | 116(73.4%) | 0.224 |
| Diabetes | 91(33.7%) | 39(34.8%) | 52(32.9%) | 0.794 |
| Hypertension | 174(64.4%) | 72(64.3%) | 102(64.6%) | 0.963 |
| Smoker | 90(33.3%) | 31(27.7%) | 59(37.3%) | 0.116 |
| Previous MI | 55(20.4%) | 31(27.7%) | 24(15.2%) | 0.012 |
| LA(mm) | 37(34–39) | 36(34–40) | 37(34–39) | 0.5 |
| LVEDD(mm) | 50 ± 5.9 | 50 ± 6.11 | 51(47–59) | 0.796 |
| EF% | 57(50–61) | 60(54–62) | 56(47–59) | <0.01 |
| Heart Rate | 73(65–83) | 73 ± 5 | 75(65–85) | 0.489 |
| SBP, mmHg | 131(123–140) | 130(124–140) | 132(121–142) | 0.652 |
| DBP, mmHg | 75 ± 12 | 72 ± 5 | 74(70–84) | 0.501 |
| TC, mmol/L | 4.24(3.56–5.14) | 4.01 ± 1.21 | 4.42(3.85–5.28) | <0.01 |
| TG, mmol/L | 1.54(1.17–2.13) | 1.49(1.08–1.96) | 1.67(1.20–2.21) | 0.51 |
| HDL-C, mmol/L | 0.96(0.83–1.15) | 1.02 ± 0.28 | 0.96(0.84–1.11) | 0.87 |
| LDL-C, mmol/L | 2.61 ± 1 | 2.47 ± 0.94 | 2.99 ± 1.13 | 0.519 |
| hs-CRP, mg/L | 2.93(0.96–8.71) | 1.48(0.62−3,82) | 4.32(1.69–13.10) | <0.01 |
| BNP, pg/ml | 73(21–279) | 35(12–138) | 116 (41–307) | <0.01 |
| Creatinine, mg/dl | 78 ± 22 | 79.34 ± 19.63 | 82.60 ± 39.22 | 0.517 |
| GEF(ml/min/1.73m2) | 95 ± 37 | 94 ± 34 | 96 ± 39 | 0.988 |
| CK-MB, U/L | 17(12–30) | 13(11–18) | 24(16–52) | <0.01 |
| hs-cTn, ng/ml | 0.199(0.026–0.673) | 0.021(0.009–0.773) | 0.45(0.203–1.457) | <0.01 |
| CD47, pg/g | 36.07(30.56–44.33) | 31.85 ± 6.42 | 42.36 ± 9.01 | <0.01 |
| CAP1, ng/g | 1.99(1.68–2.36) | 1.76 ± 0.36 | 2.26 ± 0.46 | 0.02 |
Data are expressed as mean ± SD, medians with interquartile ranges or percentage
NSTEMI Non-ST-segment elevation myocardial infarction, UA Unstable angina, MI Myocardial infarction, LA Left atrium volume, LVEDD Left ventricular end diastolic dimension, EF Ejection fraction, TC Total cholesterol, TG Triglycerides, HDL-C High-density lipoprotein cholesterol, LDL-C Low-density lipoprotein cholesterol, hs-CRP High‐sensitivity C‐reactive protein, BNP B-type natriuretic peptide, CK-MB Creatine kinase isoenzymes, CD47 Cluster of differentiation 47, CAP1 Adenylate cyclase-associated protein 1, SBP Systolic blood pressure, DBP Diastolic blood pressure
Serum CD47 and CAP levels in NSTEMI and UA group
Serum analysis revealed significantly elevated levels of both biomarkers in NSTEMI patients compared to controls. CD47 concentrations were markedly higher in the NSTEMI group (42.36 ± 9.01 pg/g) versus UA subjects (31.85 ± 6.42 pg/g; P < 0.001). Similarly, CAP1 levels showed a pronounced increase in NSTEMI patients (2.265 ± 0.46 ng/g) relative to the UA group (1.766 ± 0.36 ng/g; P < 0.001). These differential expression patterns are visually presented in Fig. 2, demonstrating robust discriminatory capacity between disease states (Fig. 2).
Fig. 2.
Peripheral Blood Levels of CD47 (A) and CAP (B) 1 in Patients with NSTEMI and UA people (P < 0.001). CD47 cluster of differentiation 47, CAP1 adenylate cyclase-associated protein 1, NSTEMI Non-ST-segment elevation myocardial infarction, UA Unstable angina
Association of CD47 and CAP1 with NSTEMI: a multivariable analysis
Logistic regression analyses identified several independent biomarkers associated with NSTEMI susceptibility (Table 2). The analysis revealed strong positive associations for circulating CD47 levels (OR = 1.19, 95% CI 1.14–1.22; P < 0.001) and CAP1 concentration (OR = 18.95, 95% CI 8.57–41.92; P < 0.001), along with established cardiac markers including CK-MB (OR = 1.09, 95% CI 1.05–1.12; P < 0.001) and hs-CRP (OR = 1.11, 95% CI 1.06–1.16; P < 0.001). Traditional cardiovascular risk factors - hypercholesterolemia (OR = 1.41, 95% CI 1.13–1.76; P = 0.003) and prior MI (OR = 2.14, 95% CI 1.17–3.89; P = 0.013) - remained significant predictors. Notably, left ventricular ejection fraction demonstrated a protective association (OR = 0.97, 95% CI 0.94–1.00; P = 0.04). In the fully adjusted model accounting for clinical covariates, CD47 (OR = 1.12, 95% CI 1.05–1.20; P = 0.001) and CAP1 (OR = 5.66, 95% CI 1.42–17.47; P = 0.009) maintained robust associations with NSTEMI diagnosis, along with CK-MB (OR = 1.06, 95% CI 1.02–1.10; P = 0.003) and hs-CRP (OR = 1.09, 95% CI 1.00–1.04; P = 0.038). Total cholesterol retained marginal significance (OR = 1.43, 95% CI 1.01–2.02; P = 0.042).
Table 2.
Univariable and multivariate regression analysis for the association of investigated parameters
| Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|
| OR | 95%CI | P | OR | 95%CI | P | |
| CD47, pg/g | 1.189 | (1.137–1.224) | <0.01 | 1.123 | (1.049–1.202) | 0.001 |
| CAP1, ng/g | 18.952 | (8.569–41.917) | <0.01 | 5.663 | (1.420−17.474) | 0.009 |
| CK-MB, U/L | 1.087 | (1.053–1.122) | <0.01 | 1.059 | (1.019−1.100) | 0.003 |
| hs-CRP, mg/L | 1.109 | (1.057–1.163) | <0.01 | 1.085 | (1.001–1.042) | 0.038 |
| TC, mmol/L | 1.408 | (1.126–1.761) | 0.03 | 1.431 | (1.013–2.022) | 0.042 |
| EF% | 0.970 | (0.942–0.999) | 0.040 | 0.535 | (0.951–1.040) | 0.946 |
| Previous MI | 2.137 | (1.173–3.894) | 0.013 | 0.994 | (0.198–1.441) | 0.422 |
CD47 Cluster of differentiation 47, CAP1 Adenylate cyclase-associated protein 1, TC Total cholesterol, CK-MB Creatine kinase isoenzymes, hs-CRP High‐sensitivity C‐reactive protein, TC Total cholesterol, EF Ejection fraction, MI Myocardial infarction
Diagnostic performance of CD47 and CAP1 for NSTEMI identification
The ROC curve analysis (Fig. 3) depicted in Table 3 revealed that CD47 predicts NSTEMI with an area under the curve (AUC) of 0.807 (95% CI: 0.756–0.859, P < 0.01), sensitivity of 0.639, specificity of 0.902, and optimal cutoff value of 38.95 pg/g. CAP1 achieved an AUC of 0.827 (95% CI: 0.778–0.875, P < 0.01), sensitivity of 0.747, specificity of 0.732, and optimal cutoff value of 1.92 ng/g. When combined, CD47 and CAP1 yielded an AUC of 0.860 (95% CI: 0.815–0.905, P < 0.01) (Table 3). These findings underscore the high sensitivity, specificity, and diagnostic capacity of CD47 and CAP1 in identifying NSTEMI patients. Moreover, their combined use demonstrates superior diagnostic ability compared to either biomarker alone. Both biomarkers hold significant diagnostic value for NSTEMI patients individually, with their combined application enhancing diagnostic efficacy.
Fig. 3.
ROC Curve Analysis of CD47 and CAP1 Index. Optimal cut-off: CD47: 38.95 pg/g; CAP1: 0.86. AUC CD47 0.807(0.756–0.859), AUC CAP1: 0.827 (0.778–0.875); AUC combine: 0.860 (0.815 − 0.723). ROC receiver operating characteristic, AUC an area under the curve, CD47 cluster of differentiation 47, CAP1 adenylate cyclase-associated protein 1
Table 3.
ROC curve analysis of CD47 and CAP1 index
| AUC | 95CI% | Cut-off | Sensitivity | Specificity | P | |
|---|---|---|---|---|---|---|
| CD47, pg/g | 0.807 | (0.756−0859) | 38.95 | 0.639 | 0.902 | <0.001 |
| CAP1, ng/g | 0.827 | (0.778–0.875) | 1.92 | 0.747 | 0.732 | <0.001 |
| CAP1 + CD47 | 0.860 | (0.815–0.905) | 0.842 | 0.750 | <0.001 |
AUC Area under the curve, CD47 Cluster of differentiation 47, CAP1 Adenylate cyclase-associated protein 1
Associations of CD47 and CAP1 levels and MACE
During the median follow-up period of 146 days, all cases were successfully followed up due to the short-term nature of the study. Among the cohort, 26 cases (16.5%) experienced MACE, with 4 cases (0.03%) resulting in cardiovascular-related mortality. Kaplan-Meier survival analysis revealed a markedly elevated risk of MACE among patients with higher serum CD47 levels compared to those with lower CD47 levels in NSTEMI cohorts(log rank = 0.004) (Fig. 4). A similar trend was observed within the CAP1 subgroup(log rank = 0.012) (Fig. 4). Cox proportional hazards regression analysis in Table 4 revealed significant associations between levels of CD47 (HR, 1.061; 95% CI, 1.017–1.107; P = 0.006) and CAP1 (HR, 3.750; 95% CI, 1.572–8.944; P = 0.003) and short-term prediction of MACE. After adjusting for age, sex, diabetes, HBP, TC, TG, HDL and LDL during short-term follow-up, multivariate Cox proportional hazards regression analysis revealed that CD47 (HR, 1.059; 95% CI 1.010–1.110; P = 0.018) and CAP1(HR, 5.385; 95% CI 1.769–16.388; P = 0.003) were independent predictor of MACE. Subsequently, patients were stratified into two groups based on the median CD47 level (CD47 Low group: <41.98 pg/g, n = 79; CD47 High group: ≥41.98 pg/g, n = 79). The incidence of MACE was 3.8% in the CD47 Low group and 12.7% in the CD47 High group (P = 0.007) (Table 5). Similarly, patients were stratified into CAP1 Low group (<2.22 ng/g, n = 79) and CAP1 High group (≥ 2.22 ng/g, n = 79)(P = 0.016) (Table 5). After adjusting variables, high CD47 group (HR: 4.017, 95%CI 1.320–12.224, P = 0.014) and high CAP1 group (HR: 3.893, 95% CI 1.366–11.090, P = 0.011) the risk of becoming MACE was significantly higher in the lower group.
Fig. 4.
The event-free survival rate in CD47 and CAP1. MACE major adverse cardiac event,CD47 cluster of differentiation 47,CAP1 adenylate cyclase-associated protein 1
Table 4.
The Cox regression analysis for MACE
| Univariate | Multivariate | |||||
|---|---|---|---|---|---|---|
| Variables | HR | 95%CI | P | HR | 95%CI | P |
| CD47, pg/g | 1.061 | (1.017–1.107) | 0.006 | 1.059 | (1.010–1.110) | 0.018 |
| CAP1, ng/g | 3.750 | (1.572–8.944) | 0.003 | 5.385 | (1.769–16.388) | 0.003 |
CD47 Cluster of differentiation 47, CAP1 Adenylate cyclase-associated protein 1
Table 5.
Associations of CD47 and CAP1 levels with MACE
| Variable | Event, n/Total | Unadjusted model HR(95%CI) |
P | Adjusted model HR(95%CI) |
P |
|---|---|---|---|---|---|
| CD47 | |||||
| Low | 6/158(3.8%) | Reference | Reference | ||
| High | 20/158(12.7%) | 3.517(1.411–8.767) | 0.007 | 3.893(1.366–11.090) | 0.011 |
| CAP1 | |||||
| Low | 7/158(4.4%) | Reference | Reference | ||
| High | 19/158(12.0%) | 2.903(1.219–6.909) | 0.016 | 4.017(1.320−12.224) | 0.014 |
Adjusted for age, sex, diabetes, HBP, TC, TG, HDL, LDL
CD47 Cluster of differentiation 47, CAP1 Adenylate cyclase-associated protein 1
Discussion
In this study, we explored the potential clinical utility of CD47 and CAP1 levels among NSTEMI patients. Our preliminary findings are as follows: (1) CD47 (OR = 1.123) and CAP1 (OR = 5.663, P = 0.009) were positively correlated with NSTEMI patients. (2) Both biomarkers can differentiate between NSTEMI patients (AUC = 0.807, 95% CI: 0.756–0.859, P < 0.01) and UA people(AUC = 0.827, 95% CI: 0.778–0.875, P < 0.01), particularly in combination(AUC = 0.860, 95% CI: 0.815–0.905, P < 0.01), demonstrating early clinical diagnosis. (3) CD47 (HR: 4.017, 95%CI 1.320–12.224, P = 0.014) and CAP1(HR: 3.893, 95% CI 1.366–11.090, P = 0.011) levels were predictive of early MACE events, aiding in early risk stratification for NSTEMI patients.
These results highlight the CD47 and CAP1 for early clinical application in NSTEMI patients. Effective early detection of acute myocardial infarction (AMI), combined with precise risk assessment, is essential for timely patient management, appropriate treatment selection, mitigation of myocardial damage, and improved outcomes [6, 7]. This is especially critical for NSTEMI patients, who often lack definitive electrocardiographic diagnostic tools. The diagnosis of NSTEMI necessitates a comprehensive approach encompassing clinical presentation, dynamic electrocardiographic changes, such as transient ST-segment depression or T-wave inversion and progressive elevations in myocardial injury biomarkers, notably high-sensitivity cardiac troponin. Crucially, NSTEMI must be differentiated from UA, where biochemical markers of myocardial necrosis are absent. According to the 2023 ESC guidelines [3], sustained elevation of high-sensitivity cardiac troponin is a hallmark of NSTEMI, whereas UA lacks such evidence of myocardial damage. It is also imperative to exclude non-coronary ischemic conditions, including severe anemia, aortic dissection, and myocarditis, as well as structural pathologies like valvular heart disease and myocardial bridging, to ensure the homogeneity of the study cohort.
In recent decades, there has been an increase in the discovery of novel biomarkers that can augment existing markers for the early diagnosis and risk assessment of MI. These emerging biomarkers enhance diagnostic accuracy through multimarker approaches and offer additional insights for early risk stratification [11].Guided by these principles, we selected CD47 and CAP1 for further investigation. Although extensive research supports their theoretical mechanisms in atherosclerotic plaque formation and their potential as therapeutic targets, clinical trials directly linking these biomarkers with patient outcomes remain sparse. This study aims to provide preliminary insights and a foundation for future research on the clinical relevance of these biomarkers.
In clinical practice, peripheral blood samples are the most expedient means for diagnosing AMI. These samples are the most practical and widely utilized specimens for clinical assessment. Prompt analysis of laboratory results upon emergency admission allows for early evaluation of myocardial injury severity, thereby facilitating more precise diagnosis and risk stratification. Our study indicates that both CD47 and CAP1 biomarkers demonstrate high specificity and sensitivity in NSTEMI patients. Consequently, we have embarked on a preliminary investigation into the utility of these novel biomarkers for early diagnosis and risk stratification in NSTEMI.
CD47 primarily functions by transmitting an anti-phagocytic “don’t eat me” signal through the CD47-SIRPα (signal regulatory protein alpha) signaling axis. CD47 on normal cells interacts with SIRPα on macrophages, preventing phagocytosis by inducing tyrosine phosphorylation of the SIRPα cytoplasmic domain [12]. Disruption of this interaction can inhibit immune evasion by tumor cells, thereby impeding tumor growth [13–15]. Similarly, targeting SIRPα in atherosclerosis and inhibiting CD47 may represent a viable strategy for treating coronary artery disease [16]. CD47 has also been identified as a key mediator of statin action, with statins reducing atherosclerosis by lowering CD47 expression [17]. Another mechanism involves TNF-α-dependent signaling, which upregulates CD47. CD47 overexpression is driven by superenhancers, facilitating immune evasion [18]. While TNF antagonists such as infliximab, adalimumab, and etanercept have shown limited clinical benefits in preventing coronary artery disease in psoriatic arthritis [19], combined use of anti-CD47 antibodies with etanercept or infliximab has demonstrated enhanced effects on atherosclerosis and cell proliferation. However, this approach has not yet been incorporated into clinical guidelines for treating atherosclerosis or ischemic heart disease [20]. Loss of CD47 leads to increased lymphocyte activation and exacerbates atherosclerosis [21]. Additionally, CD47 regulates nitric oxide (NO) production and release, which serves as an endothelium-derived vasodilator. NO stimulates endothelial cell proliferation, increases vascular permeability, recruits vascular smooth muscle cells (VSMCs), and modulates their contractile function, contributing to blood perfusion, vascular remodeling, and maturation [22]. Elevated CD47 levels can, however, contribute to atherosclerosis and stroke [23]. CD47 also influences endothelial cell and VSMC aging and cell cycle arrest, impairing arterial dilation and reducing blood flow [24]. Furthermore, CD47 can be targeted through adenovirus-mediated RNA interference (RNAi) for myocardial injection, leading to CD47 downregulation. This intervention has been shown to reduce myocardial ischemia/reperfusion injury (MIRI), helping to mitigate reperfusion-induced myocardial damage [25]. These mechanistic insights have been corroborated by our clinical findings.
CAP1, akin to CD47, plays a crucial role in oncology and serves as a novel biomarker for both lung cancer treatment and prognosis prediction [26]. In cardiovascular diseases, CAP1 primarily interacts with resistin, which activates adenylate cyclase and increases cyclic adenosine monophosphate (cAMP) production. This, in turn, stimulates protein kinase A and NF-κB pathways, leading to elevated production of pro-inflammatory cytokines such as IL-6, TNF-α, and IL-1β. Research has shown that resistin significantly influences cells with upregulated CAP1 mRNA, contributing to the regulation of monocyte pro-inflammatory activity and promoting atherosclerosis [15, 27]. CAP1 also facilitates the endocytosis of low-density lipoprotein receptors (LDL-R) and their lysosomal degradation through its interaction with proprotein convertase subtilisin/kexin type 9 (PCSK9) [28]. Elevated levels of CAP1 have been linked to increased inflammation and accelerated atherosclerosis progression [29, 30]. In the context of MI diagnosis, studies have reported that serum CAP1 protein levels rise in conjunction with cardiac troponin I (cTnI) and oxidized LDL (ox-LDL) levels. Serum CAP1 protein thus holds promise as a novel biomarker for diagnosing first-time AMI patients, supporting our research findings [31].
Overall, In the context of NSTEMI, CD47 and CAP1 exhibit a synergistic role in disease progression. CD47 activation of the NF-κB pathway triggers the release of pro-inflammatory cytokines, including IL-6 and TNF-α, while CAP1 further amplifies these signals through its interaction with resistin, establishing a positive feedback loop that exacerbates endothelial dysfunction. Moreover, CD47-mediated inhibition of phagocytosis impairs the clearance of apoptotic cells, while CAP1, via its modulation of PCSK9-dependent LDL receptor degradation, elevates circulating LDL levels. These mechanisms collectively promote lipid core expansion and fibrous cap thinning, thereby increasing the likelihood of plaque rupture. In ischemia-reperfusion injury, CD47 upregulation exacerbates oxidative stress and cellular apoptosis, while CAP1-driven monocyte infiltration broadens myocardial damage. Clinically, combined assessment of CD47 and CAP1 provides a more accurate reflection of the inflammatory burden and plaque vulnerability in NSTEMI patients. These findings suggest that the interplay between CD47 and CAP1 across multiple pathways is pivotal to NSTEMI progression, and their integrated evaluation may offer novel avenues for early diagnosis and risk stratification.
As we reviewed the literature, we identified substantial potential in these two biomarkers. Nevertheless, existing research predominantly focuses on experimental mechanisms rather than specific clinical investigations. Our current study substantiates their clinical significance, aiming to support future research in this area.
Limitation
(1) As a single-center study, the results may be influenced by selection bias inherent in the cohort from this tertiary medical institution, limiting the generalizability to broader populations. (2) The relatively short median follow-up period of 146 days restricts the ability to assess the long-term prognostic value of CD47/CAP1 levels. (3) While the sample size provides sufficient statistical power for the primary endpoint, subgroup analyses may lack the sensitivity to detect smaller effect sizes, and therefore, corresponding subgroup analyses are absent. (4) Despite standardized protocols, the ELISA-based detection method may be subject to batch-to-batch variability when compared to mass spectrometry techniques. (5) The expression patterns of CD47 and CAP1 in other cardiovascular diseases remain poorly characterized. (6) Coronary angiography and subsequent percutaneous coronary intervention (PCI) data obtained after patient admission were not included in the present analysis. We plan to address this limitation in future studies. (7) The study is limited by regional and ethnic constraints. suggesting a need for future multi-center validation studies, extended follow-up periods, and mechanistic investigations in experimental models.
Conclusion
CD47 and CAP1 demonstrated robust diagnostic value for early NSTEMI and great predictive power for short-term MACE in NSTEMI patients.Their roles in atherosclerosis warrant further investigation to explore their broader clinical utility, offering more precise methods for patient diagnosis, assessment, and treatment.
Supplementary Information
Acknowledgements
The authors would like to thank Tianjin Chest Hospital for their help and support.
Abbreviations
- ACS
Acute coronary syndromes
- AMI
Acute myocardial infarction
- AUC
Area under the curve
- cAMP
Cyclic adenosine monophosphate
- CAP1
Adenylatecyclase-associated protein
- CD47
Cluster of differentiation 47
- Cox
Proportional hazards model
- CI
Confidence Interval
- HR
Hazard Ratio
- MACE
Major adverse cardiovascular events
- NSTEMI
Non-ST-segment elevation myocardial infarction
- ROC
Receiver Operating Characteristic
- STEMI
ST-segment elevation myocardial infarction
- SIRPα
Signal regulatory protein alpha
- TSP-1
Thrombospondin-1
- UA
Unstable angina
- VSMC
Vascular Smooth Muscle Cell
Authors’ contributions
Mingyang Li，Dongxia Jin ang Yuecheng Hu contributed to the study designation.Mingyang Li contributed to manuscript writing, data analysis and editing. Mingyang Li contributed to data analysis. Dongxia Jin ，Yuecheng Hu，Cun Xie and Xiaodong Cui conducted a critical revision of the manuscript. All authors approved the final manuscript.
Funding
This study was supported by a grant from Tianjin Municipal Health Bureau (CN) (NO.MS20016),and Tianjin Key Medical Discipline(Specialty) Construction Project(NO.TJYXZDXK-055B).
Data availability
The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.
Declarations
Ethics approval and consent to participate
This work has been carried out in accordance with the Declaration of Helsinki (2000) of the World Medical Association.Informed consent was obtained from all subjects, and approval was granted by the Ethics Committee of Tianjin Chest Hospital(Ethics Approval:2024YS-013-01).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Mingyang Li and Dongxia Jin are co-first authors.
References
- 1.Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The global burden of cardiovascular diseases and risk: A compass for future health. J Am Coll Cardiol. 2022;80(25):2361–71. [DOI] [PubMed] [Google Scholar]
- 2.Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006;367(9524):1747–57. [DOI] [PubMed] [Google Scholar]
- 3.Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, Claeys MJ, Dan GA, Dweck MR, Galbraith M, Gilard M, Hinterbuchner L, Jankowska EA, Jüni P, Kimura T, Kunadian V, Leosdottir M, Lorusso R, Pedretti RFE, Rigopoulos AG, Rubini Gimenez M, Thiele H, Vranckx P, Wassmann S, Wenger NK, Ibanez B, ESC Scientific Document Group. 2023 ESC guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720–826.37622654 [Google Scholar]
- 4.Steg PG, Goldberg RJ, Gore JM, Fox KA, Eagle KA, Flather MD, Sadiq I, Kasper R, Rushton-Mellor SK, Anderson FA. GRACE investigators. Baseline characteristics, management practices, and in-hospital outcomes of patients hospitalized with acute coronary syndromes in the global registry of acute coronary events (GRACE). Am J Cardiol. 2002;90(4):358–63. [DOI] [PubMed] [Google Scholar]
- 5.Roffi M, Patrono C, Collet JP, Mueller C, Valgimigli M, Andreotti F, Bax JJ, Borger MA, Brotons C, Chew DP, Gencer B, Hasenfuss G, Kjeldsen K, Lancellotti P, Landmesser U, Mehilli J, Mukherjee D, Storey RF, Windecker S, ESC Scientific Document Group. 2015 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task force for the management of acute coronary syndromes in patients presenting without persistent ST-Segment elevation of the European society of cardiology (ESC). Eur Heart J. 2016;37(3):267–315.26320110 [Google Scholar]
- 6.Oncel RC, Ucar M, Karakas MS, Akdemir B, Yanikoglu A, Gulcan AR, Altekin RE, Demir I. Relation of neutrophil-to-lymphocyte ratio with GRACE risk score to in-hospital cardiac events in patients with ST-segment elevated myocardial infarction. Clin Appl Thromb Hemost. 2015;21(4):383–8. [DOI] [PubMed] [Google Scholar]
- 7.Libby P, Pasterkamp G, Crea F, Jang IK. Reassessing the mechanisms of acute coronary syndromes. Circ Res. 2019;124(1):150–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Gistera, Hansson GK. The immunology of atherosclerosis[J]. Nat Rev Nephrol. 2017;13(6):368–80. [DOI] [PubMed] [Google Scholar]
- 9.Kojima Y, Volkmer JP, McKenna K, Civelek M, Lusis AJ, Miller CL, Direnzo D, Nanda V, Ye J, Connolly AJ, Schadt EE, Quertermous T, Betancur P, Maegdefessel L, Matic LP, Hedin U, Weissman IL, Leeper NJ. CD47-blocking antibodies restore phagocytosis and prevent atherosclerosis. Nature. 2016;536(7614):86–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Lee S, Lee HC, Kwon YW, Lee SE, Cho Y, Kim J, Lee S, Kim JY, Lee J, Yang HM, Jung M, Nam I, Chung KY, Lazar J, M. A., and, Kim HS. Adenylyl cyclase associated protein 1 is a receptor for human resistin and mediates inflammatory actions of human monocytes. Cell Metab. 2014;19:484497. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chen Y, Tao Y, Zhang L, Xu W, Zhou X. Diagnostic and prognostic value of biomarkers in acute myocardial infarction. Postgrad Med J. 2019;95(1122):210–6. [DOI] [PubMed] [Google Scholar]
- 12.Willingham SB, et al. The CD47-signal regulatory protein alpha (SIRPα) interaction is a therapeutic target for human solid tumors. Proc Natl Acad Sci USA. 2016;109:6662–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Xu Y, Jiang P, Xu Z, Ye H. Opportunities and challenges for anti-CD47 antibodies in hematological malignancies. Front Immunol. 2024;15:1348852. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Yu WB, Ye ZH, Chen X, et al. The development of small-mole-cule inhibitors targeting CD47 [J]. Volume S1359–6446. Drug Discov Today; 2020. pp. 30468–2. 20. [DOI] [PubMed]
- 15.Kakurina GV, Kolegova ES, Kondakova IV. Adenylyl Cyclase-Associated protein 1: structure, regulation, and participation in cellular processes. Biochem (Mosc). 2018;83(1):45–53. [DOI] [PubMed] [Google Scholar]
- 16.Singla B, Lin HP, Ahn W, Xu J, Ma Q, Sghayyer M, Dong K, Cherian-Shaw M, Zhou J, Huo Y, White J, Csányi G. Loss of myeloid cell-specific SIRPα, but not CD47, attenuates inflammation and suppresses atherosclerosis. Cardiovasc Res. 2022;118(15):3097–111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Jarr KU, Ye J, Kojima Y, Ye Z, Gao H, Schmid S, Luo L, Baylis RA, Lotfi M, Lopez N, Eberhard AV, Smith BR, Weissman IL, Maegdefessel L, Leeper NJ. The pleiotropic benefits of Statins include the ability to reduce CD47 and amplify the effect of pro-efferocytic therapies in atherosclerosis. Nat Cardiovasc Res. 2022;1(3):253–62. 10.1038/s44161-022-00023-x. Epub 2022 Mar 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Betancur PA, Abraham BJ, Yiu YY, Willingham SB, Khameneh F, Zarnegar M, Kuo AH, McKenna K, Kojima Y, Leeper NJ, Ho P, Gip P, Swigut T, Sherwood RI, Clarke MF, Somlo G, Young RA, Weissman IL. A CD47-associated super-enhancer links pro-inflammatory signalling to CD47 upregulation in breast cancer. Nat Commun. 2017;8:14802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Liu J, Wang L, Zhao F, Tseng S, Narayanan C, Shura L, Willingham S, Howard M, Prohaska S, Volkmer J, Chao M, Weissman IL, Majeti R. Pre-Clinical Development of a Humanized Anti-CD47 Antibody with Anti-Cancer Therapeutic Potential. PLoS One. 2015;10(9):e0137345. pcked="1"> [DOI] [PMC free article] [PubMed]
- 20.Fihn SD, Blankenship JC, Alexander KP, Bittl JA, Byrne JG, Fletcher BJ, Fonarow GC, Lange RA, Levine GN, Maddox TM, Naidu SS, Ohman EM, Smith PK, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Guyton RA, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen WK, American College of Cardiology/Americal Heart Association Task Force on Practice Guidelines; American Association for Thoracic Surgery; Preventive ardiovascular Nurses Association; Society for Cardiovascular Angiography and Interventions; Society of Thoracic Surgeons. Of the guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American college of cardiology/american heart association task force on practice guidelines, and the American association for thoracic surgery, preventive cardiovascular nurses association, society for cardiovascular angiography and interventions, and society of thoracic surgeons. J Thorac Cardiovasc Surg. 2015;149(3):e5–23. 2014 ACC/AHA/AATS/PCNA/SCAI/STS focused update. [DOI] [PubMed] [Google Scholar]
- 21.Engelbertsen D, Autio A, Verwilligen RAF, Depuydt MAC, Newton G, Rattik S, Levinsohn E, Saggu G, Jarolim P, Wang H, Velazquez F, Lichtman AH, Luscinskas FW. Increased lymphocyte activation and atherosclerosis in CD47-deficient mice. Sci Rep. 2019;9(1):10608. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Flores AM, Hosseini-Nassab N, Jarr KU, Ye J, Zhu X, Wirka R, Koh AL, Tsantilas P, Wang Y, Nanda V, Kojima Y, Zeng Y, Lotfi M, Sinclair R, Weissman IL, Ingelsson E, Smith BR, Leeper NJ. Pro-efferocytic nanoparticles are specifically taken up by lesional macrophages and prevent atherosclerosis. Nat Nanotechnol. 2020;15(2):154–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Isenberg JS, Hyodo F, Pappan LK, et al. Blocking thrombospondin-1 / CD47 signaling alleviates deleterious effects of aging on tissue responses to ischemia [J]. Arterioscler Thromb Vasc Biol. 2007;27(12):2582–8. [DOI] [PubMed] [Google Scholar]
- 24.Zhang J, Jin S, Guo X, et al. Targeting the CD47-SIRPalpha signaling axis: current studies on B-cell lymphoma immunotherapy[J]. J Int Med Res. 2018;46(11):4418–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Wang HB, Yang J, Ding JW, Chen LH, Li S, Liu XW, Yang CJ, Fan ZX, Yang J. RNAi-Mediated Down-Regulation of CD47 protects against Ischemia/Reperfusion-Induced myocardial damage via activation of eNOS in a rat model. Cell Physiol Biochem. 2016;40(5):1163–74. [DOI] [PubMed] [Google Scholar]
- 26.Zhang Q, Tang Q, Liu W, et al. Novel role of CAP1 in regulation RNA polymerase II-mediated transcription elongation depends on its actin-depolymerization activity in nucleoplasm. Oncogene. 2021;40:3492–509. [DOI] [PubMed] [Google Scholar]
- 27.Lee S, Lee HC, Kwon YW, Lee SE, Cho Y, Kim J, Lee S, Kim JY, Lee J, Yang HM, Mook-Jung I, Nam KY, Chung J, Lazar MA, Kim HS. Adenylyl cyclase-associated protein 1 is a receptor for human resistin and mediates inflammatory actions of human monocytes. Cell Metab. 2014;19(3):484–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Jang HD, Lee SE, Yang J, Lee HC, Shin D, Lee H, Lee J, Jin S, Kim S, Lee SJ, You J, Park HW, Nam KY, Lee SH, Park SW, Kim JS, Kim SY, Kwon YW, Kwak SH, Yang HM, Kim HS. Cyclase-associated protein 1 is a binding partner of proprotein convertase subtilisin/kexin type-9 and is required for the degradation of low-density lipoprotein receptors by proprotein convertase subtilisin/kexin type-9. Eur Heart J. 2020;41(2):239–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Munjas J, Sopic M, Bogavac-Stanojevic N, et al. Serum resistin, adenylate cyclase-associated protein 1 gene expression, and carotid intima-media thickness in patients with end-stage renal disease and healthy controls. Cardiorenal Med. 2020;10:51–60. [DOI] [PubMed] [Google Scholar]
- 30.Munjas J, Sopic M, Spasojevic-Kalimanovska V, et al. Association of adenylate cyclase-associated protein 1 with coronary artery disease. Eur J Clin Invest. 2017;47:659–66. [DOI] [PubMed] [Google Scholar]
- 31.Jin D, Li X, Cong H, You B, Ma Y, Hu Y, Zhang J. Role of serum CAP1 protein in the diagnosis of patients with first-time acute myocardial infarction. Med (Baltim). 2023;102(39):e34700. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.