Skip to main content
Download PDF
Download ePub

Abnormal expression of circ_0013958 in patients with acute myocardial infarction (AMI) and its influence on prognosis

Journal of Cardiothoracic Surgery volume 19, Article number: 517 (2024) Cite this article

Abstract

Objective

The purpose of this study was to investigate the diagnostic value of circ_0013958 in acute myocardial infarction (AMI) patients and its influence on the prognosis of AMI patients.

Methods

The GSE160717 dataset was downloaded from the NCBI database and differentially expressed genes were analyzed between the control group and the AMI group. The up-regulated genes included circ_0013958. The expression of circ_0013958 in both groups was further verified by RT-qPCR. The Receiver Operating Characteristic curve was used to evaluate the diagnostic value of circ_0013958 in AMI. Pearson correlation analysis was used to examine the correlation between circ_0013958 levles and biochemical indicators. Binary logistic regression was used to analyze the risk factors affecting the occurrence of AMI. Prognostic analysis was performed using COX regression analysis and the Kaplan-Meier Curve.

Results

Compared to the control group, the level of circ_0013958 in AMI patients increased. Circ_0013958 can effectively distinguish AMI patients from non-AMI patients. Circ_0013958 levels were positively correlated with cTnI, LDH, CRP and TC levels. The elevated level of circ_0013958 was an independent risk factor for the occurrence of AMI. Higher circ_0013958 levels were also associated with the occurrence of major adverse cardiac events (MACEs) in AMI patients. Additionally, elevated circ_0013958 levels reduced the survival probability of AMI patients.

Conclusion

Circ_0013958 levels were up-regulated in AMI patients. It can be used as a diagnosis biomarker for AMI. The level of circ_0013958 was correlated with the disease severity and was an independent risk factor for the occurrence of AMI. Elevated circ_0013958 levels were associated with poor prognosis in AMI patients.

Peer Review reports

Introduction

Acute myocardial infarction (AMI) is a clinically critical disease in which acute occlusion of the coronary arteries results in myocardial ischemia and necrosis [1]. AMI is the most serious subtype of coronary heart disease, with hospitalization and mortality rates in European countries range from about 4–12% [2]. The main clinical manifestations of AMI include persistent acute circulatory dysfunction, severe retrosternal pain, heart failure and arrhythmia. Clinical data show that the incidence of AMI has been rising since 2002, seriously affecting daily life and posing a significant threat to life and health. If not treated promptly, it can lead to shock and even death [3]. In 2015, the number of AMI patients worldwide was estimated at 7.3 million [4]. Therefore, early diagnosis and treatment of AMI significantly impact its incidence and prognosis. Currently, the diagnosis and treatment of AMI are still in the research stage, it is urgent to find more biomarkers and methods to assess the risk and prognosis of AMI to enable rapid and accurate early diagnosis and reduce the morbidity and mortality of the disease.

Circulating circular RNAs (circRNAs) are a kind of endogenous non-coding RNA with a covalently closed ring structure formed by reverse splicing [5], playing a significant role in regulating gene expression [6], cell autophagy [7], cell differentiation [8], cell apoptosis and proliferation [9]. Studies have found that circRNAs play a biological function in various diseases such as cancer, cardiovascular disease, immune diseases, and other human disease [10]. Specifically, in cardiovascular diseases, circRNAs are crucial regulators in conditions like hypertension, atherosclerosis, AMI, and heart failure [11]. circRNAs regulate programmed death of cardiomyocytes, angiogenesis, myocardial fibrosis, and other pathophysiological processes closely related to the occurrence and development of AMI, and they have potential as biomarkers for early diagnosis of AMI. For example, circRNA cSMARCA5 levels were reduced in patients with AMI and inversely correlated with disease severity [12]. Knocking out circNfix can inhibit the apoptosis of cardiomyocytes, promote angiogenesis, and alleviate cardiac dysfunction after AMI [13]. cirUbe3a can promote the proliferation and migration of cardiac fibroblasts, worsening myocardial fibrosis after AMI [14]. We downloaded the GSE160717 dataset from the National Center for Biotechnology Information (NCBI) database and analyzed differentially expressed genes between non-AMI patients and AMI patients through bioinformatics. The up-regulated genes included circ_0013958. However, the diagnostic value of circ_0013958 in AMI patients remains unclear, and the effect of circ_0013958 levels on AMI progression and prognosis needs further study.

Based on the above research background, we speculate that circ_0013958 is abnormally expressed in AMI patients and is associated with prognosis. The objective is to investigate the diagnostic and prognostic value of circ_0013958 in AMI patients and to provide a novel biomarker with high sensitivity and specificity for early diagnosis of AMI.

Materials and methods

Participants

From December 2021 to October 2023, a total of 120 patients with AMI were included in this study, with an average age of 67.75 ± 6.35 years. This group included 80 males and 40 females. Additionally, 102 controls were selected with an average age of 68.07 ± 6.31 years, including 58 males and 44 females. We performed a prior power analysis by using the G*power software version 3.1.9.4 to determine the number of patients in the study. This study was reviewed and approved by Delta Health Hospital Shanghai Ethics Committee, and all patients and their families were informed and agreed to participate.

Inclusion criteria: [1] Meets the diagnostic criteria for AMI established by the World Health Organization, confirmed by coronary angiography; [2] Age: 18 ~ 80 years old; [3] All patients were first-time patients and were seen within 3 h; [4] Complete clinical information.

Exclusion criteria: [1] heart valve disease, myocarditis, myocardial hypertrophy, and dilatation; [2] chest pain caused by aortic dissection; [3] cardiogenic shock, acute heart failure, and other high-risk states; [4] malignant tumor; [5] connective tissue diseases or infectious diseases; [6] other major organ function diseases; [7] History of past myocardial infarction.

All AMI patients were followed up for six months. The occurrence of major adverse cardiovascular events (MACEs), including cardiogenic shock, malignant arrhythmia, myocardial infarction, heart failure, angina pectoris, and death, was recorded. Follow-up visits included outpatient visits, records of rehospitalization, and telephone follow-ups.

Blood sample preparation

Venous blood was collected from AMI patients on admission and controls on the day of physical examination. The samples were placed in an anticoagulant tube containing EDTA and left for 2 h at 4 ℃. After centrifugation (3000 r/min, 5 min), the supernatant was taken and stored at -80 ℃ for later use. AMI-related biochemical indicators, such as cardiac troponin I (cTnI), lactate dehydrogenase (LDH), C-reactive protein (CRP), total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) were determined using an automatic biochemical analyzer (NanoChecker710; Nano-Ditech, American).

RNA extraction and real-time quantitative reverse transcription PCR (RT-qPCR)

Step 1: Total RNA extraction from plasma. Total RNA was extracted using the Qiagen miRNeasy plasma purification kit, which was performed according to the standard procedure in the instructions. The RNA concentration was measured by spectrophotometer, and then stored in the refrigerator at -80 ℃.

Step 2: Reverse transcription PCR (RT-PCR). RT-PCR follows the standard procedure in the instructions for the RT-PCR kit (Vazyme, China). After genomic DNA was removed, the cDNA synthesis reaction solution was prepared in an RNase-free centrifuge tube, and the reaction procedure was as follows: 15 min at 37 ℃, 5 s at 85 ℃. The cDNA was stored in the refrigerator at -20 ℃, or directly for the next reaction.

Step 3: Quantitative real‑time (qPCR). According to the instructions, we used the qPCR SYBR Green Master Mix kit (Vazyme, China) to detect the relative expression of circ_0013958 in different samples. GAPDH is the internal reference gene. The primer sequence is as follows: circ_0013958 (Forward 5′-GTTTGCTGGCTGGGCTTTTCC-3′, Reverse 5′-GGGACCTCTAATAGCTGGGGGTTC-3′), GAPDH (Forward 5′-ACCATCTTCCAGGAGCGAGAT-3′, Reverse 5′-GGGCAGAGATGATGACCCTTT-3′). Under the guidance of the instruction manual, 2 µL DNA template, 0.4 µL upstream primer, and 0.4 µL downstream primer were added to the reaction system every 30 µL, and amplified by using Applied biosystems 7500 PCR instrument (Applied Biosystems, USA). Reaction conditions are as follows: 95 ℃, 30 s; 95 ℃, 10 s; 60 ℃, 30 s; 95 ℃, 15 s; 60 ℃, 60 s; 95 ℃, 15 s. All samples were set up with three repeat holes and the average value was calculated. The relative level of circ_0013958 in plasma was calculated by 2−ΔΔCT formula.

Statistical analysis

SPASS 22.0 software was used for statistical analysis. A normality test was performed on the data before statistical analysis. Measurement data were expressed as mean ± standard deviation, and Student’s t-test was used for inter-group comparison. Counting data was expressed as numbers (percentage) and the Chi-square test was used for comparison between groups. The Receiver Operating Characteristic (ROC) curve was used to evaluate the diagnostic value of circ_0013958 in AMI. Pearson correlation analysis was used to analyze the correlation between the two variables. Binary logistic regression was used to analyze the risk factors affecting the occurrence of AMI. Prognostic analysis was performed using COX regression analysis and the Kaplan-Meier (K-M) Curve. Test level: α = 0.05.

Results

Comparison of clinical data of subjects

The clinical data of all subjects were recorded in Table 1. A total of 222 subjects participated in this study, with 102 in the control group and 120 in the AMI group. Age, sex, and body mass index (BMI) were matched between controls and AMI patients (P > 0.05). Among the AMI patients, 67 had ST-elevation myocardial infarction (STEMI) and 53 had non-ST-elevation myocardial infarction (NSTEMI). There were no significant differences in smoking rates, diabetes mellitus, and hypertension between AMI and control groups (P > 0.05). Compared to controls, serum levels of cTnI, LDH, CRP, and TC in AMI patients were significantly increased, while HDL levels were decreased (P < 0.05).

Table 1 Comparison of clinical data of subjects
Full size table

Circ_0013958 was a diagnostic biomarker for AMI

We downloaded the GSE160717 dataset from the NCBI database, which contained 3 control samples and 3 AMI samples. Blue dots represent down-regulated genes, yellow dots represent up-regulated genes, and circ_0013958 is one of the up-regulated genes (Fig. 1A). The results of RT-qPCR showed that circ_0013958 levels in AMI patients were higher than in controls (P < 0.001, Fig. 1B). ROC analysis results showed that circ_0013958 could effectively distinguish AMI patients from non-AMI patients, indicating that circ_0013958 is a valuable biomarker for the diagnosis of AMI (AUC = 0.908, Sensitivity = 84.2%, Specificity = 86.2%, Fig. 2). Binary logistic regression analysis showed that circ_0013958 is an independent risk factor for the development of AMI (P < 0.001, Table 2).

Fig. 1
figure 1

circ0013958 level is upregulated in patients with AMI. (A) Volcano plot shows the fold change against the p value for genes. Blue and yellow dots represent genes with significant change. (B) Patients with AMI have elevated circ0013958 levels compared to controls. circ0013958 levels were detected by RT-qPCR. *** P < 0.001

Full size image
Fig. 2
figure 2

The diagnostic value of circ0013958 in AMI was analyzed by the ROC curve. circ0013958 can distinguish well between controls and AMI patients

Full size image
Table 2 Bivariate regression predicted independent factors of acute myocardial infarction
Full size table

Correlation between cir_c0013958 levels and biochemical indicators

To explore the relationship between circ_0013958 levels and the severity of AMI, we analyzed the correlation between circ_0013958 levels and biochemical markers. In controls, there was no correlation between circ_0013958 levels and biochemical markers (P > 0.05, Table 3). However, serum circ_0013958 levels were positively correlated with cTnI, LDH, CRP, and TC levels, and negatively correlated with HDL-C levels in AMI patients (P < 0.001, Table 3).

Table 3 Correlation analysis between circ_0013958 and each index
Full size table

Circ_0013958 levels were associated with poor prognosis in patients with AMI

The relationship between circ_0013958 levels and the occurrence of MACEs in AMI patients was analyzed. circ_0013958 levels in AMI patients without MACEs were significantly lower than in those with MACEs (P < 0.001, Fig. 3A). COX regression analysis further indicated that circ_0013958 was an independent predictor of MACEs in patients with AMI (P < 0.001, Fig. 3B). According to the average circ_0013958 level of AMI patients, AMI patients were divided into a low circ_0013958 level group (Group 0) and a high circ_0013958 level group (Group 1). We found that the survival probability between the two groups began to differ in the third month of follow-up. The survival probability of AMI patients with low circ_0013958 level was significantly higher than that of those with high circ_0013958 levels (P < 0.001, Fig. 4).

Fig. 3
figure 3

circ0013958 is a biomarker for predicting MACEs in AMI patients. (A) Patients who did not have MACEs had lower levels of circ0013958 compared to patients who had MACEs. The level of circ0013958 was detected by RT-qPCR. (B) COX regression analysis showed that circ0013958 was an independent predictor of MACEs in AMI patients. *** P < 0.001

Full size image
Fig. 4
figure 4

The relationship between circ0013958 levels and survival probability was analyzed by the K-M curve. Compared to Group 1, Group 0 has a higher probability of survival. “0” = low level group, “1” = high level group

Full size image

Discussion

CircRNAs are not sensitive to ribozymes, making them more stable than linear non-coding RNAs [15]. As effective sponges of microRNAs (miRNAs), circRNAs interact with miRNAs to regulate mRNA expression [16]. CircRNA is involved in the pathophysiological process of AMI and is related to the damage and repair of infarcted myocartis [14, 17, 18]. In our study, circ_0013958 levels were elevated in AMI patients compared to controls, suggesting that its involvement in the occurrence and development of AMI. Accurate diagnosis and timely treatment are essential for AMI patients. Currently, common indicators like CK-MB and cTnI are biomarkers for the diagnosis of AMI [19, 20]. However, more valuable biomarkers are still needed to quickly and accurately identify AMI. We analyzed the diagnostic value of circ_0013958 in AMI through the ROC curve. The AUC of circ_0013958 in diagnosing AMI was 0.908, with sensitivity and specificity were 84.2% and 86.2%, respectively. The binary logistic regression results showed that the elevated level of circ_0013958 was an independent risk factor for the occurrence of AMI. Therefore, circ_0013958 may be a potential biomarker for the diagnosis of AMI.

To explore the relationship between circ_0013958 levels and disease severity, Pearson correlation analysis was used to analyze the correlation between circ_0013958 levels and biochemical markers in AMI patients, including cTnI, LDH, CRP, TC, and HDL. Studies have shown that cTnI and LDH are commonly used as biomarkers for myocardial injury. when myocardial injury occurs, cTnI and LDH levels are elevated, making them useful for diagnosing AMI and assessing disease severity [21, 22]. Inflammation plays an important role in the occurrence and development of AMI. CRP is an acute-phase reaction protein involved in inflammation. After acute infarction, CRP levels significantly increase, which is associated with the severity of myocardial infarction and a higher risk of death [23, 24]. Dyslipidemia also increases the risk of AMI [25]. Compared with controls, AMI patients usually show dyslipidemia, which is specifically manifested as increased TC, TG, and LDL levels and decreased HDL levels [26]. In our study, the levels of circ_0013958, cTnI, LDH, CRP, and TC were increased, while HDL levels were decreased in AMI patients. Circ_0013958 levels in AMI patients were positively correlated with cTnI, LDH, and CRP and TC levels, while negatively correlated with HDL levels. Therefore, the occurrence and development of AMI may be related to the elevation of circ_0013958 levels.

Previous studies have shown that AMI patients are often accompanied by serious MACEs, such as cardiogenic shock [27], arrhythmia [28], heart failure [29], angina pectoris [30], and death. These are important factors leading to adverse outcomes in AMI patients. Therefore, early identification of AMI patients with high risk of MACEs and implementing corresponding interventions may help reduce the occurrence of MACEs, reduce mortality, and improve the prognosis of AMI patients. Therefore, we also explored the predictive value of circ_0013958 in MACEs. Compared to AMI patients without MACEs, those with MACEs had higher levels of circ_0013958. COX regression analysis showed that increased circ_0013958 levels were an independent risk factor for MACEs in AMI patients. In addition, the K-M curve results indicated that higher circ_0013958 levels shortened the survival time of AMI patients. A high level of circ_0013958 was associated with a poor prognosis in AMI patients. Therefore, measuring the levle of circ_0013958 can help identify AMI patients at high risk of MACEs early on.

Conclusion

In conclusion, circ_0013958 levels were higher in AMI patients than in non-AMI patients. Circ_0013958 can served as a diagnostic biomarker for AMI and was associated with poor prognosis in patients with AMI. Elevated circ_0013958 levels were independent risk factors for the occurrence of AMI and MACEs.

Our study provides a valuable new biomarker for the diagnosis of AMI, but a larger sample size is needed to verify the sensitivity and specificity of circ_0013958 in the diagnosis of AMI. In addition, the limitation of this study is that the action mechanism and molecular mechanism of circ_0013958 in AMI were not further explored. In the following studies, we will further investigate the action targets and regulatory pathways of circ_0013958 in AMI.

Data availability

Corresponding authors may provide data and materials.

References

  1. Ibrahim AW, Riddell TC, Devireddy CM. Acute myocardial infarction. Crit Care Clin. 2014;30(3):341–64.

    Article  PubMed  Google Scholar 

  2. Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2018;39(2):119–77.

    Article  PubMed  Google Scholar 

  3. Kapur NK, Thayer KL, Zweck E. Cardiogenic shock in the setting of Acute myocardial infarction. Methodist Debakey Cardiovasc J. 2020;16(1):16–21.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, et al. Global, Regional, and National Burden of Cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1–25.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jeck WR, Sharpless NE. Detecting and characterizing circular RNAs. Nat Biotechnol. 2014;32(5):453–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hansen TB, Jensen TI, Clausen BH, Bramsen JB, Finsen B, Damgaard CK, et al. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495(7441):384–8.

    Article  CAS  PubMed  Google Scholar 

  7. Yao H, Han B, Zhang Y, Shen L, Huang R. Non-coding RNAs and Autophagy. Adv Exp Med Biol. 2019;1206:199–220.

    Article  CAS  PubMed  Google Scholar 

  8. Lin Z, Tang X, Wan J, Zhang X, Liu C, Liu T. Functions and mechanisms of circular RNAs in regulating stem cell differentiation. RNA Biol. 2021;18(12):2136–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Huang Z, Ma W, Xiao J, Dai X, Ling W. CircRNA_0092516 regulates chondrocyte proliferation and apoptosis in osteoarthritis through the miR-337-3p/PTEN axis. J Biochem. 2021;169(4):467–75.

    Article  CAS  PubMed  Google Scholar 

  10. Li C, Ni YQ, Xu H, Xiang QY, Zhao Y, Zhan JK, et al. Roles and mechanisms of exosomal non-coding RNAs in human health and diseases. Signal Transduct Target Ther. 2021;6(1):383.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhou Q, Zhang Z, Bei Y, Li G, Wang T. Circular RNAs as novel biomarkers for Cardiovascular diseases. Adv Exp Med Biol. 2018;1087:159–70.

    Article  CAS  PubMed  Google Scholar 

  12. Zhao JW, Yu HY, Zhang YZ, Gao W. [Expression and clinical significance of circRNA cSMARCA5 in patients with acute myocardial infarction]. Zhonghua Yi Xue Za Zhi. 2023;103(12):901–6.

    CAS  PubMed  Google Scholar 

  13. Huang S, Li X, Zheng H, Si X, Li B, Wei G, et al. Loss of Super-enhancer-regulated circRNA Nfix induces Cardiac Regeneration after myocardial infarction in adult mice. Circulation. 2019;139(25):2857–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang Y, Li C, Zhao R, Qiu Z, Shen C, Wang Z, et al. CircUbe3a from M2 macrophage-derived small extracellular vesicles mediates myocardial fibrosis after acute myocardial infarction. Theranostics. 2021;11(13):6315–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yin L, Tang Y, Yuan Y. An overview of the advances in Research on the molecular function and specific role of circular RNA in Cardiovascular diseases. Biomed Res Int. 2022;2022:5154122.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cheng Y, Su Y, Wang S, Liu Y, Jin L, Wan Q et al. Identification of circRNA-lncRNA-miRNA-mRNA competitive endogenous RNA network as novel prognostic markers for Acute myeloid leukemia. Genes (Basel). 2020;11(8).

  17. Ren K, Li B, Jiang L, Liu Z, Wu F, Zhang Y, et al. circ_0023461 silencing protects cardiomyocytes from Hypoxia-Induced dysfunction through Targeting miR-370-3p/PDE4D signaling. Oxid Med Cell Longev. 2021;2021:8379962.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Tan J, Pan W, Chen H, Du Y, Jiang P, Zeng D, et al. Circ_0124644 serves as a ceRNA for mir-590-3p to promote Hypoxia-Induced cardiomyocytes Injury via regulating SOX4. Front Genet. 2021;12:667724.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Danese E, Montagnana M. An historical approach to the diagnostic biomarkers of acute coronary syndrome. Ann Transl Med. 2016;4(10):194.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kazemi Asl S, Rahimzadegan M. Recent advances in the fabrication of Nano-aptasensors for the detection of troponin as a main biomarker of Acute myocardial infarction. Crit Rev Anal Chem. 2023;53(3):594–613.

    Article  CAS  PubMed  Google Scholar 

  21. Blythe IM, Kennard C, Ruddock KH. Residual vision in patients with retrogeniculate lesions of the visual pathways. Brain. 1987;110(Pt 4):887–905.

    Article  PubMed  Google Scholar 

  22. Wei S, Mao L, Liu B, Zhong L. Serum biomarkers and the prognosis of AMI patients. Herz. 2014;39(3):384–9.

    Article  CAS  PubMed  Google Scholar 

  23. Gupta L, Thomas J, Ravichandran R, Singh M, Nag A, Panjiyar BK. Inflammation in Cardiovascular Disease: a comprehensive review of biomarkers and therapeutic targets. Cureus. 2023;15(9):e45483.

    PubMed  PubMed Central  Google Scholar 

  24. Razvi S, Jabbar A, Bano A, Ingoe L, Carey P, Junejo S et al. Triiodothyronine (T3), inflammation and mortality risk in patients with acute myocardial infarction. Eur Thyroid J. 2022;11(2).

  25. Lanas F, Avezum A, Bautista LE, Diaz R, Luna M, Islam S, et al. Risk factors for acute myocardial infarction in Latin America: the INTERHEART Latin American study. Circulation. 2007;115(9):1067–74.

    Article  PubMed  Google Scholar 

  26. Shipra, Gupta BK, Solanki R, Punia H, Agarwal V, Kaur J, et al. Relationship of lipid Profile and Serum Ferritin levels with Acute myocardial infarction. J Clin Diagn Res. 2014;8(8):CC10–3.

    Google Scholar 

  27. Henry TD, Tomey MI, Tamis-Holland JE, Thiele H, Rao SV, Menon V, et al. Invasive management of Acute myocardial infarction complicated by cardiogenic shock: a Scientific Statement from the American Heart Association. Circulation. 2021;143(15):e815–29.

    Article  PubMed  Google Scholar 

  28. Ruwald AC, Bloch Thomsen PE, Gang U, Jorgensen RM, Huikuri HV, Jons C. New-onset atrial fibrillation predicts malignant arrhythmias in post-myocardial infarction patients–a Cardiac arrhythmias and RIsk Stratification after acute myocardial infarction (CARISMA) substudy. Am Heart J. 2013;166(5):855–63. e3.

    Article  PubMed  Google Scholar 

  29. Harrington J, Jones WS, Udell JA, Hannan K, Bhatt DL, Anker SD, et al. Acute Decompensated Heart failure in the setting of Acute Coronary Syndrome. JACC Heart Fail. 2022;10(6):404–14.

    Article  PubMed  Google Scholar 

  30. Matsuda M, Matsuda Y, Ogawa H, Moritani K, Kusukawa R. Angina pectoris before and during acute myocardial infarction: relation to degree of physical activity. Am J Cardiol. 1985;55(11):1255–8.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

No funding was received to assist with the preparation of this work.

Author information

Author notes

  1. Fei Sun and Shenglan Zou contributed equally to the study.

Authors and Affiliations

  1. Department of Cardiology, DeltaHealth Hospital•Shanghai, No. 109, Xule Road, Xujing Town, Qingpu District, Shanghai, 201702, China

    Fei Sun, Xiaomin Li & Xueya Liu

  2. Department of Cardiology, Affiliated Hospital of Yangzhou University, Yangzhou, 225001, China

    Shenglan Zou

Authors
  1. Fei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shenglan Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaomin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xueya Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F. S and S.L. Z designed the research study. X.M. L and X.Y. L performed the research. F. S, S.L. Z, X.M. L and X.Y. L analyzed the data. F. S and S.L. Z wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fei Sun.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by The Ethics Committee of DeltaHealth Hospital Shanghai and followed the principles outlined in the Declaration of Helsinki. In addition, informed consent has been obtained from the participants involved.

Consent for publication

Not applicable.

Conflict of interest

Authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, F., Zou, S., Li, X. et al. Abnormal expression of circ_0013958 in patients with acute myocardial infarction (AMI) and its influence on prognosis. J Cardiothorac Surg 19, 517 (2024). https://doi.org/10.1186/s13019-024-03036-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13019-024-03036-8

Keywords

Journal of Cardiothoracic Surgery

ISSN: 1749-8090

Contact us