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Long noncoding RNA MALAT1 polymorphism predicts MACCEs in patients with myocardial infarction

BMC Cardiovascular Disorders volume 22, Article number: 152 (2022) Cite this article

Abstract

Background

Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) participates in the occurrence and development of cardiovascular and cerebrovascular diseases such as stroke and coronary heart disease by regulating inflammatory reactions, programmed cell death, and other pathological processes. Previous studies revealed that the MALAT1 gene polymorphism was associated with cardiac and cerebrovascular diseases. However, the prognostic role of the MALAT1 polymorphism in major adverse cardiac and cerebrovascular events (MACCEs) remains unknown. Therefore, this study intends to explore the association between the MALAT1 rs3200401 polymorphism and MACCEs.

Method

We enrolled 617 myocardial infarction (MI) patients and 1125 control participants who attended the First Affiliated Hospital of Xinjiang Medical University from January 2010 to 2018. SNPscan™ typing assays were used to detect the MALAT1 rs3200401 genotype. During the follow-up, MACCEs were recorded. Kaplan–Meier curves and univariate and multivariate Cox survival analyses were used to explore the correlation between MALAT1 gene polymorphisms and the occurrence of MACCEs.

Results

Among the total participants and MI patients, the frequencies of the T allele (total Participants 19.5% vs. 15.3%, P = 0.047, MI patients 20.7% vs. 14.1%, P = 0.014) and CT + TT genotypes (total Participants 37.4% vs. 28.1%, P = 0.013, MI patients 39.5% vs. 25.8%, P = 0.003) were significantly higher in subjects with MACCEs than in subjects without MACCEs. However, in control participants, the frequencies of the T allele (16.6% vs. 16.0%, P = 0.860) and CT + TT genotypes (31.4% vs. 29.3%, P = 0.760) were not higher in subjects with MACCEs than in subjects without MACCEs. In addition, among the total participants and MI patients, the Kaplan–Meier curve analysis indicated that the subjects with rs3200401 CT + TT genotypes had a higher incidence of MACCEs than CC genotype carriers (P = 0.015, P = 0.001). Nevertheless, similar results were not observed in the control participants (P = 0.790). Multivariate Cox regression indicated that compared with patients with the CC genotype, patients with CT + TT genotypes had a 1.554-fold increase in MACCE risk (hazard ratio: 1.554, 95% confidence interval: 1.060–2.277, P = 0.024).

Conclusions

The MALAT1 rs3200401 CT + TT genotypes could be a risk factor for MACCEs in MI patients, suggesting that the MALAT1 gene may become a biomarker for poor prognosis in MI patients.

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Introduction

Currently, with the aging of Chinese society and the development of urbanization, cardiac and cerebrovascular diseases, such as stroke, hypertension, and coronary heart disease, have become the leading killer diseases in China. Myocardial infarction (MI) is the main cause of mortality and disability among cardiac and cerebrovascular diseases. The death rate of MI increased by 3 times from 2010 to 2018 [1]. MI is necrosis of ischemic myocardium caused by occlusive thrombosis in the coronary artery [2]. For MI patients, early reperfusion therapies include fibrinolysis, percutaneous coronary intervention, and coronary artery bypass graft surgery. The long-term treatment strategy is mainly secondary prevention, including antithrombotic therapy, β-blocker treatment, lipid-lowering therapy, and lifestyle interventions [3]. However, current treatments cannot completely prevent major adverse cardiac and cerebrovascular events (MACCEs).

Long noncoding RNAs (lncRNAs) are usually transcripts of at least 200 base pairs in length that do not code for proteins. In the previous literature, researchers found that lncRNAs were involved in the incidence and development of MI. For example, lncRNA ZFAS1 could regulate sarcoplasmic reticulum Ca2+-ATPase 2a protein to impair cardiac contractile function in MI [4]. LncRNA CAIF suppresses cardiac autophagy and attenuates MI by binding p53 [5]. In addition, overexpression of lncRNA DACH1 could block cardiac repair and regeneration post-MI [6].

Metastasis-associated lung adenocarcinoma transcript-1 (MALAT1) is an 8779 bp transcript located at 11q13. The MALAT1 gene was first discovered in lung adenocarcinomas and reported to be a crucial factor in several human diseases, such as tumors [7], cardiovascular disease [8], and pulmonary arterial hypertension [9]. In the study of myocardial infarction, MALAT1 can regulate the programmed death of cardiomyocytes [10] and mediate cardiac fibrosis of cardiac fibroblasts [11], thereby promoting myocardial infarction. Hu et al. found that after 2 weeks of ligation of the left anterior descending coronary artery, the MALAT1 gene expression level increased 3 times in the infarct zone of the mouse heart, while the miR-320 expression level decreased, and a dual-luciferase reporter confirmed that lncRNA MALAT1 may directly target miR-320, thereby increasing cardiomyocyte apoptosis [12].

In previous studies, we investigated the relationship between the MALAT1 gene polymorphism and the occurrence of the acute coronary syndrome, including myocardial infarction [13]. However, the prognostic role of the MALAT1 polymorphism in MACCE has not been discovered. This study intended to explore the association of the MALAT1 gene rs3200401 polymorphism with MACCEs among control participants and MI patients.

Materials and methods

Study population

This was a case–control study to investigate the relationship between MALAT1 gene polymorphisms and MACCE susceptibility in Xinjiang Province. All participants were recruited at the First Affiliated Hospital of Xinjiang Medical University from 2010 to 2018.

The diagnosis of MI was made according to the guidelines [14]. All MI participants underwent arteriography to verify coronary artery stenosis (> 50% reduction luminal diameter). The Gensini score was calculated according to the angiography result to evaluate the extent of coronary stenosis [15].

We also recruited control participants who had no history of cardiovascular diseases and no signs of ischemic heart disease. The exclusion criteria for all participants were valvular heart disease, congenital heart disease, nonischemic cardiopulmonary disease, or acute and chronic inflammatory diseases.

Biochemical analysis

Peripheral venous blood samples (5 mL) were collected in EDTA-containing tubes from all participants following overnight fasting for biochemical assays. Subsequently, the samples were centrifuged at 5000 rpm for 5 min to separate plasma from blood cells at 4 °C. Some plasma samples were sent to the Central Laboratory of the First Affiliated Hospital of Xinjiang Medical University for biochemical assays, including glucose, total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), apolipoprotein A (ApoA), apolipoprotein B (ApoB), and lipoprotein(a) (Lp(a)), using a commercially available automated platform. Blood cells were kept at − 80 °C separately for further analysis [16].

Definition of cardiovascular risk factors

Body mass index (BMI) was calculated by dividing body weight (in kilograms) by height in meters squared. Smokers were regarded as people who regularly smoked for over 6 months. Hypertension was defined as systolic blood pressure (SBP) above 140 mmHg or diastolic blood pressure (DBP) above 90 mmHg, having previously been diagnosed by a physician or taking antihypertensive drugs [17]. A person who had abnormal fasting blood glucose or abnormal glucose tolerance was defined as a diabetes patient according to the Chinese guidelines for type 2 diabetes mellitus [18].

Genetic polymorphism selection and genotyping

We utilized a commercial whole blood genome extraction kit to extract DNA from peripheral blood leukocytes according to the manufacturer’s instructions (Tiangen Biotech, China). We used SNPscan™ typing assays to detect polymorphisms of the MALAT1 gene. The probes for the identification of primer sequences were 5′-TGCATTTACTTGCCAACAGAACAGAAAG-3′ and 5′-TGCATTTACTTGCCAACAGAACAGAGAA-3′. The universal primer was 5′-ACCTGAAGTCAAGACAACTGCATTC-3′.

Follow-up and description of endpoints

In the evaluation of long-term clinical outcomes, MACCEs were considered as the endpoints, including cardiac and noncardiac death, nonfatal acute myocardial infarction, unplanned revascularization (new percutaneous coronary intervention or bypass cardiac surgery), malignant arrhythmia, development of congestive heart failure, and stroke. Cardiovascular events were defined according to the European Society of Cardiology (ESC) guidelines and the Standardized Definitions for Cardiovascular and Stroke End Point Events in Clinical Trials of the Clinical Data Interchange Standards Consortium (CDISC) [3, 19]. Participants were followed up every 6 months after discharge through telephone communications or face-to-face interviews with the patients or their family members by a trained research cardiologist using a structured questionnaire. Finally, 518 patients (29.7%) were lost to follow-up.

Statistical analysis

Data analysis was performed using Statistical Package for Social Sciences-SPSS (version 22.0, SPSS Institute, Chicago, IL, USA). Continuous variables are expressed as the mean ± standard deviation (SD) or median (quartile), and the difference between the two groups was detected by independent-sample t-test or nonparametric rank test. Categorical variables are presented as n (%), and the difference between the two groups was detected by the chi-square test or Fisher’s exact test. For the analysis of the risk for MACCEs, univariate/multivariate Cox regression analyses were calculated for the influence of the risk factors on MACCEs. The results of the Cox models are presented as the hazard ratio (HR) and 95% confidence interval (CI). Kaplan–Meier analysis was used to assess the associations between participants’ survival rate and the MALAT1 polymorphism. A P value < 0.05 was deemed statistically significant.

Results

General characteristics of participants

Table 1 shows the general characteristics of the enrolled participants. A total of 495 MI patients (mean age 57.75 ± 12.58 years and 82.2% men) and 729 control participants (mean age 54.60 ± 10.03 years and 48.2% men) were recruited in the present study.

Table 1 Clinical and demographic characteristics among the total participants, control participants and MI patients
Full size table

The median follow-up time was 60 months (IQR: 36, 82). During the follow-up period, 12.2% (174/1224) participants experienced MACCEs, 24.8% (123/495) MACCEs occurred in MI patients, and 7.0% (51/729) MACCEs occurred in control participants. The proportion of MACCEs in MI patients was higher than that in control participants (P < 0.001). During the follow-up period, of the 174 patients who had MACCEs, 19 patients had cardiac deaths, 22 patients had noncardiovascular death, 19 patients had cerebrovascular events, and 110 patients had cardiac events, including nonfatal acute myocardial infarction, unplanned revascularization (new percutaneous coronary intervention or bypass cardiac surgery), malignant arrhythmia, and the development of congestive heart failure.

Among all participants, compared with the nonMACCE group, participants in the MACCE group were older (60.86 ± 11.82 vs. 55.05 ± 10.92, P < 0.001) and had a higher proportion of diabetes patients (24.1% vs. 14.9%, P = 0.014), higher levels of admission blood glucose (7.16 (5.27, 9.36) vs. 5.45 (4.71, 7.47), P < 0.001), higher levels of TC (4.28 (3.56, 5.04) vs. 1.55 (1.05, 2.25), P = 0.014), and lower levels of HDL-C (1.02 ± 0.27 vs. 1.08 ± 0.29, P = 0.014) and lower levels of ApoA (1.16 ± 0.27 vs. 1.22 ± 0.31, P = 0.018).

In MI patients, compared with the nonMACCE group, patients in the MACCE group were older (63.01 ± 12.47 vs. 56.53 ± 12.38, P < 0.001) and had a higher proportion of hypertension patients (52.0% vs. 28.2%, P = 0.002) and higher levels of admission blood glucose [8.01 (6.74, 10.58) vs. 7.58 (6.35, 10.30), P = 0.027].

For the control participants, compared with the nonMACCE group, subjects in the MACCE group were also older (59.76 ± 9.80 vs. 54.53 ± 9.96, P < 0.001) and had lower levels of ApoA (1.13 ± 0.24 vs. 1.23 ± 0.26, P = 0.032).

Distribution of MALAT1 gene polymorphism

Among the total participants, the MACCE group had higher frequencies of the rs3200401 T allele and CT + TT genotypes than the nonMACCE group (T allele, 19.5% vs. 15.3%, P = 0.047, CT + TT genotypes, 37.4% vs. 28.1%, P = 0.013, Table 2).

Table 2 Distribution of MALAT1 gene rs3200401 among total participants, control participants and MI patients
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Similarly, in the MI patients, the MACCE group had higher frequencies of the rs3200401 T allele and CT + TT genotypes than the nonMACCE group (T allele, 20.7% vs. 14.1%, P = 0.014, CT + TT genotypes, 39.5% vs. 25.8%, P = 0.003, Table 2).

However, for the control participants, we did not observe a prominent difference in the distribution of the rs3200401 T allele or CT + TT genotypes between the MACCE group and the nonMACCE group (T allele, 16.7% vs. 16.0%, P = 0.860, CT + TT genotypes, 31.4% vs. 29.4%, P = 0.760, Table 2).

Angiography MI patients with different MALAT1 genotypes

Among the MI patients carrying the CT + TT genotypes or the CC genotype, there was no significant difference in the number of lesion coronary arteries (P = 0.381, Table 3). Meanwhile, the angiography findings, including the number of left circumflex branches, left anterior descending branch, left main, right coronary artery with lesions and revascularization, did not differ by rs3200401 genotype (P = 0.732, P = 0.076, Table 3). In addition, there was no significant difference in Gensini scores between the rs3200401 CT + TT genotypes and the CC genotype in MI patients (53 (37, 84.5) vs. 60 (35, 84), P = 0.567, Table 3).

Table 3 Angiography and revascularization of MI patients with different rs3200401 genotypes
Full size table

MACCE risk and rs3200401 genotypes

The Kaplan–Meier analysis revealed that among all participants and MI patients, the MACCE-free cumulative survival rate in the CT + TT genotype group was obviously lower than that in the CC genotype group (P = 0.015, Fig. 1a, P = 0.001, Fig. 1c). However, in the control participants, there was no obvious difference between the CT + TT genotype group and the CC genotype group (P = 0.790, Fig. 1b).

Fig. 1
figure 1

Kaplan–Meier MACCE free survival analysis according to the rs3200401 genotype. a in total participants. b in control participants. c in MI patients

Full size image

Further, univariate Cox survival analysis was used to determine the risk factors for MACCEs among the total participants. The analysis showed that the rs3200401 CT + TT genotype, history of MI, male sex, age, diabetes, Gensini score, number of lesion coronary arteries, revascularization, admission blood glucose, TG, HDL-C, and ApoA were all MACCE risk factors. The multivariate Cox regression analyses suggested that the CT + TT genotypes of rs3200401 were associated with a risk for MACCEs (HR: 1.441, 95% CI 1.035–2.084, P = 0.031, Table 4).

Table 4 Univariate and multivariate Cox analyses among the total participants
Full size table

In addition, we studied the risk factors for MACCEs among MI patients with univariate Cox survival analysis. It indicated that the CT + TT genotypes of rs3200401, age, smoker, drinker, hypertension, Gensini score, number of lesion coronary arteries and TG were all risk factors for MACCE. After adjustment by multivariate Cox regression analyses, patients with the CT + TT genotypes of rs3200401 were associated with a higher risk of MACCEs than those carrying the CC genotype (HR: 1.554, 95% CI 1.060–2.277, P = 0.024, Table 5).

Table 5 Univariate and multivariate Cox analyses among the MI patients
Full size table

Discussion

In the present study, we investigated the relationship between the MALAT1 gene polymorphism and MACCE and found that subjects carrying the CT + TT genotypes of rs3200401 were susceptible to MACCE, especially MI patients.

Human genome sequencing and the GENCODE project launched in 2003 have revealed that the minority of the human genome can be translated into proteins and that the majority of the genome is primarily transcribed to produce noncoding RNAs [20]. Noncoding RNAs include miRNAs, lncRNAs, and circular RNAs. To date, noncoding RNAs have been identified to regulate gene functions and shown to play a role in various biological processes, including epigenetic and transcriptional regulation [21], protein biosynthesizing processes, pluripotency and differentiation [22], embryogenesis and development [23], and dynamic developmental and cell-specific expression patterns. Currently, several lncRNAs have been reported to be involved in heart disease [5, 6, 24]. In addition, lncRNAs are also emerging as biomarkers for cardiovascular diseases [25,26,27,28]; for example, ANRIL, KCNQ1OT1, and MIAT are markers of left ventricular dysfunction in postmyocardial infarction [29].

Jian-Zhong Wang et al. first discovered that the MALAT1 gene rs3200401 CT + TT genotypes were associated with better survival in patients with advanced lung adenocarcinoma [30]. In our study, we firstly investigated the association between the MALAT1 rs3200401 polymorphism and MACCEs occurring among MI patients and control participants and found the rs3200401 CT + TT genotypes were independent factors of MACCEs among MI patients. Nevine Fathy et al. reported that the CT + TT genotypes of rs3200401 were the independent predictors of cerebral ischemic stroke in Egypt [31]. Moreover, Yi-Lan Li et al. found that the rs3200401 TT genotype carriers had a higher level of total cholesterol than CC + CT genotypes carriers in MI patients [32]. However, Genan Wang et al. found rs3200401 CT + TT genotypes carriers had a lower level of total cholesterol among coronary atherosclerotic heart disease patients [33]. Furthermore, it is well known that risk factors of MACCEs include age, diabetes, hypertension, dyslipidemia, and the severity of ischemia disease. Considering that the CT + TT genotypes of rs3200401 carriers had a higher concentration of total cholesterol in MI patients [32] and had a disease exacerbation among the cerebral ischemic stroke patients [31], we speculated that the lncRNA MALAT1 gene rs3200401 CT + TT genotypes may affect lipid disorders and aggravate the exacerbation in cerebral ischemia, leading to a higher incidence of MACCEs. In addition, it is reported that the rs3200401 CT + TT genotypes could be a potential genetic marker of colorectal cancer predisposition [34], and the CT + TT genotypes of rs3200401 are associated with increased risk of esophageal squamous cell carcinoma [35], while Huimin Yan found the rs3200401 CT + TT genotypes tend to elevated Parkinson’s disease susceptibility [36]. Taken together, the MALAT1 gene rs3200401 polymorphism may be a potential functional mutation that deserves further research and may be a genetic biomarker of MACCEs susceptibility among MI patients.

In the present study, there were several limitations. First, this was not a multicenter and large-sample study, which would provide less robust statistical estimations. Second, compared with the MACCE group, the participants were a little older than the participants in the nonMACCE group and it may leave the possibility that the predictive power of the MALAT1 gene rs3200401 polymorphism for MACCE could be influenced. After adjusting the confounders via multivariate Cox regression analyses, the MALAT1 gene rs3200401 CT + TT genotypes remained the risk factor for MACCEs in MI patients. However, age was not the risk factor for MACCEs so the age was not the confounder of MACCEs in this study which did not significantly influence the power of MALAT1 gene rs3200401 polymorphism for MACCEs. Third, we only analyzed the association between MALAT1 gene rs3200401 CT + TT genotypes and MACCE. However, we did not measure the level of lncRNA MALAT1 in plasma, resulting in the linkage between the expression of lncRNA MALAT1 in plasma and MACCE being undetected, which deserves further research.

In summary, these results indicated that the MALAT1 gene played an important role in the prognosis of MI patients and that the MALAT1 gene rs3200401 CT + TT genotypes are risk factors for the poor prognosis of MI patients, suggesting that the MALAT1 gene can be used as a biomarker susceptible to MACCE for MI patients. Further gene sequencing for individuals with traditional risk factors could help to identify whether they carry MALAT1 rs3200401 CT + TT genotypes, which may help clinicians distinguish high-risk MACCE patients with a history of MI. This is also the real purpose of precision medicine.

Conclusions

In conclusion, our study revealed that MALAT1 rs3200401 CT + TT genotypes could be a risk factor for MACCEs in MI patients, suggesting that the MALAT1 gene may become a biomarker for poor prognosis in MI patients.

Availability of and data materials

The datasets generated and analyzed during the current study are available in the NCBI dbSNP database, SUB11120626.

References

  1. Interpretation of report on cardiovascular health and diseases in China 2020. Chin J Cardiovasc Med. 2021;26(3):209–18.

  2. Davies MJ, Thomas AC. Plaque fissuring–the cause of acute myocardial infarction, sudden ischaemic death, and crescendo angina. Br Heart J. 1985;53(4):363–73.

    Article  CAS  Google Scholar 

  3. Ibanez B, James S, Agewall S, 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  Google Scholar 

  4. Zhang Y, Jiao L, Sun L, et al. LncRNA ZFAS1 as a SERCA2a inhibitor to cause intracellular Ca(2+) overload and contractile dysfunction in a mouse model of myocardial infarction. Circ Res. 2018;122(10):1354–68.

    Article  CAS  Google Scholar 

  5. Liu CY, Zhang YH, Li RB, et al. LncRNA CAIF inhibits autophagy and attenuates myocardial infarction by blocking p53-mediated myocardin transcription. Nat Commun. 2018;9(1):29.

    Article  Google Scholar 

  6. Cai B, Ma W, Wang X, et al. Targeting LncDACH1 promotes cardiac repair and regeneration after myocardium infarction. Cell Death Differ. 2020;27(7):2158–75.

    Article  CAS  Google Scholar 

  7. Kim J, Piao HL, Kim BJ, et al. Long noncoding RNA MALAT1 suppresses breast cancer metastasis. Nat Genet. 2018;50(12):1705–15.

    Article  CAS  Google Scholar 

  8. Ansani L, Marchesini J, Pestelli G, et al. F13A1 gene variant (V34L) and residual circulating FXIIIA levels predict short- and long-term mortality in acute myocardial infarction after coronary angioplasty. Int J Mol Sci. 2018;19(9):2766.

    Article  Google Scholar 

  9. Zhuo Y, Zeng Q, Zhang P, Li G, Xie Q, Cheng Y. Functional polymorphism of lncRNA MALAT1 contributes to pulmonary arterial hypertension susceptibility in Chinese people. Clin Chem Lab Med. 2017;55(1):38–46.

    Article  CAS  Google Scholar 

  10. Hu H, Wu J, Yu X, Zhou J, Yu H, Ma L. Long non-coding RNA MALAT1 enhances the apoptosis of cardiomyocytes through autophagy inhibition by regulating TSC2-mTOR signaling. Biol Res. 2019;52(1):58.

    Article  Google Scholar 

  11. Huang S, Zhang L, Song J, et al. Long noncoding RNA MALAT1 mediates cardiac fibrosis in experimental postinfarct myocardium mice model. J Cell Physiol. 2019;234(3):2997–3006.

    Article  CAS  Google Scholar 

  12. Hu H, Wu J, Li D, Zhou J, Yu H, Ma L. Knockdown of lncRNA MALAT1 attenuates acute myocardial infarction through miR-320-Pten axis. Biomed Pharmacother. 2018;106:738–46.

    Article  CAS  Google Scholar 

  13. Song N, Luo JY, Zhao Q, et al. MALAT1 gene rs600231 polymorphism positively associated with acute coronary syndrome in Chinese population: a case-control study. Cardiovasc Diagn Ther. 2021;11(2):435–46.

    Article  Google Scholar 

  14. Zhao Q, Men L, Li XM, et al. Circulating MIF levels predict clinical outcomes in patients With ST-elevation myocardial infarction after percutaneous coronary intervention. Can J Cardiol. 2019;35(10):1366–76.

    Article  Google Scholar 

  15. Luo JY, Li XM, Zhou Y, et al. Mutant DD genotype of NFKB1 gene is associated with the susceptibility and severity of coronary artery disease. J Mol Cell Cardiol. 2017;103:56–64.

    Article  CAS  Google Scholar 

  16. Luo JY, Li YH, Fang BB, Tian T, Liu F, Li XM, et al. NFKB1 gene rs28362491 ins/del variation is associated with higher susceptibility to myocardial infarction in a Chinese Han population. Sci Rep. 2020;10(1):19518.

    Article  CAS  Google Scholar 

  17. Mancia G. The new American guidelines on hypertension: a favorable opinion. J Clin Hypertens (Greenwich). 2014;16(4):255–6.

    Article  Google Scholar 

  18. Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition). Chin J Diabetes Mellit. 2021;13(04):315–409.

  19. Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2016;37(27):2129–200.

    Article  Google Scholar 

  20. Derrien T, Johnson R, Bussotti G, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 2012;22(9):1775–89.

    Article  CAS  Google Scholar 

  21. Dykes IM, Emanueli C. Transcriptional and post-transcriptional gene regulation by long non-coding RNA. Genom Proteom Bioinform. 2017;15(3):177–86.

    Article  Google Scholar 

  22. Zhang Y, Li Y, Hu Q, et al. The lncRNA H19 alleviates muscular dystrophy by stabilizing dystrophin. Nat Cell Biol. 2020;22(11):1332–45.

    Article  CAS  Google Scholar 

  23. Carlson HL, Stadler HS. Development and functional characterization of a lncRNA-HIT conditional loss of function allele. Genesis. 2020;58(3–4):e23351.

    CAS  PubMed  Google Scholar 

  24. Zhu Y, Yang T, Duan J, Mu N, Zhang T. MALAT1/miR-15b-5p/MAPK1 mediates endothelial progenitor cells autophagy and affects coronary atherosclerotic heart disease via mTOR signaling pathway. Aging (Albany NY). 2019;11(4):1089–109.

    Article  CAS  Google Scholar 

  25. Zhang Y, Sun L, Xuan L, et al. reciprocal changes of circulating long non-coding RNAs ZFAS1 and CDR1AS predict acute myocardial infarction. Sci Rep. 2016;6:22384.

    Article  CAS  Google Scholar 

  26. Cai Y, Yang Y, Chen X, et al. Circulating “lncRNA OTTHUMT00000387022” from monocytes as a novel biomarker for coronary artery disease. Cardiovasc Res. 2016;112(3):714–24.

    Article  CAS  Google Scholar 

  27. Arbiol-Roca A, Padró-Miquel A, Hueso M, et al. Association of ANRIL gene polymorphisms with major adverse cardiovascular events in hemodialysis patients. Clin Chim Acta. 2017;466:61–7.

    Article  CAS  Google Scholar 

  28. Ma L, Liu M, Li FL. Comparison of therapeutic effects of ticagrelor and clopidogrel on patients with acute myocardial infarction and influence of lncRNA BANCR. Eur Rev Med Pharmacol Sci. 2020;24(23):12315–23.

    CAS  PubMed  Google Scholar 

  29. Poller W, Dimmeler S, Heymans S, et al. Non-coding RNAs in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur Heart J. 2018;39(29):2704–16.

    Article  CAS  Google Scholar 

  30. Wang JZ, Xiang JJ, Wu LG, et al. A genetic variant in long non-coding RNA MALAT1 associated with survival outcome among patients with advanced lung adenocarcinoma: a survival cohort analysis. BMC Cancer. 2017;17(1):167.

    Article  Google Scholar 

  31. Fathy N, Kortam MA, Shaker OG, Sayed NH. Long noncoding RNAs MALAT1 and ANRIL gene variants and the risk of cerebral ischemic stroke: an association study. ACS Chem Neurosci. 2021;12(8):1351–62.

    Article  CAS  Google Scholar 

  32. Li Y, Zhang D, Zhang Y, et al. Association of lncRNA polymorphisms with triglyceride and total cholesterol levels among myocardial infarction patients in Chinese population. Gene. 2020;724:143684.

    Article  CAS  Google Scholar 

  33. Wang G, Li Y, Peng Y, Tang J, Li H. Association of polymorphisms in MALAT1 with risk of coronary atherosclerotic heart disease in a Chinese population. Lipids Health Dis. 2018;17(1):75.

    Article  Google Scholar 

  34. Radwan AF, Shaker OG, El-Boghdady NA, Senousy MA. Association of MALAT1 and PVT1 variants, expression profiles and target miRNA-101 and miRNA-186 with colorectal cancer: correlation with epithelial-mesenchymal transition. Int J Mol Sci. 2021;22(11):614.

    Article  Google Scholar 

  35. Qu Y, Shao N, Yang W, Wang J, Cheng Y. Association of polymorphisms in MALAT1 with the risk of esophageal squamous cell carcinoma in a Chinese population. Onco Targets Ther. 2019;12:2495–503.

    Article  CAS  Google Scholar 

  36. Yang H. LncRNA MALAT1 potentiates inflammation disorder in Parkinson’s disease. Int J Immunogenet. 2021;48(5):419–28.

    Article  CAS  Google Scholar 

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Acknowledgements

Our gratitude and thanks to all the patients who accepted and took part in this study.

Funding

This work was supported by project grants from the National Natural Science Foundation of China (ID 82160091, 82160054).

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Author notes

  1. Tong Zhang and Jun-Yi Luo contributed equally to this manuscript.

Authors and Affiliations

  1. Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, 137 Liyushan South Road, Urumqi, 830054, Xinjiang, China

    Tong Zhang, Jun-Yi Luo, Xue-He Zhang, Fan Luo, Yi-Ning Yang & Xiao-Mei Li

  2. Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, 830054, Xinjiang, China

    Fen Liu, Yi-Ning Yang & Xiao-Mei Li

  3. People’s Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Urumqi, 830054, Xinjiang, China

    Yi-Ning Yang

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Contributions

Conceived and designed the experiments: YNY and XML. Data analysis: JYL and FL, TZ performed the experiments. Contributing reagents/materials/analysis tools: TZ, XHZ and FL. Quality control: XML and YNY. Paper draft: TZ. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yi-Ning Yang or Xiao-Mei Li.

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Ethics approval and consent to participate

This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University according to the standards of the Declaration of Helsinki. Written informed consent was obtained from all participants.

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The authors declare no competing interests.

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Zhang, T., Luo, JY., Liu, F. et al. Long noncoding RNA MALAT1 polymorphism predicts MACCEs in patients with myocardial infarction. BMC Cardiovasc Disord 22, 152 (2022). https://doi.org/10.1186/s12872-022-02590-0

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BMC Cardiovascular Disorders

ISSN: 1471-2261

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