Clinical Significance of MicroRNAs, Long Non-Coding RNAs, and CircRNAs in Cardiovascular Diseases

Singh, Desh Deepak; Kim, Youngsun; Choi, Seung Ah; Han, Ihn; Yadav, Dharmendra Kumar

doi:10.3390/cells12121629

Cited by 17 publications

(3 citation statements)

References 212 publications

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“…Non-coding RNAs (ncRNAs), which encompass different categories like miRNAs, lncRNAs, and circular RNAs (circRNAs), hold significant relevance in clinical applications. Recent research has unveiled that multiple types of ncRNAs play a role in controlling vascular tone to influence the pathophysiology of arterial hypertension (AH) [28]. For instance, lncRNAs contribute to the regulation of vascular smooth muscle cell function, and miR-221 and miR-222 impact lnc-Ang362, which, in turn, affects the growth of vascular smooth muscle cells [29].…”

Section: Discussionmentioning

confidence: 99%

“…Several lncRNAs, namely, NR_027032, NR_034083, and NR_104181, can serve as biomarkers for diagnosing AH [29]. Another lncRNA, GAS5 (growth arrest-specific 5) plays a role in regulating vascular remodeling in hypertension, with primary expression in endothelial cells (ECs) and VSMCs [28]. Several circRNAs appear to be associated with vascular endothelial dysfunction, which may contribute to the development of arterial hypertension.…”

Section: Discussionmentioning

confidence: 99%

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A Machine Learning Model Based on microRNAs for the Diagnosis of Essential Hypertension

Jusic,

Junuzovic,

Hujdurovic

et al. 2023

ncRNA

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Introduction: Hypertension is a major and modifiable risk factor for cardiovascular diseases. Essential, primary, or idiopathic hypertension accounts for 90–95% of all cases. Identifying novel biomarkers specific to essential hypertension may help in understanding pathophysiological pathways and developing personalized treatments. We tested whether the integration of circulating microRNAs (miRNAs) and clinical risk factors via machine learning modeling may provide useful information and novel tools for essential hypertension diagnosis and management. Materials and methods: In total, 174 participants were enrolled in the present observational case–control study, among which, there were 89 patients with essential hypertension and 85 controls. A discovery phase was conducted using small RNA sequencing in whole blood samples obtained from age- and sex-matched hypertension patients (n = 30) and controls (n = 30). A validation phase using RT-qPCR involved the remaining 114 participants. For machine learning, 170 participants with complete data were used to generate and evaluate the classification model. Results: Small RNA sequencing identified seven miRNAs downregulated in hypertensive patients as compared with controls in the discovery group, of which six were confirmed with RT-qPCR. In the validation group, miR-210-3p/361-3p/362-5p/378a-5p/501-5p were also downregulated in hypertensive patients. A machine learning support vector machine (SVM) model including clinical risk factors (sex, BMI, alcohol use, current smoker, and hypertension family history), miR-361-3p, and miR-501-5p was able to classify hypertension patients in a test dataset with an AUC of 0.90, a balanced accuracy of 0.87, a sensitivity of 0.83, and a specificity of 0.91. While five miRNAs exhibited substantial downregulation in hypertension patients, only miR-361-3p and miR-501-5p, alongside clinical risk factors, were consistently chosen in at least eight out of ten sub-training sets within the SVM model. Conclusions: This study highlights the potential significance of miRNA-based biomarkers in deepening our understanding of hypertension’s pathophysiology and in personalizing treatment strategies. The strong performance of the SVM model highlights its potential as a valuable asset for diagnosing and managing essential hypertension. The model remains to be extensively validated in independent patient cohorts before evaluating its added value in a clinical setting.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model Based on microRNAs for the Diagnosis of Essential Hypertension

Jusic,

Junuzovic,

Hujdurovic

et al. 2023

ncRNA

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“…While miRNAs show promise as potential key players in the early diagnosis of MI, further research is imperative to establish their potency as cardiac biomarkers and their potential implementation in everyday practice [39]. Lastly, long non-coding RNAs (lncR-NAs) and circular RNAs (circRNAs) can also shed light on the diagnosis and treatment of cardiovascular diseases [122].…”

Section: Micro-rnamentioning

confidence: 99%

Novel Biomarkers and Their Role in the Diagnosis and Prognosis of Acute Coronary Syndrome

Katsioupa,

Kourampi,

Oikonomou

et al. 2023

Life

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The burden of cardiovascular diseases and the critical role of acute coronary syndrome (ACS) in their progression underscore the need for effective diagnostic and prognostic tools. Biomarkers have emerged as crucial instruments for ACS diagnosis, risk stratification, and prognosis assessment. Among these, high-sensitivity troponin (hs-cTn) has revolutionized ACS diagnosis due to its superior sensitivity and negative predictive value. However, challenges regarding specificity, standardization, and interpretation persist. Beyond troponins, various biomarkers reflecting myocardial injury, neurohormonal activation, inflammation, thrombosis, and other pathways are being explored to refine ACS management. This review article comprehensively explores the landscape of clinically used biomarkers intricately involved in the pathophysiology, diagnosis, and prognosis of ACS (i.e., troponins, creatine kinase MB (CK-MB), B-type natriuretic peptides (BNP), copeptin, C-reactive protein (CRP), interleukin-6 (IL-6), d-dimers, fibrinogen), especially focusing on the prognostic role of natriuretic peptides and of inflammatory indices. Research data on novel biomarkers (i.e., endocan, galectin, soluble suppression of tumorigenicity (sST2), microRNAs (miRNAs), soluble oxidized low-density lipoprotein receptor-1 (sLOX-1), F2 isoprostanes, and growth differentiation factor 15 (GDF-15)) are further analyzed, aiming to shed light on the multiplicity of pathophysiologic mechanisms implicated in the evolution of ACS. By elucidating the complex interplay of these biomarkers in ACS pathophysiology, diagnosis, and outcomes, this review aims to enhance our understanding of the evolving trajectory and advancements in ACS management. However, further research is necessary to establish the clinical utility and integration of these biomarkers into routine practice to improve patient outcomes.

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The Diagnosis and Prognosis Value of Circulating Exosomal lncRNA MALAT1 and LNC_000226 in Patients With Acute Myocardial Infarction: An Observational Study

Gu,

Hou,

Weng

et al. 2024

Immunity Inflam & Disease

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BackgroundAcute myocardial infarction (AMI) stands as a leading cause of global morbidity and mortality. This study aims to explore the potential roles of circulating exosomal lncRNA MALAT1 and LNC_000226 in AMI diagnosis and prognosis.MethodsThis retrospective observational study included 90 patients with AMI and 88 patients with normal coronary artery (NCA). Plasma exosomes were isolated via ultracentrifugation, and the levels of exosomal lncRNA MALAT1 and LNC_000226 were examined using qRT‐PCR. Major adverse cardiovascular events (MACEs) that occurred during 1‐year follow‐up post‐stent implantation were collected. The diagnostic value of exosomal MALAT1 and LNC_000226 was determined by receiver operating characteristic (ROC) analysis. The association between exosomal LNC_000226 and MACEs was assessed by Kaplan–Meier and Cox regression analysis.ResultsBoth lncRNA MALAT1 and LNC_000226 levels in plasma exosomes were elevated in AMI patients compared to NCA controls. Moreover, LNC_000226 (AUC: 0.889, sensitivity: 82%, specificity: 72%) exhibited superior diagnostic performance compared to MALAT1 (AUC: 0.707, sensitivity: 71%, specificity: 57%). During 1‐year follow‐up period, the incidence of MACEs was significantly higher among patients with high exosomal LNC_000226 levels compared to those with low exosomal LNC_000226 levels [64% (29/45) vs. 40% (18/45), p < 0.05]. Multivariable Cox regression analysis revealed a positive association between exosomal LNC_000226 level and the risk of MACEs in AMI patients (HR: 1.959, 95% CI: 1.040–3.689).ConclusionCirculating exosomal lncRNA MALAT1 and LNC_000226 are promising biomarkers for diagnosing AMI, with LNC_000226 potentially indicating a prognosis.

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Clinical Significance of MicroRNAs, Long Non-Coding RNAs, and CircRNAs in Cardiovascular Diseases

Cited by 17 publications

References 212 publications

A Machine Learning Model Based on microRNAs for the Diagnosis of Essential Hypertension

A Machine Learning Model Based on microRNAs for the Diagnosis of Essential Hypertension

Novel Biomarkers and Their Role in the Diagnosis and Prognosis of Acute Coronary Syndrome

The Diagnosis and Prognosis Value of Circulating Exosomal lncRNA MALAT1 and LNC_000226 in Patients With Acute Myocardial Infarction: An Observational Study

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