Dataset Information

LncRNA MALAT1 functions as a biomarker of no-reflow phenomenon in ST-segment elevation myocardial infarction patients receiving primary percutaneous coronary intervention.

ABSTRACT: MALAT1 was reported to sponge miR-30e, miR-126 and miR-155 in the pathogenesis of many diseases. Plasma miR-30e can indicate the risk of no-reflow during primary percutaneous coronary intervention (pPCI), while miR-126 can be used as a predictor of coronary slow flow phenomenon. In this study, we compared the diagnostic value of above genes in the prediction of no-reflow phenomenon in ST-segment elevation myocardial infarction (STEMI) subjects receiving pPCI. Quantitative real-time PCR, ELISA, Western blot and luciferase assays were performed to explore the regulatory relationship of MALAT1/miR-30e, MALAT1/miR-126, MALAT1/miR-155, miR-126/HPSE, and miR-155/EDN1. ROC analysis was carried out to evaluate the potential value of MALAT1, miRNAs and target genes in differentiating normal reflow and no-reflow in STEMI patients receiving pPCI. Elevated MALAT1, CRP, HPSE, and EDN1 expression and suppressed miR-30e, miR-155 and miR-126 expression was found in the plasma of STEMI patients receiving pPCI who were diagnosed with no-reflow phenomenon. ROC analysis showed that the expression of MALAT1, miR-30e, miR-126 and CRP could be used as predictive biomarkers to differentiate normal reflow and no-reflow in STEMI patients receiving pPCI. MALAT1 was found to suppress the expression of miR-30e, miR-126 and miR-155, and HPSE and EDN1 were respectively targeted by miR-126 and miR-155. This study demonstrated that MALAT1 could respectively sponge the expression of miR-30e, miR-126 and miR-155. And miR-30e, miR-126 and miR-155 respectively targeted CRP, HPSE and EDN1 negatively. Moreover, MALAT1 could function as an effective biomarker of no-reflow phenomenon in STEMI patients receiving pPCI.

SUBMITTER: Yang X

PROVIDER: S-EPMC8885644 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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LncRNA MALAT1 functions as a biomarker of no-reflow phenomenon in ST-segment elevation myocardial infarction patients receiving primary percutaneous coronary intervention.

Yang Xiheng X Dai Rixin R Qin Zhong Z Cai Ruping R Xu Yuli Y Su Qiang Q

Scientific reports 20220228 1

MALAT1 was reported to sponge miR-30e, miR-126 and miR-155 in the pathogenesis of many diseases. Plasma miR-30e can indicate the risk of no-reflow during primary percutaneous coronary intervention (pPCI), while miR-126 can be used as a predictor of coronary slow flow phenomenon. In this study, we compared the diagnostic value of above genes in the prediction of no-reflow phenomenon in ST-segment elevation myocardial infarction (STEMI) subjects receiving pPCI. Quantitative real-time PCR, ELISA, W ...[more]

PMID: 35228564

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Project description:BackgroundNo-reflow (NRF) phenomenon is a significant challenge in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI). Accurate prediction of NRF may help improve clinical outcomes of patients. This retrospective study aimed at creating an optimal model based on machine learning (ML) to predict NRF in these patients, with the additional objective of guiding pre- and intra-operative decision-making to reduce NRF incidence.MethodsData were collected from 321 STEMI patients undergoing pPCI between January 2022 and May 2023, with the dataset being randomly divided into training and internal validation sets in a 7:3 ratio. Selected features included pre- and intra-operative demographic data, laboratory parameters, electrocardiogram, comorbidities, patients' clinical status, coronary angiographic data, and intraoperative interventions. Post comprehensive feature cleaning and engineering, three logistic regression (LR) models [LR-classic, LR-random forest (LR-RF), and LR-eXtreme Gradient Boosting (LR-XGB)], a RF model and an eXtreme Gradient Boosting (XGBoost) model were developed within the training set, followed by performance evaluation on the internal validation sets.ResultsAmong the 261 patients who met the inclusion criteria, 212 were allocated to the normal flow group and 49 to the NRF group. The training group consisted of 183 patients, while the internal validation group included 78 patients. The LR-XGB model, with an area under the curve (AUC) of 0.829 [95% confidence interval (CI): 0.779-0.880], was selected as the representative model for logistic regression analyses. The LR model had an AUC slightly lower than XGBoost model (AUC 0.835, 95% CI: 0.781-0.889) but significantly higher than RF model (AUC 0.731, 95% CI: 0.660-0.802). Internal validation underscored the unique advantages of each model, with the LR model demonstrating the highest clinical net benefit at relevant thresholds, as determined by decision curve analysis. The LR model encompassed seven meaningful features, and notably, thrombolysis in myocardial infarction flow after initial balloon dilation (TFAID) was the most impactful predictor in all models. A web-based application based on the LR model, hosting these predictive models, is available at https://l7173o-wang-lyn.shinyapps.io/shiny-1/.ConclusionsA LR model was successfully developed through ML to forecast NRF phenomena in STEMI patients undergoing pPCI. A web-based application derived from the LR model facilitates clinical implementation.

Project description:BackgroundThe purpose of this study was to screen the predictive factors of no-reflow after a percutaneous coronary intervention (PCI) in elderly patients with ST-segment elevation myocardial infarction (STEMI), and to construct a nomogram model, to guide clinical treatment.MethodsA total of 551 elderly STEMI patients (age >65) underwent direct PCI were randomly classified into training group (n=386, 70%) and validation group (n=165, 30%). All patients in the two groups were divided into a no-reflow group and a normal blood flow group according to whether there was a no-reflow phenomenon. Univariable and multivariable logistic regression analysis was used to analyze the relevant data, including demographic characteristics, clinical characteristics, coronary angiography results, electrocardiogram (ECG) results, and biochemical indicators. Then, a nomogram model was constructed on the screened risk factors. The performance of the nomogram was evaluated in terms of discrimination and calibration. The nomogram was further confirmed in the internal validation group. Additionally, decision curve analysis (DCA) was applied to assess the clinical usefulness of the nomogram.ResultsFive remarkable risk factors were determined: preoperative TIMI blood flow, the diameter of the target lesion, collateral circulation, pulse pressure, and the number of leads for ST-segment elevation. The nomogram involving these five risk factors showed full calibration and discrimination in the training group, with an AUC of 0.71 (95% CI: 0.66-0.77). It was confirmed in the validation group, and the entire cohort and the AUC were 0.64 (95% CI: 0.56-0.73) and 0.69 (95% CI: 0.65-0.74), respectively. Whether in the training group or the verification group, the calibration curve for the probability of no-reflow phenomenon all showed considerable consistency between prediction by nomogram and actual observation. The decision curve revealed a specific role in our nomogram in clinical practice.ConclusionsWe set up a nomogram that showed absolute accuracy for the prediction of the risk of no-reflow after primary PCI in elderly STEMI patients.

Project description:To date, the predictive role of laboratory indicators for the phenomenon of no flow is unclear. Hence, our objective was to conduct a meta-analysis to investigate the association between laboratory parameters and the risk of the no-reflow phenomenon in patients with ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI). This, in turn, aims to offer valuable insights for early clinical prediction of no-reflow. We searched Pubmed, Embase, and Cochrane Library from the establishment of the database to October 2023. We included case-control or cohort study that patients with STEMI following primary PCI. We excluded repeated publication, research without full text, incomplete information or inability to conduct data extraction and animal experiments, reviews, and systematic reviews. STATA 15.1 was used to analyze the data. The pooled results indicated that elevated white blood cell (WBC) count (odds ratio [OR] = 1.061, 95% confidence interval [CI]: 1.013-1.112), neutrophil count (OR = 1.324, 95% CI: 1.128-1.553), platelet (PLT) (OR = 1.002, 95% CI: 1.000-1.005), blood glucose (OR = 1.005, 95% CI: 1.002-1.009), creatinine (OR = 1.290, 95% CI: 1.070-1.555), total cholesterol (TC) (OR = 1.022, 95% CI: 1.012-1.032), d-dimer (OR = 1.002, 95% CI: 1.001-1.004), and fibrinogen (OR = 1.010, 95% CI: 1.005-1.015) were significantly associated with increased risk of no-reflow. However, elevated hemoglobin was significantly associated with decreased risk of no-reflow. In conclusion, our comprehensive analysis highlights the predictive potential of various parameters in assessing the risk of no-reflow among STEMI patients undergoing PCI. Specifically, WBC count, neutrophil count, PLT, blood glucose, hemoglobin, creatinine, TC, d-dimer, and fibrinogen emerged as significant predictors. This refined risk prediction may guide clinical decision-making, allowing for more targeted and effective preventive measures to mitigate the occurrence of no-reflow in this patient population.

Dataset Information

LncRNA MALAT1 functions as a biomarker of no-reflow phenomenon in ST-segment elevation myocardial infarction patients receiving primary percutaneous coronary intervention.

Publications

LncRNA MALAT1 functions as a biomarker of no-reflow phenomenon in ST-segment elevation myocardial infarction patients receiving primary percutaneous coronary intervention.

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