Exosomes play critical roles in mediating cell-to-cell communication by delivering noncoding RNAs (including miRNAs, lncRNAs, and circRNAs). Our previous study found that cardiomyocytes (CMs) subjected to hypoxia released circHIPK3-rich exosomes to regulate oxidative stress damage in cardiac endothelial cells. However, the role of exosomes in regulating angiogenesis after myocardial infarction (MI) remains unknown. The aim of this study was to establish the effects of exosomes derived from hypoxia-induced CMs on the migration and angiogenic tube formation of cardiac endothelial cells. Here, we reported that hypoxic exosomes (HPC-exos) can effectively reduce the infarct area and promote angiogenesis in the border surrounding the infarcted area. HPC-exos can also promote cardiac endothelial cell migration, proliferation, and tube formation in vitro. However, these effects were weakened after silencing circHIPK3 in hypoxia-induced CMs. We further verified that silencing and overexpressing circHIPK3 changed cardiac endothelial cell proliferation, migration, and tube formation in vitro by regulating the miR-29a expression. In addition, exosomal circHIPK3 derived from hypoxia-induced CMs first led to increased VEGFA expression by inhibiting miR-29a activity and then promoted accelerated cell cycle progression and proliferation in cardiac endothelial cells. Overexpression of miR-29a mimicked the effect of silencing circHIPK3 on cardiac endothelial cell activity in vitro. Thus, our study provides a novel mechanism by which exosomal circRNAs are involved in the communication between CMs and cardiac endothelial cells.
Autophagy and apoptosis are involved in myocardial ischemia/reperfusion (I/R) injury. Research indicates that circular RNA HIPK3 (circHIPK3) is crucial to cell autophagy and apoptosis in various cancer types. However, the role of circHIPK3 in the regulation of cardiomyocyte autophagy and apoptosis during I/R remains unknown. Our study aimed to examine the regulatory effect of circHIPK3 during myocardial I/R and investigate its mechanism in cardiomyocyte autophagy and apoptosis. Methods and results. The expression of circHIPK3 was upregulated during myocardial I/R injury and hypoxia/reoxygenation (H/R) injury of cardiomyocytes. To study the potential role of circHIPK3 in myocardial H/R injury, we performed gain-of-function and loss-of-function analyses of circHIPK3 in cardiomyocytes. Overexpression of circHIPK3 significantly promoted H/R-induced cardiomyocyte autophagy and cell injury (increased intracellular reactive oxygen species (ROS) and apoptosis) compared to those in the control group, while silencing of circHIPK3 showed the opposite effect. Further research found that circHIPK3 acted as an endogenous miR-20b-5p sponge to sequester and inhibit miR-20b-5p activity, resulting in increased ATG7 expression. In addition, miR-20b-5p inhibitors reversed the decrease in ATG7 induced by silencing circHIPK3. Conclusions. CircHIPK3 can accelerate cardiomyocyte autophagy and apoptosis during myocardial I/R injury through the miR-20b-5p/ATG7 axis. These data suggest that circHIPK3 may serve as a potential therapeutic target for I/R.
Background: The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients. Methods: This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.
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