Doxorubicin-induced cardiotoxicity (DIC), which is a cardiovascular complication, has become the foremost determinant of decreased quality of life and mortality among survivors of malignant tumors, in addition to recurrence and metastasis. The limited ability to accurately predict the occurrence and severity of doxorubicin-induced injury has greatly hindered the prevention of DIC, but reducing the dose to mitigate side effects may compromise the effective treatment of primary malignancies. This has posed a longstanding clinical challenge for oncologists and cardiologists. Ferroptosis in cardiomyocytes has been shown to be a pivotal mechanism underlying cardiac dysfunction in DIC. Ferroptosis is influenced by multiple factors. The innate immune response, as exemplified by neutrophil extracellular traps (NETs), may play a significant role in the regulation of ferroptosis. Therefore, the objective of this study was to investigate the involvement of NETs in doxorubicin-induced cardiomyocyte ferroptosis and elucidate their regulatory role. This study confirmed the presence of NETs in DIC in vivo. Furthermore, we demonstrated that depleting neutrophils effectively reduced the occurrence of doxorubicin-induced ferroptosis and myocardial injury in DIC. Additionally, our findings showed the pivotal role of high mobility group box 1 (HMGB1) as a critical molecule implicated in DIC and emphasized its involvement in the modulation of ferroptosis subsequent to NETs inhibition. Mechanistically, we obtained preliminary evidence suggesting that doxorubicin-induced NETs could modulate yes-associated protein (YAP) activity by releasing HMGB1, which subsequently bound to toll like receptor 4 (TLR4) on the cardiomyocyte membrane, thereby influencing cardiomyocyte ferroptosis in vitro. Our findings suggest that doxorubicin-induced NETs modulate cardiomyocyte ferroptosis via the HMGB1/TLR4/YAP axis, thereby contributing to myocardial injury. This study offers a novel approach for preventing and alleviating DIC by targeting alterations in the immune microenvironment.

Background: Heart failure (HF) is a serious end-stage condition of various heart diseases with increasing frequency. Few studies have combined clinical features with high-throughput echocardiographic data to assess the risk of major cardiovascular events (MACE) in patients with heart failure. In this study, we assessed the relationship between these factors and heart failure to develop a practical and accurate prognostic dynamic nomogram model to identify high-risk groups of heart failure and ultimately provide tailored treatment options.Materials and methods: We conducted a prospective study of 468 patients with heart failure and established a clinical predictive model. Modeling to predict risk of MACE in heart failure patients within 6 months after discharge obtained 320 features including general clinical data, laboratory examination, 2-dimensional and Doppler measurements, left ventricular (LV) and left atrial (LA) speckle tracking echocardiography (STE), and left ventricular vector flow mapping (VFM) data, were obtained by building a model to predict the risk of MACE within 6 months of discharge for patients with heart failure. In addition, the addition of machine learning models also confirmed the necessity of increasing the STE and VFM parameters.Results: Through regular follow-up 6 months after discharge, MACE occurred in 156 patients (33.3%). The prediction model showed good discrimination C-statistic value, 0.876 (p < 0.05), which indicated good identical calibration and clinical efficacy. In multiple datasets, through machine learning multi-model comparison, we found that the area under curve (AUC) of the model with VFM and STE parameters was higher, which was more significant with the XGboost model.Conclusion: In this study, we developed a prediction model and nomogram to estimate the risk of MACE within 6 months of discharge among patients with heart failure. The results of this study can provide a reference for clinical physicians for detection of the risk of MACE in terms of clinical characteristics, cardiac structure and function, hemodynamics, and enable its prompt management, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis.