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Despite the widespread use of doxorubicin (DOX) as a chemotherapeutic agent, its severe cumulative cardiotoxicity represents a significant limitation. While the liposomal encapsulation of doxorubicin (Myocet, MYO) reduces cardiotoxicity, it is crucial to understand the molecular background of doxorubicin-induced cardiotoxicity. Here, we examined circular RNA expression in a translational model of pigs treated with either DOX or MYO and its potential impact on the global gene expression pattern in the myocardium. This study furthers our knowledge about the regulatory network of circRNA/miRNA/mRNA and its interaction with chemotherapeutics. Domestic pigs were treated with three cycles of anthracycline drugs (DOX, n = 5; MYO, n = 5) to induce cardiotoxicity. Untreated animals served as controls (control, n = 3). We applied a bulk mRNA-seq approach and the CIRIquant algorithm to identify circRNAs. The most differentially regulated circRNAs were validated under cell culture conditions, following forecasting of the circRNA–miRNA–mRNA network. We identified eight novel significantly regulated circRNAs from exonic and mitochondrial regions in the porcine myocardium. The forecasted circRNA–miRNA–mRNA network suggested candidate circRNAs that sponge miR-17, miR-15b, miR-130b, the let-7 family, and miR125, together with their mRNA targets. The identified circRNA–miRNA–mRNA network provides an updated, coherent view of the mechanisms involved in anthracycline-induced cardiotoxicity. [ABSTRACT FROM AUTHOR]

We have designed translational animal models to investigate cardiac profibrotic gene signatures. Domestic pigs were treated with cardiotoxic drugs (doxorubicin, DOX, n = 5 or Myocet®, MYO, n = 5) to induce replacement fibrosis via cardiotoxicity. Reactive interstitial fibrosis was triggered by LV pressure overload by artificial isthmus stenosis with stepwise developing myocardial hypertrophy and final fibrosis (Hyper, n = 3) or by LV volume overload in the adverse remodeled LV after myocardial infarction (RemoLV, n = 3). Sham interventions served as controls and healthy animals (Control, n = 3) served as a reference in sequencing study. Myocardial samples from the LV of each group were subjected to RNA sequencing. RNA-seq analysis revealed a clear distinction between the transcriptomes of myocardial fibrosis (MF) models. Cardiotoxic drugs activated the TNF-alpha and adrenergic signaling pathways. Pressure or volume overload led to the activation of FoxO pathway. Significant upregulation of pathway components enabled the identification of potential drug candidates used for the treatment of heart failure, such as ACE inhibitors, ARB, ß-blockers, statins and diuretics specific to the distinct MF models. We identified candidate drugs in the groups of channel blockers, thiostrepton that targets the FOXM1-regulated ACE conversion to ACE2, tyrosine kinases or peroxisome proliferator-activated receptor inhibitors. Our study identified different gene targets involved in the development of distinct preclinical MF protocols enabling tailoring expression signature-based approach for the treatment of MF. [ABSTRACT FROM AUTHOR]

In our prospective non-randomized, single-center cohort study (n = 161), we have evaluated a multimarker approach including S100 calcium binding protein A12 (S100A1), interleukin 1 like-receptor-4 (IL1R4), adrenomedullin, copeptin, neutrophil gelatinase-associated lipocalin (NGAL), soluble urokinase plasminogen activator receptor (suPAR), and ischemia modified albumin (IMA) in prediction of subsequent cardiac adverse events (AE) during 1-year follow-up in patients with coronary artery disease. The primary endpoint was to assess the combined discriminatory predictive value of the selected 7 biomarkers in prediction of AE (myocardial infarction, coronary revascularization, death, stroke, and hospitalization) by canonical discriminant function analysis. The main secondary endpoints were the levels of the 7 biomarkers in the groups with/without AE; comparison of the calculated discriminant score of the biomarkers with traditional logistic regression and C-statistics. The canonical correlation coefficient was 0.642, with a Wilk's lambda value of 0.78 and p < 0.001. By using the calculated discriminant equation with the weighted mean discriminant score (centroid), the sensitivity and specificity of our model were 79.4% and 74.3% in prediction of AE. These values were higher than that of the calculated C-statistics if traditional risk factors with/without biomarkers were used for AE prediction. In conclusion, canonical discriminant analysis of the multimarker approach is able to define the risk threshold at the individual patient level for personalized medicine. [ABSTRACT FROM AUTHOR]