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Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. Results: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). Conclusions: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.

A great deal of evidences indicate that impaired fetal growth and in utero exposure to risk factors, especially maternal hypercholesterolemia, may be relevant for human pathophysiological signs of atherosclerosis and subsequent development of cardiovascular disease (CVD) during different life stages. Despite the underlying mechanisms of fetal programming are still unknown, epigenetics has been suggested as one of the possible explanations for the associations between intrauterine risk factors and CVD development. Indeed, a lot of translational studies support the hypothesis that epigenetic changes are related to increased CVD risk although it is still not possible to establish a direct causality in humans. Notably, epigenetic modifications can be reversible through therapeutic approaches employing histone deacetylase inhibitors, histone acetyltransferase inhibitors and commonly used drugs like statins. Thus, the whole comprehension of these mechanisms will provide in the next future the rationale for the development of novel tools to be used in the primary prevention and therapy of CVD.