Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies.

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease.

Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.

Polycomb YY1 is a critical interface between epigenetic code and miRNA machinery after exposure to hypoxia in malignancy.

Yin Yang 1 (YY1) is a member of polycomb protein family involved in epigenetic modifications and transcriptional controls. We have shown that YY1 acts as positive regulator of tumor growth and angiogenesis by interfering with the VEGFA network. Yet, the link between polycomb chromatin complex and hypoxia regulation of VEGFA is still poorly understood. Here, we establish that hypoxia impairs YY1 binding to VEGFA mRNA 3'UTR (p<0.001) in bone malignancy. Moreover, RNA immunoprecipitation reveals the formation of triplex nuclear complexes among YY1, VEGFA DNA, mRNA, and unreached about 200 fold primiRNA 200b and 200c via Dicer protein. In this complex, YY1 is necessary to maintain the steady-state level of VEGFA expression while its silencing increases VEGFA mRNA half-life at 4 h and impairs the maturation of miRNA 200b/c. Hypoxia promotes histone modification through ubiquitination both of YY1 and Dicer proteins. Hypoxia-mediated down-regulation of YY1 and Dicer changes post-transcriptional VEGFA regulation by resulting in the accumulation of primiRNA200b/c in comparison to mature miRNAs (p<0.001). Given the regulatory functions of VEGFA on cellular activities to promote neoangiogenesis, we conclude that YY1 acts as novel critical interface between epigenetic code and miRNAs machinery under chronic hypoxia in malignancy.

Osteosarcoma cells induce endothelial cell proliferation during neo-angiogenesis.

Understanding the mechanisms inducing endothelial cell (EC) proliferation following tumor microenvironment stimuli may be important for the development of antiangiogenic therapies. Here, we show that cyclin-dependent kinase 2 and 5 (Cdk2, Cdk5) are important mediators of neoangiogenesis in in vitro and in vivo systems. Furthermore, we demonstrate that a specific Yin Yang 1 (YY1) protein-dependent signal from osteosarcoma (SaOS) cells determines proliferation of human aortic endothelial cells (HAECs). Following tumor cell stimuli, HAECs overexpress Cdk2 and Cdk5, display increased Cdk2 activity, undergo enhanced proliferation, and form capillary-like structures. Moreover, Roscovitine, an inhibitor of Cdks, blunted overexpression of Cdk2 and Cdk5 and Cdk2 activity induced by the YY1-dependent signal secreted by SaOS cells. Furthermore, Roscovitine decreased HAEC proliferation and angiogenesis (the latter by 70% in in vitro and 50% in in vivo systems; P < 0.01 vs. control). Finally, the finding that Roscovitine triggers apoptosis in SaOS cells as well as in HAECs by activating caspase-3/7 indicates multiple mechanisms for the potential antitumoral effect of Roscovitine. Present work suggests that Cdk2 and Cdk5 might be pharmacologically accessible targets for both antiangiogenic and antitumor therapy.

Different expression of CD146 in human normal and osteosarcoma cell lines.

The CD146 cell membrane adhesion molecule is highly expressed on the cell surface of several tumours. The level of its expression has been found to correlate directly with tumour progression and metastatic potential, thus establishing CD146 as an important candidate of tumour growth and metastasis. In order to characterize its expression in human osteosarcoma (OS) cell lines, we have examined the CD146 expression at protein and RNA levels in both normal and tumour osteoblast-like cell lines by several methods. Our results indicate that CD146 protein is expressed at low levels in normal osteoblast cells whereas it is highly expressed in all OS cell lines analysed, (SaOS, MG-63, U-2OS). Moreover, CD146 overexpression was partially reduced in shYY1 cells, where the Yin Yang 1 transcription factor, also found over-expressed in human OS cells, has been silenced.

YY1 overexpression is associated with poor prognosis and metastasis-free survival in patients suffering osteosarcoma.

BACKGROUND: The polycomb transcription factor Yin Yang 1 (YY1) overexpression can be causally implicated in experimental tumor growth and metastasization. To date, there is no clinical evidence of YY1 involvement in outcome of patients with osteosarcoma. Prognosis of osteosarcoma is still severe and only few patients survive beyond five years. We performed a prospective immunohistochemistry analysis to correlate YY1 immunostaining with metastatic development and survival in a selected homogeneous group of patients with osteosarcoma. METHODS: We studied 41 patients suffering from osteosarcoma (stage II-IVa). Multivariate analysis was performed using Cox proportional hazard regression to evaluate the correlation between YY1 expression and both metastasis development and mortality. RESULTS: YY1 protein is not usually present in normal bone; in contrast, a high number of patients (61%) showed a high score of YY1 positive cells (51-100%) and 39% had a low score (10-50% positive cells). No statistical difference was found in histology, anatomic sites, or response to chemotherapy between the two degrees of YY1 expression. Cox regression analysis demonstrated that the highest score of YY1 expression was predictive of both low metastasis-free survival (HR = 4.690, 95%CI = 1.079-20.396; p = 0.039) and poor overall survival (HR = 8.353, 95%CI = 1.863-37.451 p = 0.006) regardless of the effects of covariates such as age, gender, histology and chemonecrosis. CONCLUSION: Overexpression of YY1 in primary site of osteosarcoma is associated with the occurrence of metastasis and poor clinical outcome.