The statins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors) have been proven to be effective in lowering cholesterol and as anti-lipid agents against cardiovascular disease. Recent reports demonstrate an anticancer effect induced by the statins through inhibition of cell proliferation. Probably, these effects are due to suppression of the mevalo­nate pathway leading to the depletion of various downstream products that play an essential role in cell cycle progression, cell signaling and membrane integrity. To date, although many hypotheses have been proposed, the exact mechanism at the basis of cancer cell growth arrest induced by statins is not known. In this study, we have demonstrated that simvastatin, at a dose of 20 µM for 24‑72 h, induced in cancer cells but not in normal cells precise features of apoptosis including increased DNA fragmentation while, at the molecular level simvastatin induced overexpression of the pro-apoptotic gene Bax together with an inhibition of BCL-2, the gene that has the well-known function of protecting cells from apoptosis. The simvastatin-mediated induction of apoptosis in similar cancer cells but not in normal cells is very interesting and may be at the basis of cancer therapy using statins, usually in combination with chemotherapy or to be used as a cancer protective drug. Simvastatin may, thus, play a dual prophylactic role as a lipid-lowering drug for the prevention of heart disease and as an anticancer agent to prevent certain types of cancers.

Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.

In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular ‘omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular ‘omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular ‘omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.

In contrast to the widely accepted consensus of the existence of a single RNA polymerase in bacteria, several actinomycetes have been recently shown to possess two forms of RNA polymerases due the to co-existence of two rpoB paralogs in their genome. However, the biological significance of the rpoB duplication is obscure. In this study we have determined the genome sequence of the lipoglycopeptide antibiotic A40926 producer Nonomuraea gerenzanensis ATCC 39727, an actinomycete with a large genome and two rpoB genes, i.e. rpoB(S) (the wild-type gene) and rpoB(R) (the mutant-type gene). We next analyzed the transcriptional and metabolite profiles in the wild-type gene and in two derivative strains over-expressing either rpoB(R) or a mutated form of this gene to explore the physiological role and biotechnological potential of the “mutant-type” RNA polymerase. We show that rpoB(R) controls antibiotic production and a wide range of metabolic adaptive behaviors in response to environmental pH. This may give interesting perspectives also with regard to biotechnological applications.

Statins are a class of drugs that inhibit the rate-limiting step in the cholesterol biosynthetic pathway and show an anticancer effect, probably through the inhibition of cell proliferation. To date, the exact mechanism of cancer cell growth arrest induced by statins is not known. We report that simvastatin is able to induce apoptosis in melanoma cells but not in normal cells and also able to contrast the growth of tumor in an experimental melanoma murine model. We observed a delay in the tumor development in almost the 50% of the simvastatin administered animals and a strong reduction of the tumor volume with a differences of ~150% compared to the controls. Also the survival rate was significantly higher in mice that received the drug with a survival increase of ~130% compared to the controls. The tumor growth reduction in mice was supported by the results of cell migration assay, confirming that simvastatin clearly reduced cell migration. Moreover, simvastatin induced a strong downregulation of NonO gene expression, an important growth factor involved in the splicing regulation. This result could explain the decrease of melanoma cells proliferation, suggesting a possible action mechanism. The results derived from our experiments may sustain the many reports on the anticancer inhibitory property of statins and encourage new studies on this drug for a possible use in therapy, probably in combination with conventional chemotherapy.

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.