Accumulating evidence suggests that metformin, used as an antidiabetic drug, possesses anticancer properties. Metformin reduced the incidence and growth of experimental tumors in vivo. In a randomized clinical trial among nondiabetic patients, metformin treatment significantly decreased the number of aberrant crypt foci compared to the untreated group with a follow-up of 1 month. In our study, HT29 cells were treated with graded concentrations of metformin, 10 mM/25 mM/50 mM, for 24/48 hours. We performed immunofluorescence experiments by means of confocal microscopy and Western blot analysis to evaluate a panel of factors involved in apoptotic/autophagic processes and oxidative stress response. Moreover, HT29 cells treated with metformin were analyzed by flow cytometry assay to detect the cell apoptosis rate. The results demonstrate that metformin exerts growth inhibitory effects on cultured HT29 cells by increasing both apoptosis and autophagy; moreover, it affects the survival of cultured cells inhibiting the transcriptional activation of nuclear factor E2–related factor 2 (NRF-2) and nuclear factor–kappa B (NF-κB). The effects of metformin on HT29 cells were dose- and time-dependent. These results are very intriguing, since metformin is emerging as a multifaceted drug: it has a good safety profile and is associated with low cost, and it might be a promising candidate for the prevention or treatment of colorectal cancer.

(1) Background: the NGS based mutational study of hereditary cancer genes is crucial to design tailored prevention strategies in subjects with different hereditary cancer risk. The ease of amplicon-based NGS library construction protocols contrasts with the greater uniformity of enrichment provided by capture-based protocols and so with greater chances for detecting larger genomic rearrangements and copy-number variations. Capture-based protocols, however, are characterized by a higher level of complexity of sample handling, extremely susceptible to human bias. Robotics platforms may definitely help dealing with these limits, reducing hands-on time, limiting random errors and guaranteeing process standardization. (2) Methods: We implemented and validated the complete automation of the SOPHiA GENETICS’ CE-IVD Hereditary Cancer Solution™ (HCS) libraries preparation workflow on the Hamilton’s STARlet platform. (3) Results: We demonstrate that this automated workflow, used for more than 1000 samples achieved the same performances of manual setup in terms of coverages and reads uniformity, with extremely lower variability of reads mapping rate onto the regions of interest. (4) Conclusions: This automated solution offers same reliable and affordable NGS data, but with the essential advantages of a flexible, automated and integrated framework, minimizing possible human errors and depicting a laboratory’s walk-away scenario.