Most of the colorectal cancer (CRC) cases are sporadic, only 25% of the patients have a family history of the disease, and major genes causing syndromes predisposing to CRC only account for 5-6% of the total cases. The following subtypes can be recognized: MIN (microsatellite instability), CIN (chromosomal instability), and CIMP (CpG island methylator phenotype). CIN occurs in 80–85% of CRC. Chromosomal instability proceeds through two major mechanisms, missegregation that results in aneuploidy through the gain or loss of whole chromosomes, and unbalanced structural rearrangements that lead to the loss and/or gain of chromosomal regions. The loss of heterozygosity that occur in the first phases of the CRC cancerogenesis (in particular for the genes on 18q) as well as the alteration of methylation pattern of multiple key genes can drive the development of colorectal cancer by facilitating the acquisition of multiple tumor-associated mutations and the instability phenotype.

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Colorectal cancer (CRC) is one of the most common cancer worldwide and results from the accumulation of mutations and epimutations in colonic mucosa cells ultimately leading to cell proliferation and metastasis. Unfortunately, CRC prognosis is still poor and the search of novel diagnostic and prognostic biomarkers is highly desired to prevent CRC-related deaths. The present article aims to summarize the most recent findings concerning the use of either genetic or epigenetic (mainly related to DNA methylation) biomarkers for CRC diagnosis, prognosis, and response to treatment. Recent large-scale DNA methylation studies suggest that CRC can be divided into several subtypes according to the frequency of DNA methylation and those of mutations in key CRC genes, and that this is reflected by different prognostic outcomes. Increasing evidence suggests that the analysis of DNA methylation in blood or fecal specimens could represent a valuable non-invasive diagnostic tool for CRC. Moreover, a broad spectrum of studies indicates that the inter-individual response to chemotherapeutic treatments depends on both epigenetic modifications and genetic mutations occurring in colorectal cancer cells, thereby opening the way for a personalized medicine. Overall, combining genetic and epigenetic data might represent the most promising tool for a proper diagnostic, prognostic and therapeutic approach.

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Folate metabolism plays an essential role in DNA synthesis and methylation processes. Deviations in the flux of folate due to genetic variation could result in selective growth and genomic instability and affect susceptibility to various cancers including lymphoma. To test this hypothesis, genetic polymorphisms in the folate metabolic pathway were investigated using DNA from a population-based case-control study of nonHodgkin lymphoma (

Most cases of the segmental progeroid syndrome, Hutchinson -Gilford progeria syndrome (HGPS), are caused by a de novo dominant mutation within a single codon of the LMNA gene. This mutation leads to the increased usage of an internal splice site that generates an alternative lamin A transcript with an internal deletion of 150 nucleotides, called lamin AD150. The LMNA gene encodes two major proteins of the inner nuclear lamina, lamins A and C, but not much is known about their expression levels. Determination of the overall expression levels of the LMNA gene transcripts is an important step to further the understanding of the HGPS. In this study, we have performed absolute quantification of the lamins A, C and AD150 transcripts in primary dermal fibroblasts from HGPS patients and unaffected age-matched and parent controls. We show that the lamin AD150 transcript is present in unaffected controls but its expression is 4160-fold lower than that in samples from HGPS patients. Analysis of transcript expression during in vitro aging shows that although the levels of lamin A and lamin C transcripts remain unchanged, the lamin AD150 transcript increases in late passage cells from HGPS patients and parental controls. This study provides a new method for LMNA transcript analysis and insights into the expression of the LMNA gene in HGPS and normal cells.