The Epstein-Barr virus (EBV) infects more than 90% of the human population, playing a key role in the origin and progression of malignant and non-malignant diseases. Many attempts have been made to classify EBV according to clinical or epidemiological information; however, these classifications show frequent incongruences. For instance, they use a small subset of genes for sorting strains but fail to consider the enormous genomic variability and abundant recombinant regions present in the EBV genome. These could lead to diversity overestimation, alter the tree topology and misinterpret viral types when classified, therefore, a reliable EBV phylogenetic classification is needed to minimize recombination signals. Recombination events occur 2.5-times more often than mutation events, suggesting that recombination has a much stronger impact than mutation in EBV genomic diversity, detected within common ancestral node positions. The Hierarchical Bayesian Analysis of Population Structure (hierBAPS) resulted in the differentiation of 12 EBV populations showed seven monophyletic and five paraphyletic. The populations identified were related to geographic location, of which three populations (EBV-p1/Asia/GC, EBV-p2/Asia II/Tumors and EBV-p4/China/NPC) were related to tumor development. Therefore, we proposed a new consistent and non-simplistic EBV classification, beneficial in minimizing the recombination signal in the phylogeny reconstruction, investigating geography relationship and even infer associations to human diseases. These EBV classifications could also be useful in developing diagnostic applications or defining which strains need epidemiological surveillance.

Colorectal cancer is a heterogeneous disease caused by both genetic and epigenetics factors. Analysing DNA methylation changes occurring during colorectal cancer progression and metastasis formation is crucial for the identification of novel epigenetic markers of patient prognosis. Genome-wide methylation sequencing of paired samples of colon (normal adjacent, primary tumour and lymph node metastasis) showed global hypomethylation and CpG island (CGI) hypermethylation of primary tumours compared to normal. In metastasis we observed high global and non-CGI regions methylation, but lower CGI methylation, compared to primary tumours. Gene ontology analysis showed shared biological processes between hypermethylated CGIs in metastasis and primary tumours. After complementary analysis with The Cancer Genome Atlas (TCGA) cohort, FIGN, HTRA3, BDNF, HCN4 and STAC2 genes were found associated with poor survival. We mapped the methylation landscape of colon normal tissues, primary tumours and lymph node metastasis, being capable of identified methylation changes throughout the genome. Furthermore, we found five genes with potential for methylation biomarkers of poor prognosis in colorectal cancer patients.

Gallbladder cancer (GBC) is a highly fatal disease with poor prognosis and few therapeutic alternatives. The molecular mechanisms involved in the pathogenesis of GBC remain poorly understood. The vascular endothelial growth factor A (VEGF-A) is a potent proangiogenic agent involved in the carcinogenesis of many human tumors and is an attractive target for cancer therapy. We characterized VEGF-A expression in advanced GBC and its relation to clinicopathologic features. VEGF-A expression was examined by immunohistochemistry in tissue microarrays containing 224 advanced gallbladder carcinomas and 39 chronic cholecystitis. The cases were classified as low or high expression to evaluate the association of VEGF-A expression level with clinicopathologic variables. The Kaplan-Meier method was used to estimate survival as a function of time, and survival differences were analyzed by the log-rank test. High expression of VEGF-A was observed in 81% (183/224) of tumors and 5.1% (2/39) of chronic cholecystitis (P<0.0001). The VEGF-A expression had a significant relationship with histologic grade and TNM stage (P<0.05). Moreover, 5-year survival analysis indicated that high expression of VEGF-A is associated with a poor prognosis in patients with advanced GBC (P=0.0116). Our results indicate that VEGF-A is highly expressed in GBC and correlates with poor prognosis, suggesting that VEGF-A expression could be used as a biomarker for predicting malignant behavior and for identifying a subset of patients who may benefit from anti-VEGF-A therapies.