Analysis of N-linked Glycan Alterations in Tissue and Serum Reveals Promising Biomarkers for Intrahepatic Cholangiocarcinoma.

There is an urgent need for the identification of reliable prognostic biomarkers for patients with intrahepatic cholangiocarcinoma (iCCA) and alterations in N-glycosylation have demonstrated an immense potential to be used as diagnostic strategies for many cancers, including hepatocellular carcinoma (HCC). N-glycosylation is one of the most common post-translational modifications known to be altered based on the status of the cell. N-glycan structures on glycoproteins can be modified based on the addition or removal of specific N-glycan residues, some of which have been linked to liver diseases. However, little is known concerning the N-glycan alterations that are associated with iCCA. We characterized the N-glycan modifications quantitatively and qualitatively in three cohorts, consisting of two tissue cohorts: a discovery cohort (n = 104 cases) and a validation cohort (n = 75), and one independent serum cohort consisting of patients with iCCA, HCC, or benign chronic liver disease (n = 67). N-glycan analysis in situ was correlated to tumor regions annotated on histopathology and revealed that bisected fucosylated N-glycan structures were specific to iCCA tumor regions. These same N-glycan modifications were significantly upregulated in iCCA tissue and serum relative to HCC and bile duct disease, including primary sclerosing cholangitis (PSC) (P < 0.0001). N-glycan modifications identified in iCCA tissue and serum were used to generate an algorithm that could be used as a biomarker of iCCA. We demonstrate that this biomarker algorithm quadrupled the sensitivity (at 90% specificity) of iCCA detection as compared with carbohydrate antigen 19-9, the current "gold standard" biomarker of CCA. Significance: This work elucidates the N-glycan alterations that occur directly in iCCA tissue and utilizes this information to discover serum biomarkers that can be used for the noninvasive detection of iCCA.

Non-enzymatic glycoxidation linked with nutrition enhances the tumorigenic capacity of prostate cancer epithelia through AGE mediated activation of RAGE in cancer associated fibroblasts.

The molecular implications of food consumption on cancer etiology are poorly defined. The rate of nutrition associated non-enzymatic glycoxidation, a reaction that occurs between reactive carbonyl groups on linear sugars and nucleophilic amino, lysyl and arginyl groups on fats and proteins, is rapidly increased by food cooking and manufacturing processes. In this study, we assign nutrition-associated glycoxidation with significant oncogenic potential, promoting prostate tumor growth, progression, and metastasis in vivo. Advanced glycation end products (AGEs) are the final irreversible product of non-enzymatic glycoxidation. Exogenous treatment of prostate tumor cells with a single AGE peptide replicated glycoxidation induced tumor growth in vivo. Mechanistically, receptor for AGE (RAGE) deficiency in the stroma inhibited AGE mediated tumor growth. Functionally, AGE treatment induced RAGE dimerization in activated fibroblasts which sustained and increased the migratory potential of tumor epithelial cells. These data identify a novel nutrition associated pathway that can promote a tissue microenvironment conducive for aggressive tumor growth. Targeted and/or interventional strategies aimed at reducing AGE bioavailability as a consequence of nutrition may be viewed as novel chemoprevention initiatives.

Evaluation of species distribution model algorithms for fine-scale container-breeding mosquito risk prediction.

The present work evaluates the use of species distribution model (SDM) algorithms to classify high densities of small container-breeding Aedes mosquitoes (Diptera: Culicidae) on a fine scale in the Bermuda Islands. Weekly ovitrap data collected by the Department of Health, Bermuda for the years 2006 and 2007 were used for the models. The models evaluated included the algorithms Bioclim, Domain, GARP (genetic algorithm for rule-set prediction), logistic regression and MaxEnt (maximum entropy). Models were evaluated according to performance and robustness. The area under the receiver operating characteristic curve was used to evaluate each model's performance, and robustness was assessed according to the spatial correlation between classification risks for the two datasets. Relative to the other algorithms, logistic regression was the best and MaxEnt the second best model for classifying high-risk areas. We describe the importance of covariables for these two models and discuss the utility of SDMs in vector control efforts and the potential for the development of scripts that automate the task of creating risk assessment maps.