Characterization of glycoproteins in pancreatic cyst fluid using a high-performance multiple lectin affinity chromatography platform.

Currently, pancreatic cancer is the fourth cause of cancer death. In 2013, it is estimated that approximately 38,460 people will die of pancreatic cancer. Early detection of malignant cyst (pancreatic cancer precursor) is necessary to help prevent late diagnosis of the tumor. In this study, we characterized glycoproteins and non-glycoproteins on pooled mucinous (n=10) and non-mucinous (n=10) pancreatic cyst fluid to identify ‘proteins of interest’ to differentiate between mucinous cyst from non-mucinous cyst and investigate these proteins as potential biomarker targets. An automated multi-lectin affinity chromatography (M-LAC) platform was utilized for glycoprotein enrichment followed by nano-LC-MS/MS analysis. Spectral count quantitation allowed for the identification of proteins with significant differential levels in mucinous cysts from non-mucinous cysts of which one protein (periostin) was confirmed via immunoblotting. To exhaustively evaluate differentially expressed proteins, we used a number of proteomic tools including; gene ontology classification, pathway and network analysis, Novoseek data mining and chromosome gene mapping. Utilization of complementary proteomic tools, revealed that several of the proteins such as mucin 6 (MUC6), bile salt-activated lipase (CEL) and pyruvate kinase lysozyme M1/M2 with significant differential expression have strong association with pancreatic cancer. Further, chromosome gene mapping demonstrated co-expressions and co-localization of some proteins of interest including 14-3-3 protein epsilon (YWHAE), pigment epithelium derived factor (SERPINF1) and oncogene p53.