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 ∼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 nonglycoproteins
on pooled mucinous (n = 10) and nonmucinous (n = 10) pancreatic cyst fluid to identify “proteins
of interest” to differentiate between mucinous cyst from nonmucinous
cyst and investigate these proteins as potential biomarker targets.
An automated multilectin 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 nonmucinous 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. Furthermore, chromosome gene mapping demonstrated
coexpressions and colocalization of some proteins of interest including
14-3-3 protein epsilon (YWHAE), pigment epithelium derived factor
(SERPINF1), and oncogene p53.