title: Glycoprotein microarrays with multi-lectin detection: unique lectin binding patterns as a tool for classifying normal, chronic pancreatitis and pancreatic cancer sera.
abstract: Pancreatic cancer is the fourth leading cause of cancer-related death in the United States, with a 5-year survival rate of less than 4%. Effective early detection and screening are currently not available, and tumors are typically diagnosed at a late stage, frequently after metastasis. Existing clinical markers of pancreatic cancer lack specificity, as they are also found in inflammatory diseases of the pancreas and biliary tract. In the work described here, naturally occurring glycoproteins were enriched by using lectin affinity chromatography and then further resolved by nonporous reversed-phase chromatography. Glycoprotein microarrays were then printed and probed with a variety of lectins to screen glycosylation patterns in sera from normal, chronic pancreatitis, and pancreatic cancer patients. Ten normal, 8 chronic pancreatitis, and 6 pancreatic cancer sera were investigated. Data from the glycoprotein microarrays were analyzed using bioinformatics approaches including principal component analysis (PCA) and hierarchical clustering (HC). Both normal and chronic pancreatitis sera were found to cluster close together, although in two distinct groups, whereas pancreatic cancer sera were significantly different from the other two groups. Both sialylation and fucosylation increased as a function of cancer on several proteins including Hemopexin, Kininogen-1, Antithrombin-III, and Haptoglobin-related protein, whereas decreased sialylation was detected on plasma protease C1 inhibitor. Target alterations on glycosylations were verified by lectin blotting experiments and peptide mapping experiments using microLC-ESI-TOF. These altered glycan structures may have utility for the differential diagnosis of pancreatic cancer and chronic pancreatitis and identify critical differences between biological samples from patients with different clinical conditions.
authors.affiliation: Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA.
authors.forename: Jia
authors.initials: J
authors.lastname: Zhao
authors.forename: Tasneem H
authors.initials: TH
authors.lastname: Patwa
authors.forename: Weilian
authors.initials: W
authors.lastname: Qiu
authors.forename: Kerby
authors.initials: K
authors.lastname: Shedden
authors.forename: Robert
authors.initials: R
authors.lastname: Hinderer
authors.forename: David E
authors.initials: DE
authors.lastname: Misek
authors.forename: Michelle A
authors.initials: MA
authors.lastname: Anderson
authors.forename: Diane M
authors.initials: DM
authors.lastname: Simeone
authors.forename: David M
authors.initials: DM
authors.lastname: Lubman
mesh_terms: D002236:Carbohydrate Conformation; D002240:Carbohydrate Sequence; D019295:Computational Biology; D006023:Glycoproteins / Q000737:chemistry*; D006801:Humans; D037102:Lectins / Q000032:analysis*; D008969:Molecular Sequence Data; D010190:Pancreatic Neoplasms / Q000097:blood* / Q000737:chemistry / Q000473:pathology; D010195:Pancreatitis / Q000097:blood*; D040081:Protein Array Analysis; D011485:Protein Binding
chemical_list: D006023:Glycoproteins; D037102:Lectins