Glycoprotein Microarrays with Multi-Lectin Detection: Unique Lectin
Binding Patterns as a Tool for Classifying Normal, Chronic
Pancreatitis and Pancreatic Cancer Sera
posted on 2020-04-02, 14:36authored byJia Zhao, Tasneem H. Patwa, Weilian Qiu, Kerby Shedden, Robert Hinderer, David E. Misek, Michelle A. Anderson, Diane M. Simeone, David M. Lubman
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 μLC−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.
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