Quantitative serum glycomics of esophageal adenocarcinoma and other esophageal disease onsets

J Proteome Res. 2009 Jun;8(6):2656-66. doi: 10.1021/pr8008385.

Abstract

Aberrant glycosylation has been implicated in various types of cancers and changes in glycosylation may be associated with signaling pathways during malignant transformation. Glycomic profiling of blood serum, in which cancer cell proteins or their fragments with altered glycosylation patterns are shed, could reveal the altered glycosylation. We performed glycomic profiling of serum from patients with no known disease (N = 18), patients with high grade dysplasia (HGD, N = 11) and Barrett's esophagus (N = 5), and patients with esophageal adenocarcinoma (EAC, N = 50) in an attempt to delineate distinct differences in glycosylation between these groups. The relative intensities of 98 features were significantly different among the disease onsets; 26 of these correspond to known glycan structures. The changes in the relative intensities of three of the known glycan structures predicted esophageal adenocarcinoma with 94% sensitivity and better than 60% specificity as determined by receiver operating characteristic (ROC) analysis. We have demonstrated that comparative glycomic profiling of EAC reveals a subset of glycans that can be selected as candidate biomarkers. These markers can differentiate disease-free from HGD, disease-free from EAC, and HGD from EAC. The clinical utility of these glycan biomarkers requires further validation.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenocarcinoma / blood*
  • Adenocarcinoma / metabolism
  • Area Under Curve
  • Biomarkers, Tumor / blood*
  • Esophageal Diseases / blood*
  • Esophageal Diseases / metabolism
  • Esophageal Neoplasms / blood*
  • Esophageal Neoplasms / metabolism
  • Glycomics / methods*
  • Glycoproteins / blood*
  • Glycoproteins / metabolism
  • Humans
  • Polysaccharides / analysis
  • Polysaccharides / metabolism
  • Principal Component Analysis
  • ROC Curve
  • Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
  • Statistics, Nonparametric

Substances

  • Biomarkers, Tumor
  • Glycoproteins
  • Polysaccharides