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Clin Chem. 2008 Jul;54(7):1166-75. doi: 10.1373/clinchem.2007.087148. Epub 2008 May 16.

Breast cancer diagnosis and prognosis through quantitative measurements of serum glycan profiles.

Author information

1
National Center for Glycomics and Glycoproteomics, Department of Chemistry, Indiana University, Bloomington, IN 47405, USA.

Abstract

BACKGROUND:

Glycosylated proteins play important roles in cell-to-cell interactions, immunosurveillance, and a variety of receptor-mediated and specific protein functions through a highly complex repertoire of glycan structures. Aberrant glycosylation has been implicated in cancer for many years.

METHODS:

We performed specific MALDI mass spectrometry (MS)-based glycomic profile analyses of permethylated glycans in sera from breast cancer patients (12, stage I; 11, stage II; 9, stage III; and 50, stage IV) along with sera from 27 disease-free women. The serum glycoproteins were enzymatically deglycosylated, and the released glycans were purified and quantitatively permethylated before their MALDI-MS analyses. We applied various statistical analysis tools, including ANOVA and principal component analysis, to evaluate the MS profiles.

RESULTS:

Two statistical procedures implicated several sialylated and fucosylated N-glycan structures as highly probable biomarkers. Quantitative changes according to a cancer stage resulted when we categorized the glycans according to molecular size, number of oligomer branches, and abundance of sugar residues. Increases in sialylation and fucosylation of glycan structures appeared to be indicative of cancer progression. Different statistical evaluations confirmed independently that changes in the relative intensities of 8 N-glycans are characteristic of breast cancer (P < 0.001), whereas other glycan structures might contribute additionally to distinctions in the statistically recognizable patterns (different stages).

CONCLUSIONS:

MS-based N-glycomic profiling of serum-derived constituents appears promising as a highly sensitive and informative approach for staging the progression of cancer.

PMID:
18487288
DOI:
10.1373/clinchem.2007.087148
[Indexed for MEDLINE]
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