Quantitative Site-Specific Glycoproteomics Reveals Glyco-Signatures for Breast Cancer Diagnosis

Intact glycopeptide characterization by mass spectrometry
has proven
to be a versatile tool for site-specific glycoproteomics analysis
and biomarker screening. Here, we present a method using a new model
of a Q-TOF instrument equipped with a Zeno trap for intact glycopeptide
identification and demonstrate its ability to analyze large-cohort
glycoproteomes. From 124 clinical serum samples of breast cancer,
noncancerous diseases, and nondisease controls, a total of 6901 unique
site-specific glycans on 807 glycosites of proteins were detected.
Much more differences of glycoproteome were observed in breast diseases
than the proteome. By employing machine learning, 15 site-specific
glycans were determined as potential glyco-signatures in detecting
breast cancer. The results demonstrate that our method provides a
powerful tool in glycoproteomic studies.