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.