Optimized Simultaneous N/O-Glycopeptide Enrichment Strategy Identifies Lung Cancer Diagnostic Biomarkers in Serum.

Glycoproteins, degradable into glycopeptides, are crucial in lung cancer (LC), yet efficient methods to enrich them from LC serum for biomarker discovery remain lacking. We mixed microcrystalline cellulose with cationic and anionic fillers was packed into the DeepGP column. Comparing DeepGP, MAX, HILIC, and PBA in HeLa cells showed DeepGP's superior glycopeptide coverage, which was then applied to profile N/O-glycoproteins in 18 serum samples, with biomarker identification via MetaboAnalyst and N-glycan analysis using GlycanFinder. A total of 4801 glycopeptides (4021 N- and 780 O-glycopeptides) were quantified using MAX, HILIC, DeepGP, and PBA, with DeepGP identifying the most O-glycopeptides in HeLa cells. Applying DeepGP to LC serum enabled quantification of 10,482 N- and 11,110 O-glycopeptides. Proteomic analysis highlighted changes in transcription, post-translational modifications, protein turnover, and chaperone functions, with pathway enrichment revealing complement/coagulation cascades, cholesterol metabolism, and cancer-related proteoglycan signaling. MetaboAnalyst identified four site-specific glycopeptides (AACT-N106-H7N6S4F1, HEMO-N187-N4H5F1S1, ITIH3-N580-N5H6S3, IGG1-N180-N5H3F1) as potential biomarkers with AUC > 0.88. Thus, this establishes a refined strategy of DeepGP to simultaneously enrich N/O glycopeptides, enhancing the potential of glycopeptides as diagnostic and prognostic biomarkers for LC in clinical settings in the future.