Abstract
The lack of efficient biomarkers for the early detection of gastric cancer (GC) contributes to its high mortality rate, so it is crucial to discover novel diagnostic targets for GC. Recent studies have implicated the potential of site-specific glycans in cancer diagnosis, yet it is challenging to perform highly reproducible and sensitive glycoproteomics analysis on large cohorts of samples. Here, a highly robust N-glycoproteomics (HRN) platform comprising an automated enrichment method, a stable microflow LC-MS/MS system, and a sensitive glycopeptide-spectra-deciphering tool is developed for large-scale quantitative N-glycoproteome analysis. The HRN platform is applied to analyze serum N-glycoproteomes of 278 subjects from three cohorts to investigate glycosylation changes of GC. It identifies over 20 000 unique site-specific glycans from discovery and validation cohorts, and determines four site-specific glycans as biomarker candidates. One candidate has branched tetra-antennary structure capping with sialyl-Lewis antigen, and it significantly outperforms serum CEA with AUC values > 0.89 compared against < 0.67 for diagnosing early-stage GC. The four-marker panel can provide improved diagnostic performances. Besides, discrimination powers of four candidates are also testified with a verification cohort using PRM strategy. This findings highlight the value of this strong tool in analyzing aberrant site-specific glycans for cancer detection.