ABSTRACT: Hepatocellular carcinoma (HCC) ranks among the foremost causes of cancer-related deaths globally, yet the contribution of N-glycosylation to its pathogenesis has remained elusive. In this study, we employed an innovative multicenter, large-cohort glycomics approach, integrating isomer-specific glycan profiling and multiomics data, to unravel the molecular mechanisms driving HCC and to harness serum N-glycome signatures for advanced diagnostic applications. We analyzed serum samples from 1,074 individuals—164 healthy controls, 223 with chronic hepatitis B, 218 with liver cirrhosis, and 469 with HCC—using a high-throughput, reproducible pipeline involving N-glycan release, derivatization, enrichment, and MALDI-TOF mass spectrometry. Our findings reveal striking N-glycosylation signatures tied to liver disease progression, notably increased glycan branching, and to hepatic dysfunction, evidenced by elevated bisecting N-acetylglucosamine and fucosylation with diminished galactosylation. Leveraging these serum N-glycan profiles, we developed machine learning models achieving exceptional diagnostic accuracy for HCC, with areas under the receiver operating characteristic curve (ROC-AUC) ranging from 0.84 to 0.93 in an internal validation cohort—outperforming alpha-fetoprotein (DeLong’s test, P < 0.05). Robustness of these models was confirmed through validation in two independent external cohorts, underscoring their potential as a transformative tool for HCC diagnosis. Moreover, by integrating glycoproteomic and transcriptomic analyses, we elucidated the regulatory and functional pathways orchestrating these glycan shifts.
Haojie Lu 
