Background and Aims: Hepatocellular carcinoma (HCC) poses a significant global health burden, with hepatitis B virus (HBV) being the predominant etiology in China. However, current diagnostic markers lack the requisite sensitivity and specificity. This study aims to develop and validate serum N-glycomics-based models for the diagnosis and prognosis of HCC in patients with chronic hepatitis B (CHB)-related cirrhosis. Approach and Results: This study enrolled a total of 397 patients with CHB-related cirrhosis and HCC for clinical management. N-glycomics profiling was conducted on all participants and clinical data were collected. First, machine learning-based models, HCC-GRF and HCC-GSVM, were established for early screening and diagnosis of HCC using N-glycomics. The AUC values in the validation set were 0.967 (95% CI: 0.930-1.000) and 0.908 (0.840-0.976) for HCC-GRF and HCC-GSVM, respectively, outperforming AFP [0.687 (0.575-0.765)] and PIVKA-II [0.665 (0.507-0.823)]. It also showed superiority in subgroups analysis and external validation. Calibration and decision curve analysis also showed good predictive performance. Additionally, we developed a prognostic model, the prog-G model, based on N-glycans to monitor recurrence in HCC patients after curative treatment. During the follow-up period, it was observed that this model correlated with the clinical condition of the patients and could identify all recurrent HCC cases (n=12) prior to imaging findings, outperforming AFP (n=7) and PIVKA-II (n=9), while also detecting recurrent lesions earlier than imaging. Conclusions: N-glycomics models can effectively predict the occurrence and recurrence of HCC to improving the efficiency of clinical decision-making and promoting the precision treatment of HCC. Export

This study aims to investigate the prognostic significance of the combined GNR and GPR in patients with early-stage hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC). Retrospective analysis was performed on clinical data of 145 HBV-infected patients with early-stage HCC (ESHCC) who underwent radiofrequency ablation (RFA) and/or surgical resection. Patients were randomly allocated to training (n = 103) and validation (n = 42) cohorts for further survival analysis and model verification. The area under the receiver operating characteristic curves (ROC) was used for evaluating the diagnostic performance of ERM-model. And at the optimal cutoff, a survival analysis on all patients was conducted. Simultaneously, all patients were divided into different subgroups to evaluating ERM-Model. Multivariate Cox analysis identified GNR and GPR as independent risk factors for postoperative intrahepatic recurrence (IHR). The combined biomarkers panel demonstrated superior diagnostic accuracy (AUC = 0.812) compared to AFP (AUC = 0.773). Survival analysis showed a significantly higher 2-year cumulative non-IHR rate in the low-risk group (logit(P) < 0.398) verus the high-risk group (logit(P) ≥ 0.398, p < 0.001). Subgroup analysis of all patients that ERM-model showed differencesin HCC stage stratification, with higher diagnostic accuracy for BCLC stage A cases compared to BCLC stage 0 cases. GNR and GPR are valuable predictors of IHR in ESHCC. Clinicians can leverage this dual-marker model to assess risk and tailor personalized follow-up strategies.