Early screening, diagnosis and recurrence monitoring of hepatocellular carcinoma in chronic hepatitis B patients based on serum N-glycomics analysis: A cohort study.

Abstract

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.