Early screening, diagnosis and recurrence monitoring of hepatocellular carcinoma in patients with chronic hepatitis B based on serum N-glycomics analysis: A cohort study
Rui Su, Xuemei Tao, Lihua Yan, Yonggang Liu, Cuiying Chitty Chen, Ping Li, Jia Li, Jing Miao, Feng Liu, Wentao Kuai, Jiancun Hou, Mei Liu, Yuqiang Mi, Liang Xu
Abstract
BACKGROUND AND AIMS: HCC poses a significant global health burden, with 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-related cirrhosis. APPROACH AND RESULTS: This study enrolled a total of 397 patients with chronic hepatitis B-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, Hepatocellular Carcinoma Glycomics Random Forest model and Hepatocellular Carcinoma Glycomics Support Vector Machine model, 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 Hepatocellular Carcinoma Glycomics Random Forest model and Hepatocellular Carcinoma Glycomics Support Vector Machine model, respectively, outperforming AFP (0.687 [0.575-0.765]) and Protein Induced by Vitamin K Absence or Antagonist-II (PIVKA-II) (0.665 [0.507-0.823]). It also showed superiority in subgroup 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 patients with HCC 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.