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CN 22-1108/R
Volume 41 Issue 8
Aug.  2025
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Article Navigation >  Journal of Clinical Hepatology  > 2025  >  41(8): 1597-1605

The application value of G-GADA model in the diagnosis of hepatitis B virus-related hepatocellular carcinoma

DOI: 10.12449/JCH250819
  • 1.

    Department of Preventive Medicine，Shihezi University School of Medicine，Shihezi，Xinjiang 832003，China

  • 2a.

    Department of Anatomy and Embryology School of Basic Medical Sciences Peking University Health Science Center Beijing 100191，China

  • 2b.

    Center for Microbiology and Infectious Diseases School of Basic Medical Sciences Peking University Health Science Center，Beijing 100191，China

  • 3.

    Department of Laboratory Medicine，Mengchao Hepatobiliary Hospital of Fujian Medical University，Fuzhou 350025，China

Research funding:

National Natural Science Foundation of China (82272433);

Open Project Funding from Fujian Provincial Key Laboratory of Liver Disease Drug Research (KFLX2022002)

More Information
  • Corresponding author: LIU Lijuan， ljliu@126.com （ORCID： 0000-0001-9809-0056）; ZHANG Mei， 13150400463@163.com （ORCID： 0000-0003-1811-042X）
  • Received Date: 2025-01-17
  • Accepted Date: 2025-04-21
  • Published Date: 2025-08-25
    Abstract
  •   Objective  To establish an optimized diagnostic model for hepatocellular carcinoma （HCC）， designated as G-GADA， in chronic hepatitis B （CHB） patients based on the parameters of age， sex， alpha-fetoprotein （AFP）， des-γ-carboxy prothrombin （DCP）， and Golgi protein 73 （GP73）， to address the problems of low sensitivity and specificity in the early diagnosis of hepatitis B virus （HBV）-related liver cancer， and to assess the value of this model in the diagnosis of HCC.  Methods  A retrospective analysis was performed for 201 CHB patients （CHB group）， 137 patients with HBV-related liver cirrhosis （LC group）， and 111 treatment-naïve patients with newly diagnosed HCC （HCC group） who were admitted to Mengchao Hepatobiliary Hospital of Fujian Medical University from June 2015 to June 2020. Serological markers （AFP， DCP， alpha-fetoprotein L3% ［AFP-L3%］， and GP73） were compared between groups and were analyzed in terms of their differences from the clinical and tumor characteristics of HCC patients， and the Spearman correlation analysis was used to assess the correlation between different markers. A Logistic regression analysis was used to establish a diagnostic model for liver cancer， and the receiver operating characteristic （ROC） curve was used to assess the diagnostic performance of each marker.  Results  Comparison of clinical features between CHB， LC， and HCC patients showed that HCC patients had significantly higher age， proportion of male patients， and serum levels of DCP， AFP， GP73， and AFP-L3% （all P<0.05）. In HCC patients， DCP levels are associated with tumor size and microvascular invasion； AFP levels are related to patient age， tumor size， tumor number， distant metastasis， and microvascular invasion； AFP-L3% levels are associated with patient age， tumor size， tumor number， distant metastasis， Milan staging， and microvascular invasion； GP73 levels are linked to tumor number， distant metastasis， and microvascular invasion（all P<0.05）. The correlation analysis of the serum markers showed a strong positive correlation between AFP and AFP-L3% （r=0.71，P<0.05） and a moderate positive correlation between AFP and GP73 （r=0.33，P<0.05） and between AFP-L3% and GP73 （r=0.41，P<0.05）. Based on the features of age， sex， DCP， AFP， and GP73， the multivariate Logistic regression analysis was used to establish a G-GADA diagnostic model for HCC， and for all patients， the G-GADA model had an area under the ROC curve （AUC） of 0.915 （95% confidence interval ［CI］：0.875‍ ‍—‍ ‍0.945） in the derivation cohort and 0.913 （95%CI：0.862‍ ‍—‍ ‍0.950） in the validation cohort for the diagnosis of HCC. In the AFP-negative patients， the G-GADA model achieved an AUC of 0.884 （95%CI：0.833‍ ‍—‍ ‍0.924） in the derivation cohort and 0.851 （95%CI：0.779‍ ‍—‍ ‍0.907） in the validation cohort， and in the patients with liver cirrhosis， the G-GADA model achieved an AUC of 0.901 （95%CI：0.841‍ ‍—‍ ‍0.944） in the derivation cohort and 0.885 （95%CI：0.806‍ ‍—‍ ‍0.940） in the validation cohort.  Conclusion  The G-GADA diagnostic model based on multiple variables significantly improves the detection rate of HCC， and demonstrates superior diagnostic performance in patients with low AFP expression and those with liver cirrhosis. The G-GADA model has a better clinical application value in the noninvasive diagnosis of HCC.

     

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