Evidence map›Paper›PMID 41220735›Full record

ArticleJournal of gastrointestinal oncology2025

Development and validation of a GP73-based predictive model for the diagnosis of early-stage hepatocellular carcinoma.

Shan Ji, Yumeng Liu, Yuhua Li, Jiaying Zhang, Tongzeng Li, Danlei Mou, Lianchun Liang, Yingmei Feng

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Article in Journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Shan Ji *Department of Infectious Diseases, Beijing You'an Hospital, Capital Medical University, Beijing, China.
Yumeng Liu *Department of Infectious Diseases, Beijing You'an Hospital, Capital Medical University, Beijing, China.ORCID https://orcid.org/0009-0002-6565-4163
Yuhua LiDepartment of Infectious Diseases, Beijing You'an Hospital, Capital Medical University, Beijing, China.
Jiaying ZhangDepartment of Infectious Diseases, Beijing You'an Hospital, Capital Medical University, Beijing, China.
Tongzeng LiFever Clinic, Beijing You'an Hospital, Capital Medical University, Beijing, China.
Danlei MouDepartment of Infectious Diseases, Beijing You'an Hospital, Capital Medical University, Beijing, China.
Lianchun LiangDepartment of Infectious Diseases, Beijing You'an Hospital, Capital Medical University, Beijing, China.
Yingmei FengLaboratory for Clinical Medicine, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Hepatocellular carcinoma (HCC) has become a major global public health concern due to its high incidence, high mortality, and frequent diagnosis at advanced stages. Currently available biomarkers, such as alpha-fetoprotein (AFP), have limited sensitivity and specificity for early-stage HCC, making early screening a significant challenge. Therefore, identifying novel and complementary biomarkers is essential to improve early detection rates and patient prognosis. Golgi protein-73 (GP73) is a secreted glycoprotein that is highly expressed in various malignancies and can be released into the bloodstream. Herein, we evaluated its potential value for early diagnosis of HCC. Methods: This retrospective cross-sectional study aimed to evaluate the diagnostic value of serum GP73 for early-stage HCC. A total of 401 patients were included in the study, comprising 63 patients with early-stage HCC and 338 patients with liver cirrhosis. Diagnoses of HCC and cirrhosis were confirmed based on a combination of imaging, pathological findings, and clinical guidelines. Serum levels of GP73 and AFP were measured using enzyme-linked immunosorbent assay (ELISA), and other clinical variables such as age and prothrombin time (PT) were obtained from medical records. Results: The area under the curve (AUC) for AFP and GP73 alone in diagnosing early-stage HCC was 0.769 [95% confidence interval (CI): 0.700-0.838] and 0.627 (95% CI: 0.539-0.716), respectively. After adjustment in multivariate analysis, GP73 remained an independent diagnostic factor (P<0.05). The developed nomogram achieved corrected C-indexes of 0.812 and 0.918 in the training and validation cohorts, respectively. Among patients infected with hepatitis B virus (HBV), the nomogram demonstrated AUCs of 0.836 in the training cohort and 0.900 in the validation cohort, indicating good discriminative ability. Baseline characteristics were comparable between the groups. Conclusions: The nomogram model proposed in this study, which integrates GP73, AFP, age, and PT, may serve as a simple, intuitive, and customizable clinical tool for identifying early-stage HCC among patients with liver cirrhosis. The model exhibits high diagnostic performance, especially in HBV-related populations, and shows promising potential for clinical application.

Indexed as

early diagnosishepatitis BHepatocellular carcinoma (HCC)nomogram model

Identifiers

PMID41220735
PMCPMC12598328

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.