Li YL, Tung HD, Chuang TW, Cheng CT, Pang MG, Chen JJ, Zhong KM, Huang TY, Lee PL. Baseline and longitudinal biomarkers predict hepatocellular carcinoma in chronic hepatitis C: A cohort study. World J Gastrointest Oncol 2026; 18(5): 117512 [DOI: 10.4251/wjgo.v18.i5.117512]
Corresponding Author of This Article
Pei-Lun Lee, Chief Physician, Lecturer, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Hospital, Liouying, No. 201 Taikang, Tainan 736402, Taiwan. peilun57@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Cohort Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
May 15, 2026 (publication date) through May 14, 2026
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Journal Information of This Article
Publication Name
World Journal of Gastrointestinal Oncology
ISSN
1948-5204
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Li YL, Tung HD, Chuang TW, Cheng CT, Pang MG, Chen JJ, Zhong KM, Huang TY, Lee PL. Baseline and longitudinal biomarkers predict hepatocellular carcinoma in chronic hepatitis C: A cohort study. World J Gastrointest Oncol 2026; 18(5): 117512 [DOI: 10.4251/wjgo.v18.i5.117512]
Yi-Lin Li, Hung-Da Tung, Tang-Wei Chuang, Chun-Ta Cheng, Mai-Gio Pang, Jyh-Jou Chen, Kun-Ming Zhong, Ting-Yi Huang, Pei-Lun Lee, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Hospital, Liouying, Tainan 736402, Taiwan
Author contributions: Li YL, Tung HD, and Chen JJ contributed to the conceptualization and design of the study; Li YL, Tung HD, and Chuang TW conducted the literature analysis and drafted the initial manuscript; Li YL and Huang TY were responsible for data collection, database construction, and visualization (tables and figures); Cheng CT, Pang MG, Lee PL and Chen JJ contributed to data analysis and critical revision of the manuscript; Zhong KM assisted in drafting and revising the manuscript; Lee PL provided supervision, biostatistical analysis and final editing; all authors have read and approved the final manuscript.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of the Chi Mei Hospital, Liouying (Approval No. 11412-L02).
Informed consent statement: Waiver of informed consent for this retrospective analysis.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: The data are available from the corresponding author upon reasonable request.
Corresponding author: Pei-Lun Lee, Chief Physician, Lecturer, Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Hospital, Liouying, No. 201 Taikang, Tainan 736402, Taiwan. peilun57@gmail.com
Received: December 9, 2025 Revised: January 16, 2026 Accepted: February 10, 2026 Published online: May 15, 2026 Processing time: 156 Days and 11.4 Hours
Abstract
BACKGROUND
Hepatocellular carcinoma (HCC) remains a major complication in patients with chronic hepatitis C (CHC). Practical, low-cost tools that identify untreated patients at high cancer risk are needed for surveillance and treatment prioritization. Noninvasive biomarkers, including the fibrosis-4 (FIB-4) index and alpha-fetoprotein (AFP), have been identified as potential predictors of HCC development in patients with CHC. While most studies focus on baseline predictors, longitudinal changes in biomarkers has not been fully clarified.
AIM
To evaluate whether baseline and longitudinal trajectories of noninvasive biomarkers, particularly the FIB-4 index, predict subsequent HCC in treatment-naïve CHC.
METHODS
We performed a single-center retrospective cohort study of adults with CHC first evaluated between 1999 and 2015. Patients with other major liver diseases or prior HCC were excluded. Demographics, virology, labs and imaging were collected at baseline and year 3. FIB-4 categories were < 1.45, 1.45-3.25, and > 3.25; 3-year dynamics were assessed by category transition and absolute change (ΔFIB-4). Time-to-event analyses (Kaplan-Meier, Cox models) estimated associations with incident HCC.
RESULTS
Over a median 9.4-year follow-up, 29.9% of patients developed HCC. Baseline FIB-4 > 3.25 significantly predicted HCC [adjusted hazard ratio (aHR) = 2.72; P < 0.001]. At year 3, AFP ≥ 20 ng/mL was independent predictor (aHR = 6.74; P < 0.001). Notably, longitudinal analysis demonstrated that a 3-year absolute increase in FIB-4 (ΔFIB-4 ≥ 1.0) was an independent risk factor (aHR = 2.67; P < 0.001). Stratification by trajectory revealed that the increase group (ΔFIB-4 ≥ +1.0) exhibited the highest 15-year cumulative HCC incidence of 72.7%.
CONCLUSION
High baseline and rising FIB-4 scores strongly predict HCC. Integrating longitudinal FIB-4 monitoring with AFP assessment is essential for risk stratification and prognosis in CHC.
Core Tip: This retrospective cohort study evaluated 271 treatment-naïve patients with chronic hepatitis C to determine whether baseline and 3-year trajectories of the fibrosis-4 (FIB-4) index and other biomarkers predict long-term hepatocellular carcinoma (HCC) risk. A baseline FIB-4 value > 3.25 strongly predicted HCC independently, while a 3-year ΔFIB-4 ≥ 1 helped risk stratification. Persistently low FIB-4 values identified a group with negligible HCC incidence. Periodic, low-cost monitoring of FIB-4 and alpha-fetoprotein may offer a practical approach to guide surveillance and treatment prioritization in resource-limited settings.
