Hyung Seok Kim, Jung Hwan Yoon, Ji Yi Choi, Moon Gyeong Yoon, Geum Ok Baek, Minji Kang, Se Ha Jang, Won Park, Yunjin Go, Jestlin Tianthing Ng, Suk Woo Nam, Jee-Yeong Jeong, Ji Eun Han, Hyo Jung Cho, Su Bin Lim, Soon Sun Kim, Jae Youn Cheong, Jung Woo Eun
Clin Mol Hepatol 2025;31(3):914-934. Published online February 6, 2025
Background/Aims Hepatocellular carcinoma (HCC) is characterized by high recurrence and mortality, necessitating the identification of reliable biomarkers. In this study, we aimed to identify the predictive gene signatures for HCC recurrence and evaluate the efficiency of GULP PTB domain-containing engulfment adaptor 1 (GULP1) as a predictive and diagnostic marker and therapeutic target for HCC.
Methods We analyzed genomic datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases via least absolute shrinkage and selection operator Cox regression and 10-fold cross-validation, leading to the development of a 15-gene risk score model, which was validated using three independent datasets. Serum GULP1 and α-fetoprotein levels were assessed to determine the diagnostic accuracy of the model. Using clinical cohorts and patient sera, GULP1 roles were examined, and functional assays in vitro and in vivo were used to evaluate its effects on cell growth, epithelial–mesenchymal transition (EMT), ADP-ribosylation factor 6 (ARF6) activation, and β-catenin signaling.
Results Our newly developed risk-score model accurately predicted recurrent HCC in all datasets. Among the 15 genes in the risk score model, GULP1 was overexpressed in patients with HCC and independently predicted HCC recurrence. Its expression modulation influenced cell growth and EMT, with observed effects on ARF6 activation and β-catenin signaling pathways.
Conclusions GULP1 is a crucial biomarker for HCC, serving as a non-invasive diagnostic and predictive tool. It also plays key roles in HCC progression. Our findings highlight the potential use of GULP1 in treatment strategies targeting EMT and HCC recurrence to improve the personalized care and patient outcomes.
Citations
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Correspondence to letter to the editor on “GULP1 as a novel diagnostic and predictive biomarker in hepatocellular carcinoma” Hyung Seok Kim, Soon Sun Kim, Jae Youn Cheong, Jung Woo Eun Clinical and Molecular Hepatology.2026; 32(1): e103. CrossRef
GULP1, a multifaceted diagnostic biomarker and potential therapeutic target in hepatocellular carcinoma: Editorial on “GULP1 as a novel diagnostic and predictive biomarker in hepatocellular carcinoma” Yuhao Xie, Lu-Qi Cao, John Wurpel, Zhe-Sheng Chen Clinical and Molecular Hepatology.2026; 32(1): 413. CrossRef
Letter to the editor on “GULP1 as a novel diagnostic and predictive biomarker in hepatocellular carcinoma” Juan Yang, Xinyi Li, Sheng Zheng Clinical and Molecular Hepatology.2026; 32(1): e10. CrossRef
GULP1: New hope for hepatocellular carcinoma: Reply to correspondence on “GULP1 as a novel diagnostic and predictive biomarker in hepatocellular carcinoma” Yuhao Xie, Lu-Qi Cao, John Wurpel, Zhe-Sheng Chen Clinical and Molecular Hepatology.2026; 32(1): e112. CrossRef
Correspondence to editorial on “GULP1 as a novel diagnostic and predictive biomarker in hepatocellular carcinoma” Soon Sun Kim, Hyung Seok Kim, Jae Youn Cheong, Jung Woo Eun Clinical and Molecular Hepatology.2026; 32(1): e72. CrossRef
Unveiling GULP1 as a hepatocyte-specific role for recurrence: Editorial on “GULP1 as a novel diagnostic and predictive biomarker in hepatocellular carcinoma” Pengde Lu, Ning Wang Clinical and Molecular Hepatology.2026; 32(1): 410. CrossRef
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Background/Aims Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers.
Methods We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses.
Results Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types.
Conclusions Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.
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