Hypoxia-related and immune phenotype-related fusion model for non-invasive prognostication of hepatocellular carcinoma treated by TACE: a multicentre study

Yusheng Guo; Guilin Zhang; Xiaona Fu; Shanshan Jiang; Yi Li; Shanmei Li; Xiaofang Guo; Xiaolin Zhang; Chang Zhao; Rong Ding; Lei Yu; Xuegang Yang; Kai Zhao; Yuxin Sun; QiuPing Liu; YuDong Zhang; Xuhua Duan; Hui Zhao; Jiahua Zou; Bin Liang; Lian Yang; Chuansheng Zheng; Xuefeng Kan

doi:10.1136/gutjnl-2025-337938

Hypoxia-related and immune phenotype-related fusion model for non-invasive prognostication of hepatocellular carcinoma treated by TACE: a multicentre study

Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.

Authors

Yusheng Guo^#^{1

2

3}, Guilin Zhang^#^{1

2

3}, Xiaona Fu^#^{1

2

3}, Shanshan Jiang^#^{1

2

3}, Yi Li^{1

2

3}, Shanmei Li⁴, Xiaofang Guo⁴, Xiaolin Zhang⁵, Chang Zhao⁶, Rong Ding⁷, Lei Yu⁸, Xuegang Yang⁹, Kai Zhao¹⁰, Yuxin Sun^{1

2

3}, QiuPing Liu¹¹, YuDong Zhang¹¹, Xuhua Duan¹², Hui Zhao¹³, Jiahua Zou^{14

15}, Bin Liang^{1

2

3}, Lian Yang^{16

2

3}, Chuansheng Zheng^{16

2

3}, Xuefeng Kan^{16

2

3}

Affiliations

¹ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.
³ Hubei Key Laboratory of Molecular Imaging, Wuhan, China.
⁴ Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
⁵ Department of Interventional Radiology, The First College of Clinical Medical Science, Yichang Central People's Hospital, Three Gorges University, Yichang, Hubei, China.
⁶ Department of Interventional Radiology, Guangxi Medical University Cancer Hospital, Guangxi Medical University, Nanning, Guangxi, China.
⁷ Department of Minimally Invasive Intervention, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China.
⁸ Department of Interventional Radiology, Guangxi Zhuang Autonomous Region People's Hospital, Nanning, Guangxi, China.
⁹ Department of Interventional Therapy, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
¹⁰ Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin, China.
¹¹ Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
¹² Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
¹³ Department of Interventional Radiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
¹⁴ Department of Oncology, Huanggang Central Hospital of Yangtze University, Huanggang, Hubei, China.
¹⁵ Hubei Clinical Medical Research Center of Esophageal and Gastric Malignancy, Huanggang, Hubei, China.
¹⁶ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China xkliulang1314@163.com hqzcsxh@sina.com yanglian@hust.edu.cn.

^# Contributed equally.

PMID: 41856522
DOI: 10.1136/gutjnl-2025-337938

Abstract

Background: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.

Objective: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE therapy.

Design: This study enrolled 1448 HCC patients, including a TACE cohort (n=1349), a biomarker subset from a randomised trial (n=41), a single-cell RNA sequencing cohort and The Cancer Genome Atlas (TCGA) HCC cohort (n=50). Pre-treatment contrast-enhanced CT images were used to construct deep learning and conventional radiomic models. The early-fusion and late-fusion models (LFMs) were compared, and a clinical-radiologic model (CRM) was formed by integrating the better-performing LFM with clinical variables. Using TCGA data and single-cell transcriptomic profiles, the differences between high-score and low-score groups in tumour immune microenvironment, cellular functional states and key signalling pathways were investigated.

Results: The CRM effectively stratified patients' survival across multiple independent cohorts and achieved more granular risk stratification than the existing clinical models. Multi-omic analyses revealed that in the LFM high-score group, myelocytomatosis oncogene was activated, epithelial-mesenchymal transition enhanced, glycolysis upregulated and hypoxia pathway activated. Single-cell transcriptomic data confirmed that virtually all cell types in high-risk patients scored high in hypoxia, and cytotoxic T cells had a reduced cytotoxic activity.

Conclusion: The CRM model can non-invasively predict the prognosis of HCC patients treated by TACE therapy.

Keywords: AI (Artificial Intelligence); HEPATOCELLULAR CARCINOMA; INTERVENTIONAL RADIOLOGY; RADIOLOGY.