ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1663943
This article is part of the Research TopicCommunity Series in Novel Biomarkers for Predicting Response to Cancer Immunotherapy: Volume IIIView all 22 articles
Plasma exosomal lncRNA-related signatures define molecular subtypes and predict survival and treatment response in hepatocellular carcinoma
Provisionally accepted- Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
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Background Hepatocellular carcinoma (HCC) faces challenges in early diagnosis, prognosis, and treatment stratification due to molecular heterogeneity. This study aimed to establish a plasma exosomal long non-coding RNA (lncRNA)-based framework for molecular classification, prognostication, and therapeutic guidance in HCC. Methods The transcriptomic data from 230 plasma exosomes and 831 HCC tissues were integrated. A competitive endogenous RNA (ceRNA) network was constructed via the miRcode, miRTarBase, TargetScan, and miRDB databases to define exosome-related genes (ERGs). Unsupervised consensus clustering was used to stratify HCC patients on the basis of ERG profiles. Prognostic models were developed and optimized via 10 machine learning algorithms with 10-fold cross-validation. Treatment responses were predicted via the SubMap, TIDE, and oncoPredict algorithms. RT-qPCR experiments were conducted to validate the expression of model genes. Results We identified 22 dysregulated plasma exosomal lncRNAs in HCC. The upregulated lncRNAs formed a ceRNA network regulating 61 ERGs and were significantly enriched in cell cycle regulation, TGF-β signaling, the p53 pathway, and ferroptosis. ERG expression stratified HCC into three subtypes (C1–C3). The C3 subtype exhibited the poorest overall survival, advanced grade and stage, an immunosuppressive microenvironment (increased Treg infiltration, elevated PD-L1/CTLA4 expression, highest TIDE score), and hyperactivation of proliferation (MYC, E2F targets) and metabolic pathways (glycolysis, mTORC1). A random survival forest-derived 6-gene risk score (G6PD, KIF20A, NDRG1, ADH1C, RECQL4, MCM4) demonstrated high prognostic accuracy. High-risk patients presented increased TP53/TTN mutations and increased tumor mutational burdens. Risk model analysis predicted differential treatment responses: low-risk patients exhibited superior anti-PD-1 immunotherapy responses, whereas high-risk patients showed increased sensitivity to DNA-damaging agents (e.g., the Wee1 inhibitor MK-1775) and sorafenib. Experimental validation confirmed consistent dysregulation of the six-gene signature (G6PD, KIF20A, NDRG1, ADH1C, RECQL4, MCM4) in HCC cell lines, reinforcing the model's biological relevance. Conclusion Plasma exosomal lncRNAs enable robust molecular subtyping, accurate prognostic stratification, and treatment response prediction in HCC. The ERG-centric classification system and validated 6-gene risk model provide clinically actionable tools for precision oncology.
Keywords: Hepatocellular Carcinoma, Exosomal lncRNA, Molecular subtype, prognosis, treatment response
Received: 11 Jul 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 Zhong, Yao, Wang, Zihao, Huang, Liu, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jing Liu, lj8679@163.com
Xiaozhong Wang, wangxiaozhong@ncu.edu.cn
Lei Zhang, 1107375031@qq.com
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