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ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1649618

This article is part of the Research TopicCommunity Series in Novel Biomarkers in Tumor Immunity and Immunotherapy: Volume IIIView all articles

Identification of Immunogenic Cell Death Signature Genes in Hepatocellular Carcinoma: From Single-cell Transcriptomics to In Vitro Mechanistic Validation and Comprehensive Prognostic Modeling with Hundreds of Machine Learning Algorithms

Provisionally accepted
  • 1Department of Hepatobiliary and pancreatic surgery&Retroperitoneal tumor surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
  • 2Department of Oncology, Qingdao Municipal Hospital Group, Qingdao, China

The final, formatted version of the article will be published soon.

Background: Hepatocellular carcinoma (HCC) lacks reliable prognostic biomarkers for immunotherapy. Immunogenic cell death (ICD) represents a promising therapeutic target, but its comprehensive characterization in HCC remains unexplored. Methods: We performed multi-omics integration of single-cell RNA sequencing data from 7 HCC samples (GSE112271, 44,461 cells) with bulk transcriptomics from three independent cohorts (TCGA-HCC [n=371], GSE14520 [n=242], ICGC [n=445]). ICD activity was quantified using ssGSEA. We identified HCC-specific ICD-related (HCC-ICDR) genes via WGCNA and optimized a prognostic model by benchmarking machine learning algorithms. Experimental validation included functional assays using CLIC1 and NAP1L1 overexpression in HepG2 cells. Results: The ICD-based risk score (ICDRS) demonstrated superior prognostic accuracy (C-index=0.839), validated across cohorts. Single-cell profiling revealed macrophages exhibited the highest ICD activity. High-risk patients displayed immunosuppressive microenvironments with enriched Tregs, M0 macrophages, and neutrophils, alongside hyperactivated DNA repair and MYC signaling. Low-risk patients showed anti-tumor immunity with increased CD8+ T cells and M1 macrophages. ICDRS predicted differential therapeutic vulnerabilities: low-risk patients showed enhanced sensitivity to standard immunotherapy-compatible treatments including sorafenib and doxorubicin, while high-risk patients demonstrated preferential sensitivity to EGFR-targeted therapies. Experimental validation confirmed CLIC1 and NAP1L1 significantly promoted HCC malignant behaviors. Conclusions: We established the comprehensive ICD-based prognostic framework for HCC, revealing novel tumor-immune interactions and therapeutic vulnerabilities. This model provides robust stratification for immunotherapy selection and advances precision medicine in HCC management. Future clinical translation includes prospective validation and development of companion diagnostics, offering potential pathways for personalized HCC treatment implementation.

Keywords: Immunogenic cell death, Hepatocellular Carcinoma, Multi-omics integration, precision medicine, Tumor Microenvironment, machine learning

Received: 18 Jun 2025; Accepted: 07 Oct 2025.

Copyright: © 2025 Liu, Sun, Wang, Zhou and Cha. 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: Lichao Cha, jnmustudent@163.com

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