Open Access

ARTICLE

Immunogenic Cell Death Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Bi Feng^#, Siqi Yang^#, Zhiqiang He, Yushi Dai, Ruiqi Zou, Yafei Hu, Haijie Hu^*, Fuyu Li^*

Division of Biliary Tract Surgery, Department of General Surgery, West China Hospital of Sichuan University, Chengdu, 610041, China

* Corresponding Authors: Haijie Hu. Email: ; Fuyu Li. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Novel Biomarkers and Treatment Strategies in Solid Tumor Diagnosis, Progression, and Prognosis)

Oncology Research 2025, 33(9), 2353-2377. https://doi.org/10.32604/or.2025.061422

Received 24 November 2024; Accepted 19 June 2025; Issue published 28 August 2025

Abstract

Objectives: Hepatocellular carcinoma (HCC) is among the most frequently occurring malignant tumors of the digestive tract and is associated with an increased mortality rate worldwide. This study aimed to develop and validate a prognostic model based on immunogenic cell death (ICD)-related genes to predict patient survival and guide individualized treatment strategies for HCC. Methods: ICD-related genes were identified from the GeneCards database using a relevance score threshold of >10. A combination of least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis was used to screen prognostic genes and construct a risk score model. Immune cell infiltration was evaluated through single-sample gene set enrichment analysis (ssGSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithms. Associations between risk groups and the tumor microenvironment (TME), N6-methyladenosine (m6A) regulators, and immune checkpoint expression were analyzed. Drug sensitivity was predicted based on the risk stratification. The reliability of the model was validated in internal cohorts and further confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Results: A six-gene signature (CFHR3, G6PD, IGHM, KPNA2, PON1, and SERPINE1) was identified and used to calculate the risk scores. This study found that high-risk patients exhibited significantly poorer overall survival in both the training and validation datasets. The nomogram integrating the risk score and clinical factors showed strong predictive performance. High-risk patients demonstrated reduced immune cell infiltration, altered expression of immune checkpoints and immunosuppressive factors, and a distinct m6A modification pattern, suggesting a higher likelihood of immune escape. This study also revealed that the risk model effectively predicted sensitivity to multiple anticancer drugs. Conclusion: This study developed a robust ICD-related six-gene prognostic model for HCC that can accurately stratify patient risk, reflect the tumor immune landscape, and provide guidance for immunotherapy and personalized treatment strategies.

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