ArticleOncology research2025
Immunogenic Cell Death Genes Related Prognostic Biomarker in Hepatocellular Carcinoma.
Article in Oncology research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Investigating the Shared Mechanisms of Endocrine-Disrupting Chemicals in Urogenital Tumors.Biology · 2026Article
- Integrated multi-omics analysis of neutrophil extracellular trap-related genes in renal cell carcinoma using bioinformatics and machine learning.Discover oncology · 2026Article
- Novel models for predicting individualized outcomes in patients with advanced hepatocellular carcinoma receiving immunotherapy.Frontiers in oncology · 2026Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
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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