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Constructing a Pan-Cancer Prognostic Model via Machine Learning Based on Immunogenic Cell Death Genes and Identifying
Luojin Wu1, Qing Sun1, Atsushi Kitani2
1Department of Immunology, School of Medicine, Nantong University, Nantong 226019, China.
Current Issues in Molecular Biology
|October 28, 2025
Summary
Immunogenic cell death (ICD) regulates antitumor immunity by releasing antigens. This study analyzes ICD genes across 33 cancers, identifying NT5E as a key biomarker for head and neck cancer, aiding immunotherapy prediction.
Area of Science:
- Oncology
- Immunology
- Genetics
Background:
- Immunogenic cell death (ICD) is crucial for initiating antitumor immune responses by releasing tumor antigens.
- Understanding ICD's regulatory mechanisms and immunological effects across diverse cancer types is limited.
- Systematic analysis of ICD-related genes is needed to elucidate their role in cancer progression and immune evasion.
Purpose of the Study:
- To comprehensively analyze the expression and clinical significance of 34 ICD-related genes across 33 tumor types.
- To identify prognostic biomarkers and therapeutic targets for improving immunotherapy response and patient outcomes.
- To explore the relationship between ICD genes, tumor microenvironment, and immune cell infiltration.
Main Methods:
- Systematic analysis of RNA expression, copy number variation (CNV), and DNA methylation of 34 ICD-related genes in 33 cancer types.
- Association analysis of gene expression, CNVs, single-nucleotide variations (SNVs), and methylation with clinical features and patient survival.
- Machine learning framework for building prognostic models, stratifying patients into immunological subtypes, and identifying key biomarkers.
- Exploration of relationships with immune cell infiltration, stemness, heterogeneity, immune scores, and immune checkpoint genes.
- Drug prediction and molecular docking analyses to nominate therapeutic targets.
Main Results:
- ICD-related genes were predominantly overexpressed in ESCA, GBM, KIRC, LGG, PAAD, and STAD.
- RNA expression of most ICD-related genes correlated with poor prognosis; DNA methylation showed survival correlations in LGG and UVM.
- Prognostic models were established for 18 cancer types, revealing intrinsic immune regulatory mechanisms of ICD genes.
- NT5E was identified as a predictive biomarker in head and neck squamous cell carcinoma (HNSC), mediating tumor-immune interactions.
- Key prognostic biomarkers were identified across cancers, offering insights into immunotherapy response.
Conclusions:
- This study provides a comprehensive landscape of ICD-related genes across various cancers.
- NT5E is highlighted as a potential predictive biomarker for HNSC, impacting tumor-immune interactions.
- The findings offer novel targets for predicting immunotherapy response and improving clinical outcomes in cancer patients.
