ArticleFrontiers in immunology2025
Integrated multi-omics and machine learning reveal an immunogenic cell death-related signature for prognostic stratification and therapeutic optimization in colorectal cancer.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prognostic significance and pathological correlation analysis of DKK4 in colorectal cancer.Translational cancer research · 2026Article
- Comprehensive Landscape of Diagnostic, Prognostic and Predictive Biomarkers in Colorectal Cancer: From Genomics to Multi-Omics Integration in Precision Medicine.Journal of personalized medicine · 2026Review
- Myokine-mediated mechanisms of immune checkpoint inhibitors-associated colorectal injury and repair in rectal cancer.Frontiers in immunology · 2026Review
- The dual roles ofFrontiers in oncology · 2026Review
- Bioinformatics combined with machine learning for the identification of malignant transformation markers in colorectal polyps.Frontiers in molecular biosciences · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer (CRC) continues to rise in global incidence and remains a leading cause of cancer-related mortality. Immunogenic cell death (ICD) has emerged as a critical modulator of tumor microenvironment (TME) dynamics; however, its prognostic implications and therapeutic potential in CRC require systematic characterization. Through the integrative analysis of single-cell RNA sequencing and bulk transcriptomic data, 11 ICD-related genes with prognostic significance were identified in CRC. A comprehensive computational framework was then employed to evaluate 101 machine learning combinations, ultimately constructing an optimized 11-gene ICD-related signature (ICDRS) by integrating StepCox [forward] and RSF. The ICDRS exhibited strong predictive performance for overall survival in CRC patients across the training and validation datasets. Notably, the ICDRS-based nomogram achieved outstanding time-dependent AUCs (>0.90) for 1- to 3-year survival prediction. Multidimensional analysis revealed significant associations between ICDRS-derived risk score and distinct immune infiltration patterns, immunotherapy response and TME characteristics. Furthermore, a novel macrophage subtype, SPP1
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