Frontiers in pharmacology · 2026
Multi-omics integration and machine learning-driven construction of an immunogenic cell death prognostic model for colon cancer and functional validation of FCGR2A.
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Wang Haipeng, Chen Ningning, Wang Weijia
Аннотация
BACKGROUND: Immunogenic cell death (ICD) influences tumor immune microenvironment remodeling and immunotherapy response. However, the prognostic value of ICD-related genes in colon cancer has not been systematically clarified. This study aimed to develop an ICD-based prognostic model and explore its association with the immune microenvironment and treatment sensitivity.
METHODS: Transcriptomic and clinical data of colon adenocarcinoma (COAD) patients were obtained from TCGA and GTEx, with GSE17538 and GSE38832 used as external validation cohorts. Single-cell RNA-seq data from the Colon Cancer Atlas were analyzed to characterize ICD-associated T-cell states. Differentially expressed genes between high and low ICD-score T cells were identified using ssGSEA, followed by WGCNA to select immune-related modules. One hundred seventeen machine-learning model combinations were evaluated to construct the optimal prognostic signature. Immune infiltration was assessed using CIBERSORT, ssGSEA, and ESTIMATE. GSEA explored pathway differences, while drug sensitivity was predicted using pRRophetic. The top-weighted gene was validated through in vitro assays.
RESULTS: Seven major cell types were identified within the tumor microenvironment. T cells with high and low ICD scores exhibited distinct functional and spatial patterns. WGCNA identified a key module highly correlated with ICD scores, and 51 genes were screened. The Random Survival Forest model yielded a 15-gene ICD-related signature with strong prognostic performance (C-index 0.968 in TCGA; 0.767 and 0.855 in validation cohorts). High-risk patients consistently showed poorer survival (p < 0.001). A combined nomogram demonstrated stable predictive accuracy. High-risk patients displayed increased M2 macrophages and Tregs, whereas low-risk patients exhibited higher activated CD4+ T cells and plasma cells. EMT and angiogenesis pathways were enriched in the high-risk group, while metabolic pathways predominated in the low-risk group. High-risk patients were more sensitive to drugs such as Dasatinib. FCGR2A overexpression promoted proliferation, migration, and invasion in vitro.
CONCLUSION: The 15-gene ICD-based model effectively predicts COAD prognosis, reflects immune microenvironment heterogeneity, and offers insights for individualized treatment planning.
METHODS: Transcriptomic and clinical data of colon adenocarcinoma (COAD) patients were obtained from TCGA and GTEx, with GSE17538 and GSE38832 used as external validation cohorts. Single-cell RNA-seq data from the Colon Cancer Atlas were analyzed to characterize ICD-associated T-cell states. Differentially expressed genes between high and low ICD-score T cells were identified using ssGSEA, followed by WGCNA to select immune-related modules. One hundred seventeen machine-learning model combinations were evaluated to construct the optimal prognostic signature. Immune infiltration was assessed using CIBERSORT, ssGSEA, and ESTIMATE. GSEA explored pathway differences, while drug sensitivity was predicted using pRRophetic. The top-weighted gene was validated through in vitro assays.
RESULTS: Seven major cell types were identified within the tumor microenvironment. T cells with high and low ICD scores exhibited distinct functional and spatial patterns. WGCNA identified a key module highly correlated with ICD scores, and 51 genes were screened. The Random Survival Forest model yielded a 15-gene ICD-related signature with strong prognostic performance (C-index 0.968 in TCGA; 0.767 and 0.855 in validation cohorts). High-risk patients consistently showed poorer survival (p < 0.001). A combined nomogram demonstrated stable predictive accuracy. High-risk patients displayed increased M2 macrophages and Tregs, whereas low-risk patients exhibited higher activated CD4+ T cells and plasma cells. EMT and angiogenesis pathways were enriched in the high-risk group, while metabolic pathways predominated in the low-risk group. High-risk patients were more sensitive to drugs such as Dasatinib. FCGR2A overexpression promoted proliferation, migration, and invasion in vitro.
CONCLUSION: The 15-gene ICD-based model effectively predicts COAD prognosis, reflects immune microenvironment heterogeneity, and offers insights for individualized treatment planning.