Identification of a novel ferroptosis-induced immunogenic cell death related signature based on a machine learning framework in colorectal cancer

作者：Feng Zhu, Xin Liu, Huiyuan Li, Jianfeng Li, Hongzhang Liu, Yusheng Wang · 发表于：Discover Oncology · 年份：2025 · DOI：10.1007/s12672-025-03147-1 · 被引用次数：6 · 研究领域：Ferroptosis and cancer prognosis、Cancer Immunotherapy and Biomarkers、Immune cells in cancer

BACKGROUND: Ferroptosis and immunogenic cell death play vital roles in colorectal cancer (CRC). The interplay between ferroptosis and immunogenic cell death (F-ICD) represents a promising frontier in cancer therapy. However, few studies have explored the combined regulatory effects of F-ICD in CRC. METHODS: In current study, we identified F-ICD related genes based on analysis of single-cell transcriptomics level and developed F-ICD related signature using 101 machine learning algorithms and WGCNA analysis. Differential analysis between normal and tumor samples was performed using DESeq2 (|logFC|>1, p. adj < 0.05). The RSF algorithm was chosen for further analysis due to its strong predictive performance, making it a robust tool for our study. An external validation was performed to access the expression level of seven key F-ICD related genes. RESULTS: By quantifying the expression levels of 44 genes related to F-ICD, we found that F-ICD activity was significantly elevated in NK cells, T cells, and some B cells. The module showed a significant correlation with the F-ICD score (r = 0.66). The predictive model had highly accurate AUCs in three datasets (0.99, 0.61, and 0.58 for the 3-years training sets), revealing the importance of F-ICD in different pathological stages and prognoses in CRC. Further results indicated that F-ICD was associated with pathways such as oxidative phosphorylation and NF-κB signaling. Patients with high F-ICD had significantly different mutation profil...