基于机器学习框架在结直肠癌中识别一种新型铁死亡诱导的免疫原性细胞死亡相关特征

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.

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.

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 profiles and poorer prognoses.

This study developed a novel signature integrating ferroptosis and immunogenic cell death, creating a valuable model for predicting prognosis and the tumor immune environment in CRC. Furthermore, seven key genes emerged as promising targets for further investigation and therapeutic intervention, highlighting their potential role in ferroptosis and immunogenic cell death.