Immunogenic cell death-related macrophage gene model for prognostic prediction in glioblastoma

Abstract Glioblastoma (GBM) is an extremely aggressive and diverse type of brain tumor that is associated with a poor prognosis. Immunogenic cell death (ICD) is a particular form of cellular demise that is closely linked to antineoplastic immune responses. However, it is important to note that the function of ICD in GBM is not yet fully understood. Utilizing single-cell analysis, AddModuleScore, and weighted gene co-expression network analyses (WGCNA), ICD-related genes were identified at both the bulk transcriptome and single-cell levels. Based on these newly identified ICD-related genes, a novel signature for GBM was developed employing three machine learning methods: LASSO, XGBoost, and RandomForest. This prognostic model comprises four characteristic genes: G0S2, NEUROD1, SERPINE2, and LZTS1. The overall survival was significantly lower for the high-risk group compared to the low-risk group in the TCGA-GBM cohort (P < 0.001), CGGA693 cohort (P < 0.001), CGGA325 cohort (P < 0.001), and CGGA301 cohort (P < 0.001). This dichotomy indicates that the riskscore encapsulates a spectrum of tumor microenvironment phenotypes, ranging from immune-permissive (high-risk group) to immune-excluded (low-risk group), with direct implications for therapeutic response. Tumor microenvironment profiling has revealed that the high-risk patients display elevated stromal and immune scores, alongside reduced tumor purity, indicative of a densely infiltrated, macrophage-rich microenvironment. By elucidating the interaction between ICD and macrophage biology, this study establishes a robust prognostic model that surpasses traditional clinical parameters, offering insights into tumor microenvironment immunity and therapeutic vulnerabilities in GBM.