Weighted Gene Coexpression Network Analysis Identifies an Immunogenic Cell Death Signature for Predicting Prognosis and Therapeutic Responses in Glioblastoma

Abstract

Abstract Immunogenic cell death (ICD) represents a regulated cell death (RCD) process that can activate the adaptive immune response. Accumulating evidence supports the notion that ICD can reshape the tumor immune microenvironment (TIME) by releasing danger signals or damage-associated molecular patterns (DAMPs), exerting a profound impact on glioblastoma prognosis. In this study, we applied weighted gene coexpression network analysis (WGCNA) to the TCGA-GBM cohort, enabling the identification of 319 ICD-related genes. Subsequently, we developed and rigorously validated an ICD-based prognostic model, effectively predicting the survival rate of GBM patients. Notably, marked disparities in the tumor immune microenvironment (TIME) were observed between the high- and low-score groups, underscoring the significance of our findings. In summary, we present a novel prognostic model for GBM utilizing an ICD signature. These findings highlight the potential of ICD as a promising prognostic indicator in GBM and contribute to our understanding of the intricate interplay between ICD and the tumor microenvironment. Ultimately, this research contributes to the advancement of GBM prognosis assessment, paving the way for improved patient outcomes in this challenging disease setting.