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

Background: Studies have shown that inducing immunogenic cell death (ICD) breaks down the immunosuppressive tumor microenvironment and controls tumor progression, but the relationship between ICD and glioblastoma (GBM) was unclear. Therefore, this study was designed to investigate the potential prognostic value of ICD-related genes in GBM. Methods: A total of 34 ICD-related genes were collected from various sources. Utilizing public databases, relevant data about GBM were extracted and analyzed by the weighted gene co-expression network analysis (WGCNA) to section prognosis-related ICD gene modules. A risk model was developed using the Lasso algorithm, and its accuracy was confirmed by including an independent Gene Expression Omnibus (GEO) dataset. Enrichment analysis was employed to analyze the biological functions and pathways associated with these signals, and the tumor immune infiltration capacity was evaluated. The R package oncoPredict was used to infer the drug sensitivity of patients in different risk groups using the GDSC2 database with expression profiling data. Results: Thirty-four ICD-related genes were differentially expressed in GBM samples and two gene modules significantly associated with prognosis were identified. Base on the two modules, VDR and CIDEB were identified as two signature genes for the prognostic prediction of GBM. Multivariate Cox analysis demonstrated that this signature was an independent factor for evaluating overall survival of GBM, and ROC curves also supported an effective prediction of the signature (1-year AUC: 0.667; 3-year AUC:0.727; 5-year AUC: 0.762). We observed that the high-risk group had higher immune cell infiltration and sensitivity to some drugs. Conclusion: This work developed a novel ICD-related prognostic model for GBM patients. Our findings highlighted the potential of using ICD as a promising prognostic indicator in GBM, contributing to the current understanding of the intricate interplay between ICD and tumor microenvironment.