Multiomics Data Analysis and Identification of Immune-Related Prognostic Signatures With Potential Implications in Prognosis and Immune Checkpoint Blockade Therapy of Glioblastoma

BackgroundIn recent years, glioblastoma multiforme (GBM) has been a concern of many researchers, as it is one of the main drivers of cancer-related deaths worldwide. GBM in general usually does not responding well to immunotherapy due to its unique microenvironment. MethodsTo uncover any further informative immune-related prognostic signatures, we explored the immune-related distinction in the genetic or epigenetic features of the three types (expression profile, somatic mutation, and DNA methylation). Twenty eight immune-related hub genes were identified by Weighted Gene Co-Expression Network Analysis (WGCNA). The findings showed that three genes (IL1R1, TNFSF12, and VDR) were identified to construct an immune-related prognostic model (IRPM) by lasso regression. Then, we used three hub genes to construct an IRPM for GBM and clarify the immunity, mutation, and methylation characteristics. ResultsSurvival analysis of patients undergoing anti-program cell death protein 1 (anti-PD-1) therapy showed that overall survival was superior in the low-risk group than in the high-risk group. The high-risk group had an association with epithelial-mesenchymal transition (EMT), high immune cell infiltration, immune activation, a low mutation number, and high methylation, while the low-risk group was adverse status. ConclusionsIn conclusion, IRPM is a promising tool to distinguish the prognosis of patients and molecular and immune characteristics in GBM, and the IRPM risk score can be used to predict patient sensitivity to checkpoint inhibitor blockade therapy. Thus, three immune-related signatures will guide us in improving treatment strategies and developing objective diagnostic tools.