Immune prognostic model for glioblastoma based on the ssGSEA enrichment score

Few effective immune prognostic models based on the tumor immune microenvironment (TIME) for glioblastoma have been reported. Therefore, this study aimed to construct an immune prognostic model for glioblastoma by analyzing enriched biological processes and pathways in tumors.

A comprehensive single-sample gene set enrichment analysis (ssGSEA) of gene sets from the Molecular Signatures Database was performed using TCGA RNA sequencing data (141 glioblastoma cases). After evaluating gene sets associated with prognosis using univariable Cox regression, gene sets related to biological processes and tumor immunity in gliomas were extracted. Finally, the least absolute shrinkage and selection operator Cox regression refined the gene sets and a nomogram was constructed. The model was validated using CGGA (183 cases) and Aichi Cancer Center (42 cases) datasets.

The immune prognostic model consisted of three gene sets related to biological processes (sphingolipids, steroid hormones, and intermediate filaments) and one related to tumor immunity (immunosuppressive chemokine pathways involving tumor-associated microglia and macrophages). Kaplan-Meier curves for the training (TCGA) and validation (CGGA) cohorts showed significantly worse overall survival in the high-risk group compared to the low-risk group (p < 0.001 and p = 0.04, respectively). Furthermore, in silico cytometry revealed a significant increase in macrophages with immunosuppressive properties and T cells with effector functions in the high-risk group (p < 0.01) across all cohorts.

Construction of an immune prognostic model based on the TIME assessment using ssGSEA could potentially provide valuable insights into the prognosis and immune profiles of patients with glioblastoma and guide treatment strategies.