Immune prognostic model for glioblastoma based on the ssGSEA enrichment score.

in Cancer genetics by Takanari Okamoto, Ryo Mizuta, Ayako Demachi-Okamura, Daisuke Muraoka, Eiichi Sasaki, Katsuhiro Masago, Rui Yamaguchi, Satoshi Teramukai, Yoshihiro Otani, Isao Date, Shota Tanaka, Yoshinobu Takahashi, Naoya Hashimoto, Hirokazu Matsushita

TLDR

  • The study constructed an immune prognostic model for glioblastoma by analyzing biological processes and pathways in tumors, showing a significant correlation with overall survival.

Abstract

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.

Overview

  • The study aimed to construct an immune prognostic model for glioblastoma by analyzing enriched biological processes and pathways in tumors.
  • The model was constructed using TCGA RNA sequencing data (141 glioblastoma cases) and further validated using CGGA (183 cases) and Aichi Cancer Center (42 cases) datasets.
  • The primary objective of the study is to develop a model that can predict the prognosis of glioblastoma patients based on the tumor immune microenvironment (TIME).

Comparative Analysis & Findings

  • The analysis found that the high-risk group had significantly worse overall survival compared to the low-risk group (p < 0.001 and p = 0.04, respectively) in the training (TCGA) and validation (CGGA) cohorts.
  • The model consisted of four 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).
  • 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.

Implications and Future Directions

  • The study provides valuable insights into the prognosis and immune profiles of patients with glioblastoma, potentially guiding treatment strategies.
  • Future studies could explore the use of this model to predict patient response to immunotherapies and to identify potential targets for treatment.
  • The model could also be used to understand the mechanisms underlying the immune suppression in glioblastoma and to identify new therapeutic approaches to enhance anti-tumor immunity.
Read Full Article