Comprehensive analysis of immune escape-related prognostic signature in high-grade glioma

Objective High-grade glioma (HGG) is one of the most lethal malignancies. Immune escape is considered to be a reason for the failure of immunotherapy for HGG patients, and there is currently no immune escape-related prognostic model for glioma. Therefore, we explored the relationship between immune escape-related genes and the prognosis of patients with HGG. Methods This study combined 101 machine learning algorithms to determine the best immune escape-related prognostic model. Subsequently, the TCGA and CGGA cohorts were used to verify the effectiveness of the model. Subsequently, molecular docking, Mendelian randomization and other comprehensive analyses were performed on the model genes. Finally, the biology function of the signature gene was further verified via CCK-8, and colony formation. Results Our differential expression analysis found that 41 immune escape-related genes were significantly related to the prognosis of HGG patients. We further selected 18 key genes through various machine learning methods to construct an immune escape-related prognosis model. This model can effectively distinguish between high-risk and low-risk groups, and shows good prediction results in both CGGA and TCGA data sets. Subsequently, the risk score was found to be an independent prognostic factor, and the nomogram including clinical characteristics was constructed. Immunotherapy response prediction results show that patients in the low-risk group respond better to immunotherapy and have longer survival. Furthermore, TAB1 knockdown reduced the ability of human glioma cells to proliferate and clone. Conclusion This study constructed a prognostic model related to immune escape through multiple machine learning methods and verified its clinical application value in HGG patients. It provides a theoretical basis for the exploration of immune escape treatment targets.