Immune Evasion Mechanism Mediated by ITPRIPL1 and Its Prognostic Implications in Glioma

Abstract

ABSTRACT Background: Glioma represent one of the most prevalent and lethal malignancies within the central nervous system. Recent studies have identified ITPRIPL1, a newly reported CD3ε‐inhibitory ligand, as a suppressor of T cell activation, thereby facilitating tumor immune evasion and offering a novel avenue for immunotherapeutic intervention in glioma. Methods: A comprehensive analysis was performed using datasets from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO). This included evaluating ITPRIPL1 expression levels in glioma, its association with clinicopathological features, prognostic significance, immune landscape, targeted drug sensitivity, and underlying biological functions. Ninety‐eight machine learning algorithm combinations were screened to identify the optimal predictive model. A nomogram was subsequently constructed and validated to assess the integrated prognostic impact of ITPRIPL1 expression on glioma patients. Results: Elevated ITPRIPL1 expression was positively correlated with higher tumor grade and poorer clinical outcomes. Immune infiltration profiling revealed that ITPRIPL1 expression was negatively associated with effector memory CD4⁺ T cells and type 17 T helper cells (Th17), but positively correlated with M2‐polarized macrophages and several immune checkpoint molecules. Moreover, drug sensitivity analyses and molecular docking studies highlighted a potential therapeutic relationship between ITPRIPL1 and antitumor agents such as AZD8055. The SuperPC model emerged as the most robust predictor and was utilized to develop a prognostic nomogram capable of reliably forecasting survival in glioma patients. Conclusions: This study reveals that ITPRIPL1 plays a dual role in glioma: It suppresses T cell–mediated immune responses, contributing to an immunosuppressive microenvironment, and interferes with the efficacy of antitumor drugs, thereby promoting tumor progression and ultimately leading to poor patient prognosis.

Keywords

• glioma
• immune infiltration
• ITPRIPL1
• machine learning
• prognostic biomarker