Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis.

Abstract

Background: Glioma is the most common primary tumor of the central nervous system. The conventional glioma treatment strategies include surgical excision and chemo- and radiation-therapy. Interferon Gamma (IFN-γ) is a soluble dimer cytokine involved in immune escape of gliomas. In this study, we sought to identify IFN-γ-related genes to construct a glioma prognostic model to guide its clinical treatment. Methods: RNA sequences and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the China Glioma Genome Atlas (CGGA). Using univariate Cox analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, IFN-γ-related prognostic genes were selected to construct a risk scoring model, and analyze its correlation with the clinical features. A high-precision nomogram was drawn to predict prognosis, and its performance was evaluated using calibration curve. Finally, immune cell infiltration and immune checkpoint molecule expression were analyzed to explore the tumor microenvironment characteristics associated with the risk scoring model. Results: Four out of 198 IFN-γ-related genes were selected to construct a risk score model with good predictive performance. The expression of four IFN-γ-related genes in glioma tissues was significantly increased compared to normal brain tissue (p < 0.001). Based on ROC analysis, the risk score model accurately predicted the overall survival rate of glioma patients at 1 year (AUC: The Cancer Genome Atlas 0.89, CGGA 0.59), 3years (AUC: TCGA 0.89, CGGA 0.68), and 5years (AUC: TCGA 0.88, CGGA 0.70). Kaplan-Meier analysis showed that the overall survival rate of the high-risk group was significantly lower than that of the low-risk group (p < 0.0001). Moreover, high-risk scores were associated with wild-type IDH1, wild-type ATRX, and 1P/19Q non-co-deletion. The nomogram predicted the survival rate of glioma patients based on the risk score and multiple clinicopathological factors such as age, sex, pathological grade, and IDH Status, among others. Risk score and infiltrating immune cells including CD8 T-cell, resting CD4 memory T-cell, regulatory T-cell (Tregs), M2 macrophages, resting NK cells, activated mast cells, and neutrophils were positively correlated (p < 0.05). In addition, risk scores closely associated with expression of immune checkpoint molecules such as PD-1, PD-L1, CTLA-4, LAG-3, TIM-3, TIGIT, CD48, CD226, and CD96. Conclusion: Our risk score model reveals that IFN-γ -associated genes are an independent prognostic factor for predicting overall survival in glioma, which is closely associated with immune cell infiltration and immune checkpoint molecule expression. This model will be helpful in predicting the effectiveness of immunotherapy and survival rate in patients with glioma.