Comprehensive Exploration of Tumor Mutational Burden and Immune Infiltration in Diffuse Glioma

Background: Immune checkpoint inhibitors (ICIs) have been used as a novel treatment for diffuse gliomas, but the efficacy varies with patients, which may be associated with the tumor mutational burden (TMB) and immune infiltration. We aimed to explore the relationship between the two and their impacts on the prognosis.

Methods: The data of the training set were downloaded from The Cancer Genome Atlas (TCGA). "DESeq2" R package was used for differential analysis and identification of differentially expressed genes (DEGs). A gene risk score model was constructed based on DEGs, and a nomogram was developed combined with clinical features. With the CIBERSORT algorithm, the relationship between TMB and immune infiltration was analyzed, and an immune risk score model was constructed. Two models were verification in the validation set downloaded from the Chinese Glioma Genome Atlas (CGGA).

Results: Higher TMB was related to worse prognosis, older age, higher grade, and higher immune checkpoint expression. The gene risk score model was constructed based on BIRC5, SAA1, and TNFRSF11B, and their expressions were all negatively correlated with prognosis. The nomogram was developed combined with age and grade. The immune risk score model was constructed based on M0 macrophages, neutrophils, naïve CD4 T cells, and activated mast cells. The proportions of the first two were higher in the high-TMB group and correlated with worse prognosis, while the latter two were precisely opposite.

Conclusions: In diffuse gliomas, TMB was negatively correlated with prognosis. The association of immune infiltration with TMB and prognosis varied with the type of immune cells. The nomogram and risk score models can accurately predict prognosis. The results can help identify patients suitable for ICIs and potential therapeutic targets, thus improve the treatment of diffuse gliomas.