CD96, a new immune checkpoint, correlates with immune profile and clinical outcome of glioma

Abstract

CD96 is a promising candidate for immunotherapy. However, its role and importance in glioma remains unknown. We thus aimed to genetically and clinically characterize CD96 expression in gliomas. For this, we extracted RNA-seq data of 699 glioma samples from the TCGA dataset and validated these findings using the CGGA dataset comprising 325 glioma samples. Clinical and isocitrate dehydrogenase (IDH) mutation status were also analyzed. Various packages in R language were mainly used for statistical analysis. CD96 expression was significantly up-regulated in high-grade, IDH-wildtype, and mesenchymal-molecular subtype gliomas based on TCGA data, which was validated using the CGGA dataset. Subsequent gene ontology analysis of both datasets suggested that genes relevant to CD96 are mainly involved in immune functions in glioma as such genes were positively correlated with CD96 expression. To further explore the relationship between CD96 and immune responses, we selected seven immune-related metagenes and found that CD96 expression was positively correlated with HCK, LCK, and MHC II in the CGGA and TCGA cohorts but negatively associated with IgG. Further, Pearson correlation analysis showed that CD96 is associated with TIGIT, CD226, CRTAM, TIM-3, PD-L1, CTLA-4, and STAT3, indicating the additive antitumoral effects of these checkpoint proteins. CD96 was also suggested to play an important role in immune responses and positively collaborate with other checkpoint members. These findings show that CD96 is promising candidate for immunotherapy, and that such agents could complement current immunotherapy strategies for glioma.

Figure 1

The association of CD96 expression with clinical glioma parameters. CD96 expression in the TCGA dataset according to WHO grade (A) and isocitrate dehydrogenase (IDH) status (C); CD96 expression in CGGA dataset according to WHO grade (B) and IDH status (D). *Indicates p value < 0.05, **Indicates p value < 0.01, ***Indicates p value < 0.001.

Figure 2

Relationship between CD96 expression and glioma molecular subtypes. CD96 expression pattern in different molecular subtypes of gliomas based on TCGA (A) and CGGA (B) datasets. ROC curve analysis revealed the predictive value of CD96 in the mesenchymal-subtype based on both datasets (C,D).

Figure 3

Gene ontology (GO) analysis of CD96-related signatures in gliomas. Results were based on in TCGA (A) and CGGA (B) datasets; 328 genes common to both datasets (C,D) were used for GO analysis and validation.

Figure 4

Heatmap analysis of the relationship between CD96 and immune function-related genes in glioma. Gene sets of immune system were downloaded from the AmiGO 2 website for TCGA (A) and CGGA (B) datasets.

Figure 5

CD96-related inflammatory activities in gliomas. The relationship between CD96 and glioma grade, isocitrate dehydrogenase (IDH) status, molecular subtypes, and seven metagenes are presented as a heatmap (A,B). Correlogram show the correlation between CD96 and seven immune-related metagenes based on TCGA (C) and CGGA (D) datasets.

Figure 6

Association between CD96 and immune checkpoint markers in glioma. The correlations between CD96 and immunoglobulin family members including TIGIT, CD226, and CRTAM based on TCGA (A) and CGGA (B) datasets are presented. Ligands CD111 and CD115, which bind CD96, were also included in the analysis. Correlation between CD96 and other immune checkpoint markers that have been examined in clinical trials or clinical situations, including PD-L1, CTLA-4, TIM-3, and STAT3, were also subjected to the analysis (C,D).

Figure 7

CD96 as a malignant biological indicator in glioma. Results show that higher CD96 expression predicted worse overall survival in patients with glioma (A,C) and GBM (B,D) for both TCGA and CGGA datasets.