Immunological classification of gliomas based on immunogenomic profiling

Abstract

Background Gliomas are heterogeneous in the tumor immune microenvironment (TIM). However, a classification of gliomas based on immunogenomic profiling remains lacking. Methods We hierarchically clustered gliomas based on the enrichment levels of 28 immune cells in the TIM in five datasets and obtained three clusters: immunity-high, immunity-medium, and immunity-low. Results Glioblastomas were mainly distributed in immunity-high and immunity-medium, while lower-grade gliomas were distributed in all the three subtypes and predominated in immunity-low. Immunity-low displayed a better survival than other subtypes, indicating a negative correlation between immune infiltration and survival prognosis in gliomas. IDH mutations had a negative correlation with glioma immunity. Immunity-high had higher tumor stemness and epithelial-mesenchymal transition scores and included more high-grade tumors than immunity-low, suggesting that elevated immunity is associated with tumor progression in gliomas. Immunity-high had higher tumor mutation burden and more frequent somatic copy number alterations, suggesting a positive association between tumor immunity and genomic instability in gliomas. Conclusions The identification of immune-specific glioma subtypes has potential clinical implications for the immunotherapy of gliomas.

PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma

“…Whereas, in LGGs with high methylation levels of these CpGs, non-oncogenic cells and functions are enriched. These results support the findings of unfavorable survival associated with low methylation of these CpGs in LGG, as immunity-high LGGs have been reported to show higher tumor stemness and epithelial-mesenchymal transition scores, leading to unfavorable survival [21,22].…”

“…The Kaplan-Meier survival curves for CpGs that are significantly prognostic after adjusting for age, gender, tumor grade, and IDH1 mutation are presented in Figure 2(Ci-Civ) for DFS along with forest plot visualization of hazard ratios with 95% CI in Figure 2D. For cg07425555 (Figure 2Ci), the median DFS of high methylation ("Meth") versus low methylation ("Unmeth") was 72 (52-91) months versus 31 (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) months (p < 0.001). For cg26969888, the median DFS of Meth versus Unmeth was 63 (44-83) months versus 30 (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37) months (p < 0.001).…”

Single-Cell Omics in Dissecting Immune Microenvironment of Malignant Gliomas—Challenges and Perspectives

“…Stratification of WHO grade II-IV glioma patients according to mutational burden indicates that a high number of mutations is associated with significantly worse overall survival and higher expression of immune checkpoint encoding genes [ 87 , 88 ]. Importantly, patients with high mutational burden showed the enrichment of immune cell signatures [ 89 ].…”

Bioinformatics analysis of the prognostic value of NEK8 and its effects on immune cell infiltration in glioma

“…Finally, a PPI network analysis of DEGs illustrated that these genes are closely correlated with biomorph regulation and development (Figure 6D ). Interestingly, mutations in immune infiltration‐related genes 27 were significantly associated with NEK8 expression in glioma datasets from CGGA. These genes included DNAH10 , DNAH11 , ALK , FAT2 , ZEB2 and CD274 (Figure 6E–J ).…”

“…The NEK8 expression level showed significant prognostic value in the following subgroups: grade III & IV (p < 0.001), IDH mutation (p = 0.003), non-codeletion (p < 0.001) for the 1p/19q codeletion, female (p < 0.001) and male (p < 0.001), ≤60 years of age (p < 0.001) and >60 years of age (p = 0.012), wild-type (WT) EGFR status (p < 0.001), WT PIK3CA status (p < 0.001), progressive disease (p < 0.001) and stable disease (p < 0.001) for the primary therapy outcome; astrocytoma (p < 0.001) and oligodendroglioma (p = 0.03) histological types and white race (p < 0.001) and development (Figure 6D). Interestingly, mutations in immune infiltration-related genes 27 were significantly associated with NEK8 expression in glioma datasets from CGGA. These genes included DNAH10, DNAH11, ALK, FAT2, ZEB2 and CD274 (Figure 6E-J).…”