Three Immune-Associated Subtypes of Diffuse Glioma Differ in Immune Infiltration, Immune Checkpoint Molecules, and Prognosis

Abstract

Diffuse glioma is one of the most prevalent malignancies of the brain, with high heterogeneity of tumor-infiltrating immune cells. However, immune-associated subtypes of diffuse glioma have not been determined, nor has the effect of different immune-associated subtypes on disease prognosis and immune infiltration of diffuse glioma patients. We retrieved the expression profiles of immune-related genes from The Cancer Genome Atlas (TCGA) (n = 672) and GSE16011 (n = 268) cohorts and used them to identify subtypes of diffuse glioma via Consensus Cluster Plus analysis. We used the limma, clusterProfiler, ESTIMATE, and survival packages of R for differential analysis, functional enrichment, immune and stromal score evaluation respectively in three subtypes, and performed log-rank tests in immune subtypes of diffuse glioma. The immune-associated features of diffuse glioma in the two cohorts were characterized via bioinformatic analyses of the mRNA expression data of immune-related genes. Three subtypes (C1-3) of diffuse glioma were identified from TCGA data, and were verified using the GSE16011 cohort. We then evaluated their immune characteristics and clinical features. Our mRNA profiling analyses indicated that the different subtypes of diffuse glioma presented differential expression profile of specific genes and signal pathways in the TCGA cohort. Patients with subtype C1, who were mostly diagnosed with grade IV glioma, had poorer outcomes than patients with subtype C2 or C3. Subtype C1 was characterized by a higher degree of immune cell infiltration as estimated by GSVA, and more frequent wildtype IDH1. By contrast, subtype C3 included more grade II and IDH1-mutated glioma, and was associated with more infiltration of CD4⁺T cells. Most subtype C2 had the features between subtypes C1 and C3. Meanwhile, immune checkpoints and their ligand molecules, including PD1/(PD-L1/PDL2), CTLA4/(CD80/CD86), and B7H3/TLT2, were significantly upregulated in subtype C1 and downregulated in subtype C3. In addition, patients with subtype C1 exhibited more frequent gene mutations. Univariate and multivariate Cox regression analyses revealed that diffuse glioma subtype was an effective, independent, and better prognostic factor. Therefore, we established a novel immune-related classification of diffuse glioma, which provides potential immunotherapy targets for diffuse glioma.

Keywords: Gene Expression Omnibus (GEO); The Cancer Genome Atlas (TCGA); bioinformatic analysis; diffuse glioma; immune checkpoint molecule; immune-associated subtype; tumor immune infiltration.

Figure 1

Identification of immune-related subtypes of diffuse glioma in TCGA cohort. (A) The cumulative distribution function (CDF) curves in consensus cluster analysis. Consensus scores for different subtype numbers (k = 2 to 6) are presented. (B) The heatmap illustrating the consensus matrix at k = 3 in TCGA cohort. (C, D) The stratification into three subtypes validated by t-SNE in TCGA and GSE16011 cohorts. Each dot represents a single sample, and each color denotes a subtype. (E, F) Survival analysis of patients with the three diffuse glioma subtypes (C1, C2, and C3) in TCGA and GSE16011 cohorts. The log-rank test was conducted to determine the significance of the differences.

Figure 2

Subtype-specific genes and signaling pathways in diffuse glioma. (A, B) KEGG analysis of differentially expressed genes in subtypes C1 (A) and C3 (B). The size and color of the dots represent the number of genes and the range of P values, respectively. (C) A Venn diagram depicting overlapping immune-gene sets among the three subtypes. (D) t-SNE analysis showing the stratification based on the expression profile of subtype-specific genes in TCGA cohort. (E) The expression profile of 109 subtype-specific genes.

Figure 3

Clinical characteristics of the three subtypes in TCGA cohort. (A, B) Histogram depicting the frequency of IDH mutations in the tumor (A) and various tumor grades (B) in each subtype. (C, D) Box plots displaying the immune scores (C) and stromal scores (D) in each subtype. Pairwise comparisons of the subtypes were performed using Student’s t-test, and P <0.05 was regarded as significant. *** means P<0.001.

Figure 4

Immune characteristics of the three subtypes in TCGA cohort. (A) The heatmap showing the abundance of immune-cell populations calculated by GSVA in the three subtypes. (B–D) A box plot depicting differences in immune infiltration in C1 (B), C2 (C), and C3 (D). Unpaired Student’s t-test was performed to compare two groups with normally distributed variables, and the P values are labeled above each box plot with asterisks (“ns” means “not significant,” *P < 0.05, **P < 0.01, ***P < 0.001).

Figure 5

Validation of immune infiltration data from the three immune-related in the GSE16011 cohort. (A–F) A box plot displaying differential abundance of CD8⁺ T cells (A), activated CD4⁺ T cells (B), neutrophils (C), activated dendritic cells (D), macrophages (E), and NK cells (F) among subtypes C1, C2, and C3. The pairwise comparisons between subtypes were performed using the Student’s t-test, and P <0.05 was considered significant. **, *** means P<0.01 and P<0.001, respectively. “ns” means “not significant”.

Figure 6

Differential expression of immune checkpoint genes in the TCGA cohort. (A) The heatmap illustrating mRNA expression of 16 immune checkpoint genes in three subtypes. (B–I) A box plot displaying the differential abundance of PD-L1 (B), CD80 (C), B7-H3 (D), CTLA-4 (E), CD86 (F), PD-1 (G), B7-H4 (H), and PD-L2 (I) among subtypes C1, C2, and C3. The pairwise comparisons between subtypes were performed using Student’s t-test, and P <0.05 was considered significant. “ns” means “not significant”, *P < 0.05, **P< 0.01, ***P < 0.001.

Figure 7

Association between immune-related subtypes and somatic mutations in TCGA cohort. (A) The number of mutations in diffuse glioma subtypes. (B–D) A scatter plot depicting the correlation between TMB and PD-L1 mRNA expression (B), relative abundance of CD8⁺ T cells (C), and PD-1 mRNA expression (D). (E) Oncoprint analysis of mutation status of genes involved in “glycolysis,” “P53 pathway,” and “G2/M checkpoint”. “ns” means “not significant”, *** means P<0.001.

Figure 8

Prognostic value of the proposed subtyping for diffuse glioma. (A, B) Forest plots depicting the univariate Cox regression analysis (A) and multivariate Cox regression analysis (B). The red and green curves respectively indicate poor and favorable prognostic factors, while the blue curve depicts clinical characteristics that are not associated with the prognosis. HR, hazard ratio; CI, confidence interval. P <0.05 was considered significant.