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. 2022 Sep 9:13:1001381.
doi: 10.3389/fimmu.2022.1001381. eCollection 2022.

A specific immune signature for predicting the prognosis of glioma patients with IDH1-mutation and guiding immune checkpoint blockade therapy

Zhirui Zeng  1   2 , Chujiao Hu  3   4 , Wanyuan Ruan  5 , Jinjuan Zhang  1   2 , Shan Lei  1   2 , Yushi Yang  6 , Pailan Peng  5   7 , Feng Pan  8 , Tengxiang Chen  1   2
Affiliations

A specific immune signature for predicting the prognosis of glioma patients with IDH1-mutation and guiding immune checkpoint blockade therapy

Zhirui Zeng et al. Front Immunol. .

Abstract

Isocitrate dehydrogenase (IDH1) is frequently mutated in glioma tissues, and this mutation mediates specific tumor-promoting mechanisms in glioma cells. We aimed to identify specific immune biomarkers for IDH1-mutation (IDH1mt) glioma. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were used to obtain RNA sequencing data and clinical characteristics of glioma tissues, while the stromal and immune scores of TCGA glioma tissues were determined using the ESTIMATE algorithm. Differentially expressed genes (DEGs), the protein-protein interaction(PPI) network, and least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to select hub genes associated with stroma and immune scores and the prognoses of patients and to construct the risk model. The practicability and specificity of the risk model in both IDH1mt and IDH1-wildtype (wtIDH1) gliomas in TCGA and CGGA were evaluated. Molecular mechanisms, immunological characteristics and benefits of immune checkpoint blockade therapy in glioma tissues with IDH1mt were analyzed using GSEA, immunohistochemical staining, CIBERSORT, and T-cell dysfunction and exclusion (TIDE) analysis. The overall survival rate for IDH1mt-glioma patients with high stroma/immune scores was lower than that for those with low stroma/immune scores. A total of 222 DEGs were identified in IDH1mt glioma tissues with high stroma/immune scores. Among them, 72 genes had interactions in the PPI network, while three genes, HLA-DQA2, HOXA3, and SAA2, were selected as hub genes and used to construct risk models classifying patients into high- and low-risk score groups, followed by LASSO and Cox regression analyses. This risk model showed prognostic value in IDH1mt glioma in both TCGA and CCGA; nevertheless, the model was not suitable for wtIDH1 glioma. The risk model may act as an independent prognostic factor for IDH1mt glioma. IDH1mt glioma tissues from patients with high-risk scores showed more infiltration of M1 and CD8 T cells than those from patients with low-risk scores. Moreover, TIDE analysis showed that immune checkpoint blockade(ICB) therapy was highly beneficial for IDH1mt patients with high-risk scores. The risk model showed specific potential to predict the prognosis of IDH1mt-glioma patients, as well as guide ICB, contributing to the diagnosis and therapy of IDH1mt-glioma patients.

Keywords: IDH1 mutation; glioma; immune; immune checkpoint blockade therapy; signature.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Effects of stromal and immune scores on the survival of IDH1mt-glioma patients. (A) Kaplan–Meier survival analysis showing the survival rate in high- and low-stromal-score-group IDH1mt-glioma patients. (B) Kaplan–Meier survival analysis showing the survival rate in high- and low-immune-score-group IDH1mt-glioma patients.
Figure 2
Figure 2
Differentially expressed genes in high- and low-stromal/immune-score group IDH1mt-glioma tissues. (A) Volcano plot demonstrating the differential expression of genes in high- and low-stromal group of IDH1mt-glioma tissues. (B) Heatmap plot showing DEGs between high- and low-stromal group of IDH1mt-glioma tissues. (C) Volcano plot demonstrating the differential expression of genes in high- and low-immune group of IDH1mt-glioma tissues. (D) Heatmap plot showing DEGs between high- and low-immune group of IDH1mt-glioma tissues. (E) Overlapping upregulated genes between high-stromal and -immune group of IDH1mt-glioma tissues. (F) Overlapping downregulated genes between high-stromal and -immune group of IDH1mt-glioma tissues.
Figure 3
Figure 3
Landscape of 222 overlapping DEGs. (A) PPI network was constructed using 222 overlapping DEGs, while isolated genes were removed. Genes in PPI network were set as candidate hub genes. (B) Biological process (BP) analysis for candidate hub genes. (C) Molecular function (MF) analysis for candidate hub genes. (D) Cellular component (CC) analysis for candidate hub genes.
Figure 4
Figure 4
Hub genes selected to construct the risk model. (A, B) LASSO analysis for hub genes associated with the survival rate of IDH1mt-glioma patients. (C) Multivariate Cox regression analysis of HLA-DQA2, HOXA3, and SAA2. These three genes were used to construct the risk model.
Figure 5
Figure 5
Verification of the applicability of the risk model in IDH1mt-glioma patients in TCGA and CGGA databases. (A, B) IDH1mt-glioma patients in TCGA and CGGA databases were divided into high- and low-risk score groups according to the median of risk scores. (C) The survival difference between high- and low-risk score group IDH1mt-glioma patients in TCGA. (D, E) The diagnostic value of risk model for one- and three-year survival in IDH1mt-glioma patients in TCGA. (F) The survival difference between high- and low-risk score group IDH1mt-glioma patients in CGGA. (G, H) The diagnostic value of risk model for one- and three-year survival in IDH1-mt glioma patients in CGGA. (I, J) Death cases in high- and low-risk score group IDH1mt-glioma patients in TCGA and CGGA (Green dots mean alive cases, red dots mean death cases).
Figure 6
Figure 6
Verification of the applicability of the risk model in wtIDH1-glioma patients in TCGA and CGGA databases. (A, B) wtIDH1-glioma patients in TCGA and CGGA databases were divided into high- and low-risk score groups according to the median of risk scores. (C) The survival difference between high- and low-risk score group wtIDH1-glioma patients in TCGA. (D, E) The diagnostic value of risk model for one- and three-year survival in wtIDH1-glioma patients in TCGA. (F) The survival difference between high- and low-risk score group wtIDH1-glioma patients in CGGA. (G, H) The diagnostic value of the risk model for one- and three-year survival in wtIDH1-glioma patients in CGGA. (I, J) Death cases in high- and low-risk score group wtIDH1-glioma patients in TCGA and CGGA (Green dots mean alive cases, red dots mean death cases).
Figure 7
Figure 7
Construction of nomogram based on the signature risk score and clinical characteristics.
Figure 8
Figure 8
GSEA analysis of the pathway terms enriched in high-risk score IDH1mt-glioma tissues in TCGA (A) and CGGA (B).
Figure 9
Figure 9
Immune characteristics of the three immune signatures. (A, B) The gene expression profiles of the high- and low-risk score group IDH1mt-glioma tissues in TCGA and CGGA were converted into 22 immune cell expression matrices. (C, D) Difference in immune cells between high- and low-risk score group IDH1mt-glioma tissues in TCGA and CGGA.
Figure 10
Figure 10
Expression of HLA-DQA2, HOXA3, SAA2, CD86, and CD8 in IDH1mt-glioma tissues. IDH1mt-glioma tissues were divided into long- and short-term survival groups according to the patient’s number of days of survival with the cut-off as 15 months. (A) The IHC score of HLA-DQA2, HOXA3, and SAA2 in IDH1mt-glioma tissues in long- and short-term groups. (B) Representative figures of expression of HLA-DQA2, HOXA3, and SAA2 in long- and short-term group IDH1mt-glioma tissues. (C) Expression of CD86 and CD8 in long- and short-term group IDH1mt-glioma tissues. (D–F) The diagnostic value of HLA-DQA2, HOXA3, and SAA2 for distinguishing long- and short-term survival of IDH1mt-glioma patients. **P < 0.01.
Figure 11
Figure 11
Glioma patients with IDH1mt in high-risk group exhibit high responsiveness to ICB therapy. (A) Exclusion score of glioma patients with IDH1mt in high- and low-risk groups. (B) Dysregulation score of glioma patients with IDH1mt in high- and low-risk groups. (C) TIDE score of glioma patients with IDH1mt in high- and low-risk groups. (D) Responders and non-responders among glioma patients with IDH1mt in high- and low-risk groups. *P < 0.05; **P < 0.01.
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