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. 2019 Nov 22:9:1310.
doi: 10.3389/fonc.2019.01310. eCollection 2019.

Development and Validation of an IDH1-Associated Immune Prognostic Signature for Diffuse Lower-Grade Glioma

Xiangyang Deng  1 , Dongdong Lin  1 , Bo Chen  2 , Xiaojia Zhang  1 , Xingxing Xu  3 , Zelin Yang  1 , Xuchao Shen  1 , Liang Yang  1 , Xiangqi Lu  1 , Hansong Sheng  1 , Bo Yin  1 , Nu Zhang  1 , Jian Lin  1
Affiliations

Development and Validation of an IDH1-Associated Immune Prognostic Signature for Diffuse Lower-Grade Glioma

Xiangyang Deng et al. Front Oncol. .

Abstract

A mutation in the isocitrate dehydrogenase 1 (IDH1) gene is the most common mutation in diffuse lower-grade gliomas (LGGs), and it is significantly related to the prognosis of LGGs. We aimed to explore the influence of the IDH1 mutation on the immune microenvironment and develop an IDH1-associated immune prognostic signature (IPS) for predicting prognosis in LGGs. IDH1 mutation status and RNA expression were investigated in two different public cohorts. To develop an IPS, LASSO Cox analysis was conducted for immune-related genes that were differentially expressed between IDH1wt and IDH1mut LGG patients. Then, we systematically analyzed the influence of the IPS on the immune microenvironment. A total of 41 immune prognostic genes were identified based on the IDH1 mutation status. A four-gene IPS was established and LGG patients were effectively stratified into low- and high-risk groups in both the training and validation sets. Stratification analysis and multivariate Cox analysis revealed that the IPS was an independent prognostic factor. We also found that high-risk LGG patients had higher levels of infiltrating B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells, and expressed higher levels of CTLA-4, PD-1 and TIM-3. Moreover, a novel nomogram model was established to estimate the overall survival in LGG patients. The current study provides novel insights into the LGG immune microenvironment and potential immunotherapies. The proposed IPS is a clinically promising biomarker that can be used to classify LGG patients into subgroups with distinct outcomes and immunophenotypes, with the potential to facilitate individualized management and improve prognosis.

Keywords: IDH1; immune prognostic signature; lower-grade glioma; mutation; nomogram.

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Figures

Figure 1
Figure 1
Identified IDH1-associated immune genes. (A) Genomic landscape of LGG and the mutational signatures in the TCGA dataset, which were assayed on the FireBrowse platform. (B) Volcano plot of 984 genes differentially expressed between IDH1wt and IDH1mut patients. (C) Heatmap of genes differentially expressed between IDH1wt and IDH1mut patients. (D) Heatmap of immune genes differentially expressed between IDH1wt and IDH1mut patients.
Figure 2
Figure 2
Functional analysis of 88 IDH1-associated immune genes. (A) Heatmap of enriched terms across input gene lists, colored by P-values. Network of enriched terms: (B) colored by cluster ID, where nodes that share the same cluster ID are typically close to each other; (C) colored by p-value, where terms containing more genes tend to have a more significant P-value.
Figure 3
Figure 3
Construction and validation of the immune prognostic signature. (A,B) LASSO Cox analysis identified four genes most correlated to overall survival in TCGA set. (C) Coefficient values for each of the four selected genes. (D,G) Kaplan–Meier curves of overall survival for LGG patients based on the IPS in TCGA cohort and CGGA cohort. (E,H) Risk scores distribution, survival status of each patient, and heatmaps of prognostic four-gene signature in TCGA and CGGA cohorts. (F,I) Time-dependent ROC curve analysis of the IPS.
Figure 4
Figure 4
Stratification analysis. The Kaplan–Meier analysis of the IPS grouping according to patients with (A) IDH1 mutant, (B) IDH1 wildtype, (C) grade II, (D) grade III, (E) > 41 years, (F) ≤ 41 years, (G) male, and (H) female. The risk score was group by (I) age, tumor grade (J), and (K) sex.
Figure 5
Figure 5
Different immune phenotypes between high- and low-risk groups in TCGA cohort. (A) Principal components analysis of IDH1-associated immune genes between high- and low-risk groups. Blue color indicates low-risk patients, and red color represents high-risk patients. (B,C) Gene set enrichment analysis for comparing immune phenotype between high- and low-risk groups. Significant enrichment of five immune-related GO terms in high-risk group. FDR, false discovery rate; NES, normalized enrichment score.
Figure 6
Figure 6
Correlations of the IPS with infiltrating immune cell proportions and immune checkpoints. (A) Correlation of the risk score with infiltrating immune cell proportions. Pearson's correlation coefficient values with the level of significance were shown on the top of the diagonal. ***P < 0.001. (B) Violin plots visualizing significantly different immune cell proportions between high- and low-risk patients. (C) Correlation of the risk score with the expression of several crucial immune checkpoints (D) Violin plots visualizing significantly different immune checkpoints between high- and low-risk patients.
Figure 7
Figure 7
Functional analysis of 41 risk score-associated genes. (A) Heatmap of IDH1-associated immune genes that were differentially expressed between patients with high- and low-risk scores. (B) Heatmap of enriched terms across input gene lists, colored by P-values. Network of enriched terms: (C) colored by cluster ID, where nodes that share the same cluster ID are typically close to each other; (D) colored by P-value, where terms containing more genes tend to have a more significant P-value.
Figure 8
Figure 8
Construction and validation of the nomogram model. (A) Univariate and multivariate Cox analyses indicated that IPS was significantly associated with OS in both TCGA and CGGA sets. Red indicates statistical significance, and blue indicates no statistical significance. (B) Nomogram model for predicting the probability of 1-, 3-, and 5-year OS in LGGs. (C,D) Calibration plots of the nomogram for predicting the probability of OS at 1, 3, and 5 years in TCGA and CGGA cohorts. (E,F) Time-dependent ROC curve analyses of the nomogram model, risk signature, age and tumor grade in TCGA cohort. (G,H) Decision curves of the nomogram predicting 3- and 5-year OS in TCGA cohort.
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