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. 2022 Aug 30:13:998236.
doi: 10.3389/fimmu.2022.998236. eCollection 2022.

The cuproptosis-related signature associated with the tumor environment and prognosis of patients with glioma

Weichen Wang  1 , Zhichao Lu  1 , Maoyu Wang  1 , Zongheng Liu  1 , Bing Wu  1 , Chengkai Yang  1 , He Huan  1 , Peipei Gong  1
Affiliations

The cuproptosis-related signature associated with the tumor environment and prognosis of patients with glioma

Weichen Wang et al. Front Immunol. .

Abstract

Background: Copper ions are essential for cellular physiology. Cuproptosis is a novel method of copper-dependent cell death, and the cuproptosis-based signature for glioma remains less studied.

Methods: Several glioma datasets with clinicopathological information were collected from TCGA, GEO and CGGA. Robust Multichip Average (RMA) algorithm was used for background correction and normalization, cuproptosis-related genes (CRGs) were then collected. The TCGA-glioma cohort was clustered using ConsensusClusterPlus. Univariate Cox regression analysis and the Random Survival Forest model were performed on the differentially expressed genes to identify prognostic genes. The cuproptosis-signature was constructed by calculating CuproptosisScore using Multivariate Cox regression analysis. Differences in terms of genomic mutation, tumor microenvironment, and enrichment pathways were evaluated between high- or low-CuproptosisScore. Furthermore, drug response prediction was carried out utilizing pRRophetic.

Results: Two subclusters based on CRGs were identified. Patients in cluster2 had better clinical outcomes. The cuproptosis-signature was constructed based on CuproptosisScore. Patients with higher CuproptosisScore had higher WHO grades and worse prognosis, while patients with lower grades were more likely to develop IDH mutations or MGMT methylation. Univariate and Multivariate Cox regression analysis demonstrated CuproptosisScore was an independent prognostic factor. The accuracy of the signature in prognostic prediction was further confirmed in 11 external validation datasets. In groups with high-CuproptosisScore, PIK3CA, MUC16, NF1, TTN, TP53, PTEN, and EGFR showed high mutation frequency. IDH1, TP53, ATRX, CIC, and FUBP1 demonstrated high mutation frequency in low-CuproptosisScore group. The level of immune infiltration increased as CuproptosisScore increased. SubMap analysis revealed patients with high-CuproptosisScore may respond to anti-PD-1 therapy. The IC50 values of Bexarotene, Bicalutamide, Bortezomib, and Cytarabine were lower in the high-CuproptosisScore group than those in the low-CuproptosisScore group. Finally, the importance of IGFBP2 in TCGA-glioma cohort was confirmed.

Conclusion: The current study revealed the novel cuproptosis-based signature might help predict the prognosis, biological features, and appropriate treatment for patients with glioma.

Keywords: bioinformatics; clusters; cuproptosis; glioma; 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
Characteristics of CuproptosisCluster in TCGA-glioma cohort. (A) The correlations among the ten cuproptosis-related genes (CRGs). The color represents the correlation coefficient. (B) Cluster diagram for subtype analysis of glioma samples. The intragroup correlations were the highest and the inter-group correlations were low when k=2. (C) PCA analysis for the two subclusters. (D) Kaplan-Meier survival curve showing survival probability of cluster1 and cluster2. (E) Heatmap showing the expression levels of the ten cuproptosis-related genes (CRGs) in different clinical features and clusters. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Figure 2
Figure 2
Establishment of cuproptosis-signature. (A) The volcano map reflects the differentially expressed genes identified (logFC > 1, P < 0.05). (B) The forest figure for Univariate Cox regression analysis of the differentially expressed genes. (C) The distribution of error rates in Random Survival Forest model. (D) The variable relative importance of the seven genes. (E) Heat map showing the relationship between six genes in the cuproptosis-signature and CuproptosisScore distribution and its clinical characteristics. ****P < 0.0001.
Figure 3
Figure 3
Prognostic potential of cuproptosis-signature. (A) The violin figures for comparing the CuproptosisScore of TCGA patients among WHO grades, mutation status and MGMT methylation status. (B) Kaplan-Meier survival curve showing survival probability of high-CuproptosisScore or low-CuproptosisScore subgroups. (C) The forest figure for Univariate or Multivariate Cox regression analysis of CuproptosisScore and clinicopathologic features. (D) The 1-year, 2-year, 3-year, 4-year, and 5-year survival ROC curves are predicted by the cuproptosis-signature. (E) Univariate Cox regression analysis of the cuproptosis-signature in 11 external validation data sets. ***P < 0.001.
Figure 4
Figure 4
Genomic mutation analysis for cuproptosis-signature. (A) Genomic characterization landscape of high-CuproptosisScore or low-CuproptosisScore subgroups. (B) Gene mutation frequency in high-CuproptosisScore. (C) Gene mutation frequency in low-CuproptosisScore.
Figure 5
Figure 5
Immune status for cuproptosis-signature. (A) The heatmap shows the abundance of infiltrating immune cell populations at different CuproptosisScores. (B–D) Glioma patients with high CuproptosisScores had higher levels of TMB (B), GEP (C), and CYT (D). (E) The heatmap shows CuproptosisScores, clinical features, and immune-related pathways based on GSVA analysis. ****P < 0.0001.
Figure 6
Figure 6
Immunotherapy and chemotherapy of cuproptosis-signature. (A) Correlation of CuproptosisScore with seven immunomodulators in gliomas. (B) SubMap analysis for cuproptosis-signature in gliomas. (C) Box plots of estimated IC50 for several chemotherapeutic agents in the high- or low-CuproptosisScore groups. *P < 0.05; **P < 0.01; ***P < 0.001, ****P < 0.0001.
Figure 7
Figure 7
(A) WB for IGFBP2 in 3 pairs patients from Nantong cohort. (B) Represented IHC for IGFBP2 in three parents with different WHO stage from Nantong cohort. (C) Boxplot of IHC for IGFBP2 in six pairs parents from Nantong cohort. (D) The expression level of IGFBP2 in glioma sample and the control normal sample. (E) Kaplan-Meier survival curve showing survival probability of high- or low-expression IGFBP2. (F) The 1-year, 2-year, 3-year, 4-year, and 5-year survival ROC curves are predicted by the expression of IGFBP2. (G) The heat map shows the correlation between IGFBP2 and eight immune checkpoints in TCGA. (H) GSEA maps of cancer and immune-related signaling pathways positively modulated by IGFBP2. ∗∗P < 0.01; ∗∗∗P < 0.001; ∗∗∗∗P < 0.0001.
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