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. 2022 Oct 25:13:1036460.
doi: 10.3389/fgene.2022.1036460. eCollection 2022.

Cuproptosis-related gene signature stratifies lower-grade glioma patients and predicts immune characteristics

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Cuproptosis-related gene signature stratifies lower-grade glioma patients and predicts immune characteristics

Zihao Zhang et al. Front Genet. .

Abstract

Cuproptosis is the most recently discovered type of regulated cell death and is mediated by copper ions. Studies show that cuproptosis plays a significant role in cancer development and progression. Lower-grade gliomas (LGGs) are slow-growing brain tumors. The majority of LGGs progress to high-grade glioma, which makes it difficult to predict the prognosis. However, the prognostic value of cuproptosis-related genes (CRGs) in LGG needs to be further explored. mRNA expression profiles and clinical data of LGG patients were collected from public sources for this study. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to build a multigene signature that could divide patients into different risk groups. The differences in clinical pathological characteristics, immune infiltration characteristics, and mutation status were evaluated in risk subgroups. In addition, drug sensitivity and immune checkpoint scores were estimated in risk subgroups to provide LGG patients with precision medication. We found that all CRGs were differentially expressed in LGG and normal tissues. Patients were divided into high- and low-risk groups based on the risk score of the CRG signature. Patients in the high-risk group had a considerably lower overall survival rate than those in the low-risk group. According to functional analysis, pathways related to the immune system were enriched, and the immune state differed across the two risk groups. Immune characteristic analysis showed that the immune cell proportion and immune scores were different in the different groups. High-risk group was characterized by low sensitivity to chemotherapy but high sensitivity to immune checkpoint inhibitors. The current study revealed that the novel CRG signature was related to the prognosis, clinicopathological features, immune characteristics, and treatment perference of LGG.

Keywords: cuproptosis-related gene; gene signature; immune score; lower-grade glioma; prognosis.

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Figures

FIGURE 1
FIGURE 1
Flow chart of data collection and analysis.
FIGURE 2
FIGURE 2
Identification of candidate cuproptosis-related genes in the TCGA cohort. (A) Cuproptosis-related genes were differentially expressed between glioma tissue and normal tissue. (B) Forest plots showing the results of the univariate Cox regression analysis of DEGs related to OS. (C) Heatmap showing the expression of cuproptosis-related prognostic genes in tumor and normal tissues. (D) The PPI network downloaded from the STRING database indicated the interactions among candidate genes. (E) The correlation network of candidate genes. The correlation coefficients are represented by different colors. Adjusted p values are shown as follows: ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
FIGURE 3
FIGURE 3
(A) The distribution and median value of the risk scores in the TCGA cohort. (B) PCA plot of the TCGA cohort. (C) t-SNE analysis of the TCGA cohort. (D) The distribution of OS status, OS, and risk score in TCGA. (E) Kaplan‒Meier curves for the OS of patients in the high-risk group and low-risk group in the TCGA cohort. (F) The AUC values of time-dependent ROC curves verified the prognostic performance of the risk score in the TCGA cohort.
FIGURE 4
FIGURE 4
(A) The distribution and median value of the risk scores in the CGGA cohort. (B) PCA plot of the CGGA cohort. (C) t-SNE analysis of the CGGA cohort. (D) The distribution of OS status, OS, and risk score in the CGGA. (E) Kaplan‒Meier curves for the OS of patients in the high-risk group and low-risk group in the CGGA cohort. (F) The AUCs of time-dependent ROC curves verified the prognostic performance of the risk score in the CGGA cohort.
FIGURE 5
FIGURE 5
Results of the univariate and multivariate Cox regression analyses regarding OS in the TCGA cohort (A) and the CGGA validation cohort (B).
FIGURE 6
FIGURE 6
Kaplan–Meier curve of stratified analyses of the CRG signature for associations with clinical characteristics in the TCGA cohort. (A) Risk score between grade 2 and grade 3 stage patients. (B) Risk score between IDH mutation and IDH wild-type patients. (C) Risk score between 1q/19p non-codeletion and 1q/19p codeletion patients. (D) OS curve in grade 2 patients. (E) OS curve in grade 3 patients. (F) OS curve in IDH mutation patients. (G) OS curve in IDH wild-type patients. (H) OS curve in 1q/19p non-codeletion patients. Adjusted p values are shown as follows: ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
FIGURE 7
FIGURE 7
Kaplan–Meier curve of stratified analyses of the CRG signature for associations with clinical characteristics in the CGGA cohort. (A) Risk score between grade 2 and grade 3 stage patients. (B) Risk score between IDH mutation and IDH wild-type patients. (C) Risk score between 1q/19p non-codeletion and 1q/19p codeletion patients. (D) OS curve in grade 2 patients. (E) OS curve in grade 3 patients. (F) OS curve in IDH mutation patients. (G) OS curve in IDH wild-type patients. (H) OS curve in 1q/19p non-codeletion patients. Adjusted p values are shown as follows: ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
FIGURE 8
FIGURE 8
Functional analysis of DEGs between the high- and low-risk groups in the TCGA cohort. (A and B) Bubble graph for GO analysis. (C) Bar plot for KEGG pathways. (D) GSEA.
FIGURE 9
FIGURE 9
Functional analysis of DEGs between the high- and low-risk groups in the CGGA cohort. (A and B) Bubble graph for GO analysis. (C) Bar plot for KEGG pathways. (D) GSEA.
FIGURE 10
FIGURE 10
The correlations between the expression of 6 CRGs and the levels of immune cells from the TIMER database. (A) MTF1 and immune cells; (B) FDX1 and immune cells; (C) LIAS and immune cells; (D) DLD and immune cells; (E) DLAT and immune cells; (F) PDHB and immune cells.
FIGURE 11
FIGURE 11
Immune characteristics analysis in the CGGA cohort. (A and B) ssGSEA scores of 13 immune-related functions and scores of 16 immune cells between the high- and low-risk groups. (C and D) Immune cell infiltration in the high- and low-risk groups using CIBERSORT. (E–G) Immune score, stromal score, and combined score estimated using ESTIMATE. Adjusted p values are shown as follows: ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
FIGURE 12
FIGURE 12
The correlation between different risk groups and drug sensitivity in LGG patients. (A) temozolomide, (B) dabrafenib, (C) cyclophosphamide, (D) oxaliplatin, (E) tamoxifen, (F) sorafenib, (G) lapatinib, (H) gefitinib, and (I) erlotinib. Adjusted p values are shown as follows: ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
FIGURE 13
FIGURE 13
Immunotherapy response of LGG patients. (A) Differences in immune checkpoint gene expression between patients in the high- and low-risk groups. (B–D) TIDE, dysfunction and exclusion scores between patients in the high- and low-risk groups. Adjusted p values are shown as follows: ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001.

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