Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas

Abstract

Background: Cuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown.

Methods: In this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB).

Results: Two distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients' prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity-related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB.

Conclusion: Collectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients' therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.

Keywords: chemoradiotherapy; cuproptosis; immune checkpoint inhibitors; lower-grade gliomas; molecular subtypes; tumor microenvironment.

Figure 1

Flow chart of this study. *P< 0.05; **P< 0.01; ***P< 0.001.

Figure 2

Landscape of genetic and transcriptional variations of cuproptosis-related genes (CRGs) in lower-grade glioma (LGG). (A, B) Summary of variation across 506 lower-grade glioma (LGG) patients including the variant classification, variant type, single-nucleotide variant (SNV) class, variants per sample, and top 10 mutated genes. (C, D) Landscape of genetic variations of 506 LGG patients in The Cancer Genome Atlas (TCGA) cohort. (E) Copy number variation (CNV) amplifications and deletions of CRGs in LGG patients. (F) The circus plot showed the location of CNV alteration of CRGs on 23 chromosomes. (G) Differences in the expression levels of 13 CRGs between tumor and normal samples. (tumor, red; normal, blue) P-values were shown as: ***P< 0.001. (H) The network showed interactions among CRGs in LGG. LGG, lower-grade glioma (WHO II and III); TCGA, the Cancer Genome Atlas; SNV, single-nucleotide variant; CNV, copy number variation; CRGs, cuproptosis-related genes.

Figure 3

Correlations of CRG clusters with clinical features, CRSGs, immune checkpoint genes (ICGs), and tumor microenvironment (TME). (A) The heatmap showed the different expressions of CRGs and clinicopathological characteristics between CRG cluster A and (B)(B) Landmark survival analysis for two CRG clusters. The overall survival probability of LGG patients in the two CRG clusters was calculated by Kaplan–Meier analysis (log-rank tests). A landmark time of 9 years was set. (C) The heatmap showed the different expressions of ICGs and clinicopathological characteristics between CRG cluster A and B (D) Differences in the expression levels of seven chemoradiotherapy sensitivity–related genes (CRSGs) between CRG cluster A and B(E) Correlations between the two CRG clusters and TME scores. (F) Differences in the abundance of infiltrating immune cells between CRG cluster A and B (CRG cluster A, blue; CRG cluster B, red) P values were shown as: *P< 0.05; **P< 0.01; ***P< 0.001. CRGs, cuproptosis-related genes; CRSGs, chemoradiotherapy sensitivity–related genes; ICGs, immune checkpoint genes; TME, tumor microenvironment; LGG, lower-grade glioma (WHO II and III).

Figure 4

Functional enrichment analysis and identification of differentially expressed genes (DEGs) between CRG clusters. (A) Gene set variation analysis (GSVA) of gene ontology biological process (GOBP) terms between CRG cluster A and B, in which red and blue represent activated and inhibited, respectively. (B) GSVA of Kyoto Encyclopedia of Genes and Genomes (KEGG) terms between CRG cluster A and B, in which red and blue represent activated and inhibited, respectively. (C-E) Gene set enrichment analysis (GSEA) of significant HALLMARK enriched in CRG cluster (A) (F–H) GO and KEGG enrichment analyses of DEGs between two CRG clusters. DEGs, differentially expressed genes; GSVA, gene set variation analysis; GOBP, gene ontology biological process; GSEA, gene set enrichment analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 5

Identification of cuproptosis gene clusters and construction of the cuproptosis-related prognostic model in LGG. (A) The heatmap showed the different expressions of overall survival (OS)–DEGs and clinicopathological characteristics among gene cluster A to C (B) Kaplan–Meier OS curves for patients in the three gene clusters (log-rank test). (C) Differences in the expression levels of 13 CRGs among gene cluster A to C. (D) Differences in CRG scores between CRG cluster A and B (E) Differences in the expression levels of 13 CRGs between high- and low-risk groups. (F) Differences in CRG scores among gene cluster A to C, (G) Sankey diagram of subtype distributions in groups with different CRG scores and survival outcomes. P-values were shown as: *P< 0.05; **P< 0.01; ***P< 0.001. LGG, lower-grade glioma (WHO II and III); OS, overall survival; DEGs, differentially expressed genes; CRGs, cuproptosis-related genes.

Figure 6

Evaluation of the cuproptosis-related prognostic model in the TCGA cohort. (A) The heatmap showed the different expressions of genes in the cuproptosis-related prognostic model between the high- and low-risk groups in TCGA training and test cohorts. (B) Distribution of the CRG score in TCGA training and testing cohorts. (C) The risk point plot showed the patterns of the survival time and survival status between the high- and low-risk groups in TCGA training and test cohorts. (D) The Kaplan–Meier OS curves for patients in the high- and low-risk groups in the TCGA training cohort (log-rank test). (E) The Kaplan–Meier OS curves for patients in the high- and low-risk groups in the TCGA testing cohort (log-rank test). (F) ROC curves showed the prognostic performance of the cuproptosis-related prognostic model in TCGA training and testing cohorts. TCGA, the Cancer Genome Atlas; CRGs, CRGs, cuproptosis-related genes; OS, overall survival.

Figure 7

Validation of the cuproptosis-related prognostic model in CGGA cohorts. (A) The Kaplan–Meier OS curves for patients in the high- and low-risk groups in the CGGA1 cohort. (B) The Kaplan–Meier OS curves for patients in the high- and low-risk groups in the CGGA2 cohort. (C) ROC curves showed the prognostic performance of the cuproptosis-related prognostic model in the CGGA1 cohort. (D) ROC curves showed the prognostic performance of the cuproptosis-related prognostic model in the CGGA2 cohort. (E–G) The Kaplan–Meier OS curves among four groups classified by the CRG score and treatment with radiotherapy in CGGA1 and CGGA2 cohorts. (G–H) The Kaplan–Meier OS curves among four groups classified by the CRG score and treatment with TMZ in CGGA1 and CGGA2 cohorts. CGGA, Chinese Glioma Genome Atlas; ROC, receiver operating characteristic; OS, overall survival; TMZ, temozolomide; CRGs, cuproptosis-related genes.

Figure 8

Correlations of the CRG score with immune infiltration and the cancer stem cell (CSC) index in LGG. (A) GSEA of significant GOBP terms enriched in the high-risk group. (B) GSEA of significant HALLMARK terms enriched in the high-risk group. (C) Correlations between CRG scores and TME scores. (D) Correlations between the abundance of immune cells and five genes in the cuproptosis-related prognostic model. (E–K) Correlations between the abundance of immune cells and the CRG score. (L) Correlations between the CSC index and the CRG score. P-values were shown as: *P< 0.05; **P< 0.01; ***P< 0.001. CRGs, cuproptosis-related genes; CSC, cancer stem cell; LGG, lower-grade glioma (WHO II and III); GSEA, gene set enrichment analysis; GOBP, gene ontology biological process; TME, tumor microenvironment.

Figure 9

Correlations of the CRG score with TMB and ICGs in LGG. (A, B) The mutational landscape of LGG patients in high- and low-risk groups. (C) Correlations between the TMB and the CRG score in different gene clusters. (D) Difference in the TMB scores between high- and low-risk groups. (E) Correlations between the expression of ICGs and five genes in the cuproptosis-related prognostic model. (F) Correlations between the expression of ICGs and the CRG score. P-values were shown as: *P< 0.05; **P< 0.01; ***P< 0.001. CRGs, cuproptosis-related genes; TMB, tumor mutation burden; ICGs, immune checkpoint genes; LGG, lower-grade glioma (WHO II and III).

Figure 10

Estimation of the cuproptosis-related prognostic model in immunotherapy response in LGG. (A) Difference in TIDE scores between high- and low-risk groups. (B) Difference in dysfunction scores between high- and low-risk groups. (C) Difference in exclusion scores between high- and low-risk groups. (D) Difference in CRG scores between responder and no responder groups based on the TIDE algorithm. (E) The distribution of immunotherapy response in indicated groups stratified by the CRG scores based on the TIDE algorithm. (F) The Kaplan–Meier OS curves among four groups classified by the CRG score and TIDE score. TIDE, tumor immune dysfunction and exclusion; CRG, cuproptosis-related genes; OS, overall survival.

Figure 11

Correlation of the CRG score with immunotherapy response in mutiple cohorts. The distribution of immunotherapy response in indicated groups stratified by CRG scores in (A) GSE126044, (B) GSE78220, (C) the CheckMate cohort, and (D) the IMvigor210 cohort. (E) Difference in CRG scores among four immunotherapy response groups in the IMvigor210 cohort. (F) Sankey diagram of subtype distributions in groups with different CRG scores and immunotherapy response in the IMvigor210 cohort. Differences in the CRG score among (G) three immune phenotypes, (H) three TC levels, and (I) three immune cell (IC) levels in the IMvigor210 cohort. CRGs, cuproptosis-related genes; TC, tumor cell; IC, immune cell.

Figure 12

Correlations of the CRG score with CRSGs and chemotherapeutic sensitivity in LGG. (A) Correlations between the expression of CRSGs and five genes in the cuproptosis-related prognostic model. (B–G) Correlations between the expression of CRSGs and the CRG score. (H) Correlations between the imputed sensitivity score of TMZ and the CRG score. (I–L) Difference in chemotherapeutic sensitivity between high- and low-risk groups. P-values were shown as: *P< 0.05; **P< 0.01; ***P< 0.001. CRSGs, chemoradiotherapy sensitivity–related genes; CRGs, cuproptosis-related genes; TMZ, temozolomide.

Figure 13

Independent prognostic analysis and establishment of a nomogram. (A) Univariate Cox regression analysis of the CRG score and clinical characteristics in the TCGA cohort. (B) Multivariate Cox regression analysis of the CRG score and clinical characteristics in the TCGA cohort. (C) The nomogram was extablished to predict 1-, 3-, and 5-year overall survival probability of LGG patients in the TCGA cohort. (D) ROC curves showed the prognostic performance of the model in the TCGA cohort. (E) The calibration curves measured the relationship between the outcomes predicted by the model and the observed outcomes in the TCGA cohort. (F) ROC curves showed the prognostic performance of the model in the CGGA1 cohort. (G) The calibration curves measured the relationship between the outcomes predicted by the model and the observed outcomes in the CGGA1 cohort. (H) ROC curves showed the prognostic performance of the model in the CGGA2 cohort. (I) The calibration curves measured the relationship between the outcomes predicted by the model and the observed outcomes in the CGGA2 cohort. CRGs, cuproptosis-related genes; TCGA, the Cancer Genome Atlas; LGG, lower-grade glioma (WHO II and III); ROC, receiver operating characteristic; CGGA, Chinese Glioma Genome Atlas.

Figure 14

The expressions of five signature genes were validated by quantitative real-time PCR (qRT-PCR) and Western blotting (WB). (A–E) Expression of genes at the mRNA level by qRT-PCR. (F) Expression of genes at the protein level by WB. qRT-PCR, quantitative real-time PCR; WB, Western blotting. *P< 0.05; **P< 0.01; ***P< 0.001. ns, no significance.