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. 2024;40(2):185-198.
doi: 10.3233/CBM-230341.

Cuproptosis-related gene-located DNA methylation in lower-grade glioma: Prognosis and tumor microenvironment

Liucun Zhu  1   1 , Fa Yuan  1   1 , Xue Wang  2 , Rui Zhu  1   3 , Wenna Guo  2
Affiliations

Cuproptosis-related gene-located DNA methylation in lower-grade glioma: Prognosis and tumor microenvironment

Liucun Zhu et al. Cancer Biomark. 2024.

Abstract

Cuproptosis a novel copper-dependent cell death modality, plays a crucial part in the oncogenesis, progression and prognosis of tumors. However, the relationships among DNA-methylation located in cuproptosis-related genes (CRGs), overall survival (OS) and the tumor microenvironment remain undefined. In this study, we systematically assessed the prognostic value of CRG-located DNA-methylation for lower-grade glioma (LGG). Clinical and molecular data were sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We employed Cox hazard regression to examine the associations between CRG-located DNA-methylation and OS, leading to the development of a prognostic signature. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses were utilized to gauge the accuracy of the signature. Gene Set Enrichment Analysis (GSEA) was applied to uncover potential biological functions of differentially expressed genes between high- and low-risk groups. A three CRG-located DNA-methylation prognostic signature was established based on TCGA database and validated in GEO dataset. The 1-year, 3-year, and 5-year area under the curve (AUC) of ROC curves in the TCGA dataset were 0.884, 0.888, and 0.859 while those in the GEO dataset were 0.943, 0.761 and 0.725, respectively. Cox-regression-analyses revealed the risk signature as an independent risk factor for LGG patients. Immunogenomic profiling suggested that the signature was associated with immune infiltration level and immune checkpoints. Functional enrichment analysis indicated differential enrichment in cell differentiation in the hindbrain, ECM receptor interactions, glycolysis and reactive oxygen species pathway across different groups. We developed and verified a novel CRG-located DNA-methylation signature to predict the prognosis in LGG patients. Our findings emphasize the potential clinical implications of CRG-located DNA-methylation indicating that it may serve as a promising therapeutic target for LGG patients.

Keywords: DNA-methylation; Lower-grade glioma; cuproptosis; immune checkpoint inhibitors; tumor microenvironment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1.
Figure 1.
Correlation between the signature and OS in the TCGA cohort. (A) The distribution of risk score. (B) The distribution of OS and survival status of each patient as risk score increases. Patients were divided into high- and low-risk groups using the median score as the cut-off value. (C) The distribution of OS and survival status of each patient as DNA methlation level increases. (D) DNA methylation levels of three DNA methylation sites in patients with short (OS < 3 years) and long survival (OS > 3 years). The thick line represents the median, the bottom and top of the box are the 25th and 75th percentiles (interquartile range), and the whiskers indicated the min and max. Differences between short and long survival groups were compared by the Mann-Whitney U test.
Figure 2.
Figure 2.
Kaplan-Meier and ROC curves of the CRG-located DNA-methylation signature in the discovery (A-B) and validation sets (C-D). Kaplan-Meier analysis combined with Wilcoxon test was used to visualize and compare the OS in low- and high-risk groups. AUC values for the 1, 3, and 5-year survival to show the prognostic performance of the signature.
Figure 3.
Figure 3.
Association between the CRG-located DNA-methylation signature and clinical factors in the TCGA set. (A–F) The constituent ratio of clinical factors in the low- or high-risk group, and distribution of risk-score in different groups based on pathological characteristics. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05. (G–L) Kaplan-Meier curves of LGG patients stratified by clinical factors.
Figure 4.
Figure 4.
The Cox regression (A-B) of risk scores and clinical characteristics. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05. (C) The nomogram for OS prediction, which consisted of age, grade, IDH1 mutation status, histologic subtype and the riskscore.
Figure 5.
Figure 5.
Estimated infiltration level of tumor-infiltrating leukocytes (TILs) in individuals based on the DNA-methylation signature. (A) Correlation between signature and immune infiltration. (B-C) The differences in various immune score and infiltration levels of 22 immune cells in different groups. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.
Figure 6.
Figure 6.
Correlation analysis of the signature. (A) Correlation of CRG-located DNA-methylation signature with ICGs. The colors above represent the Spearman’s correlation coefficients, and the colors below represent P-values. (B) The expressions of ICGs in different groups.
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