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. 2022 Feb 25:13:655169.
doi: 10.3389/fgene.2022.655169. eCollection 2022.

Establishment and Validation of a 5 m6A RNA Methylation Regulatory Gene Prognostic Model in Low-Grade Glioma (V体育2025版)

Zhiqun Bai  1 , Xuemei Wang  1 , Zhen Zhang  1
Affiliations

Establishment and Validation of a 5 m6A RNA Methylation Regulatory Gene Prognostic Model in Low-Grade Glioma

Zhiqun Bai et al. Front Genet. .

Abstract

Background: The prognosis of low-grade glioma (LGG) is different from that of other intracranial tumors. Although many markers of LGG have been established, few are used in clinical practice. M6A methylation significantly affects the biological behavior of LGG tumors. Therefore, establishment of an LGG prognostic model based on m6A methylation regulatory genes is of great interest. Methods: Data from 495 patients from The Cancer Genome Atlas (TCGA) and 172 patients from the Chinese Glioma Genome Atlas (CGGA) were analyzed. Univariate Cox analysis was used to identify methylation regulatory genes with prognostic significance. LASSO Cox regression was used to identify prognostic genes. Receiver operating characteristic (ROC) and Kaplan-Meier curves were used to verify the accuracy of the model. Gene Set Enrichment Analysis (GSEA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify cellular pathways that were significantly associated with the prognosis of LGG. Results: A glioma prognostic model based on five methylation regulatory genes was established. Compared with low-risk patients, patients identified as high risk had a poorer prognosis VSports手机版. There was a high degree of consistency between the internal training and internal validation CGGA cohorts and the external validation TCGA cohort. Furthermore, KEGG and GSEA analyses showed that the focal adhesion and cell cycle pathways were significantly upregulated in high-risk patients. This signature could be used to distinguish among patients with different immune checkpoint gene expression levels, which may inform immune checkpoint inhibitor (ICI) immunotherapy. Conclusion: We comprehensively evaluated m6A methylation regulatory genes in LGG and constructed a prognostic model based on m6A methylation, which may improve prognostic prediction for patients with LGG. .

Keywords: ICI; LASSO; LGG; M6A RNA methylation; prognostic model. V体育安卓版.

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"V体育官网入口" 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
Flow chart of the analysis methods utilized in the current study.
FIGURE 2
FIGURE 2
m6A-related gene profile in the CGGA cohort. (A) Unsupervised clustering of patients with LGG from the CGGA cohort using 26 m6A methylation regulatory genes. The red cube represents highly expressed genes, and the blue cube represents genes with lower expression levels. (B) Correlation of the 26 m6A methylation regulatory genes. (C) Forest plots showing associations between different m6A methylation regulatory genes and OS in the internal training CGGA cohort.
FIGURE 3
FIGURE 3
Establishment of a 5-gene prognostic model. (A) LASSO coefficient profiles of the fractions of immune cells. The minimum lambda value was reached when the number of genes was 5. (B) Parameter selection for tuning by 10-fold cross validation in the LASSO model. (C–E) Kaplan–Meier curve for patients with high and low risk in the internal training CGGA cohort, internal validation CGGA cohort, and external validation TCGA cohort, respectively. (F–H) Risk score measured using time-dependent receiver operating characteristic (ROC) curves in the internal training cohort, internal validation CGGA cohort, and external validation TCGA cohort at 1, 3, and 5 years, respectively.
FIGURE 4
FIGURE 4
Association between the 5-gene signature and clinicopathological parameters. (A–C) 5-gene signature-based risk score in the CGGA cohort. (A) Risk score plot based on the 5-gene signature. (B) Live/dead state corresponding to the risk score in the upper panel. (C) Z-score-transformed expression value of each gene in the 5-gene signature. (D) Correlation analysis of the 5 methylation regulatory genes in the signature. (E–L) Kaplan–Meier curve showed significant statistical differences in overall survival between the high- and the low-risk groups regardless of gender (E,F), WHO grade (G,H), IDH mutation status (I,J), and co-mutation state of chromosomes 1p and 19q (K,L).
FIGURE 5
FIGURE 5
Pathway enrichment analysis. (A) Spearman correlation for PRI top 1,000 genes was used for KEGG analysis. These genes were enriched in KEGG pathways “cell cycle” and “focal adhesion.” (B) GSEA terms significantly enriched in the CGGA cohort. “KEGG_CELL_CYCLE,” “KEGG_APOPTOSIS,” “KEGG_JAK_STAT_SIGNALING_PATHWAY,” and “KEGG_T_CELL_RECEPTOR_SINGALING_PATHWAY” were significantly enriched. (C) Hierarchical clustering of gene expression profiles for each KEGG pathway. (D) Chord plots show the relationship between genes and the KEGG pathway.
FIGURE 6
FIGURE 6
Association between the 5-gene signature and immune checkpoint genes. (A) Comparison of the expression pattern of immune checkpoint genes (PD-1, PD-L1, and CTLA-4) between patients with different risk scores in the CGGA analysis. (B) Kaplan–Meier survival curves of overall survival among four patient groups stratified by the risk score and PD-1 (B), PD-L1 (C), and CTLA-4 (D).
FIGURE 7
FIGURE 7
Construction and validation of a 5-gene signature prognostic nomogram. (A) Prognostic nomogram based on the 5-gene signature, WHO grade, gender, and X1p19q codeletion status. (B,C) Calibration curve at 3 and 5 years.
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