Identification of Critical m6A RNA Methylation Regulators with Prognostic Value in Lower-Grade Glioma

Abstract

Increasing evidences have revealed that N⁶-methyladenosine (m⁶A) RNA methylation regulators participate in the tumorigenesis and development of multiple tumors. So far, there has been little comprehension about the effects of m⁶A RNA methylation regulators on lower-grade gliomas (LGG). Here, we systematically investigated the expression profiles and prognostic significance of 36 m⁶A RNA methylation regulators in LGG patients from the TCGA and CGGA databases. Most of the m⁶A RNA methylation regulators are differentially expressed in LGG tissues as compared with normal brain tissues and glioblastoma (GBM) tissues. The consensus clustering for these m⁶A RNA methylation regulators identified three clusters. Patients in cluster 3 exhibited worse prognosis. In addition, we constructed an m⁶A-related prognostic signature, which exhibited excellent performance in prognostic stratification of LGG patients according to the results of the Kaplan-Meier curves, ROC curves, and univariate and multivariate Cox regression analyses. In addition, a significant correlation was observed between the m⁶A-related prognostic signature and the immune landscape of the LGG microenvironment. The high-risk group exhibited higher immune scores, stromal scores, and ESTIMATE scores but lower tumor purity and lower abundance of activated NK cells. Moreover, the expression level of immune checkpoints was positively correlated with the risk score. To conclude, the current research systematically demonstrated the prognostic roles of m⁶A RNA methylation regulators in LGG.

Figure 1

The expression profiles of m⁶A RNA methylation regulators across tissues. (a) The expression comparison of m⁶A RNA methylation regulators between LGG and normal tissues. (b) The expression comparison of m⁶A RNA methylation regulators between LGG and GBM tissues. (c) The expression comparison of m⁶A RNA methylation regulators between WHO grade II and III. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

Figure 2

Interaction among m⁶A RNA methylation regulators and consensus clustering of LGG patients in the TCGA cohort. (a) Protein-protein interaction (PPI) network of m⁶A RNA methylation regulators. Elements not connected to others are hidden. (b) Correlation of m⁶A RNA methylation regulators. (c) Consensus clustering matrix for optimal k = 3. (d) PCA of the RNA expression profile. (e) The Kaplan-Meier curve for LGG patients in cluster 1/2/3. (f) 303 upregulated overlapping DEGs between clusters 3/1 and 3/2. (g) Gene Ontology biological processes of upregulated overlapping DEGs. (h) KEGG pathway analysis of upregulated overlapping DEGs.

Figure 3

Prognostic value of m⁶A RNA methylation regulators and construction of the m⁶A-related prognostic signature. (a) Overall survival- (OS-) related m⁶A RNA methylation regulators in TCGA cohort. (b, c) LASSO analysis with minimal lambda value. (d) LASSO coefficients of nine m⁶A RNA methylation regulators.

Figure 4

Validation of the m⁶A-related prognostic signature. (a, b) The Kaplan-Meier curves for survival in the TCGA and CGGA cohorts. (c, d) The distribution plots of the risk score and survival status in the TCGA and CGGA cohorts. (e, f) The receiver operating characteristic (ROC) curve analyses of the prognostic FRLS in predicting 1-, 3-, and 5-year overall survival (OS) in the TCGA and CGGA cohorts.

Figure 5

Correlation analysis between the m⁶A-related prognostic signature and clinicopathological features in the TCGA cohort. (a) Different levels of risk scores in LGG patients stratified by age, gender, WHO grade, histology, IDH status, 1p19q codeletion, and MGMT methylation status. (b) The Kaplan-Meier curves for subgroup survival analysis. A: astrocytoma; O: oligodendroglioma; AA: anaplastic astrocytoma; AO: anaplastic oligodendroglioma/oligoastrocytoma. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001; ns: no significance.

Figure 6

(a) Univariate and multivariate Cox regression analyses in the TCGA cohort. (b) Univariate and multivariate Cox regression analyses in the CGGA cohort.

Figure 7

Establishment and evaluation of a nomogram. (a) A nomogram was established based on the signature-based risk score, age, and 1p19q codeletion status in the TCGA cohort. (b, c) Calibration plots of the nomogram for predicting the probability of 1-, 3-, and 5-year OS in the TCGA and CGGA cohorts.

Figure 8

Correlation of the m⁶A-related prognostic signature with the immune landscape of LGG microenvironment in the TCGA cohort. (a) The comparison of immune scores, stromal scores, ESTIMATE scores, and tumor purity between the high- and low-risk groups. (b) The correlation between the risk score and the expression levels of immune checkpoints. (c) The abundance of 22 immune cells in the high- and low-risk groups. ^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001.