AUTHOR=Bai Zhiqun , Wang Xuemei , Zhang Zhen 

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

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.655169

DOI=10.3389/fgene.2022.655169

ISSN=1664-8021

ABSTRACT=Background: The prognosis of low-grade glioma (LGG) is different from that of other intracranial tumors. Although many markers have been established, few of them are really used in clinical practice. M6A methylation modification significantly affects the biological behavior of the tumor. Therefore, it is of great significance to establish a LGG prognostic model based on m6A methylation regulatory genes.
Methods: 495 samples from TCGA and 172 samples from CGGA, were analyzed. Univariate cox analysis was used to identify methylation regulatory genes with prognostic significance. Lasso-cox regression algorithm was used to find valuable prognostic genes. ROC curve and Kaplan-Meier curve were used to verify the accuracy of the model. GSEA and KEGG are used to find important cellular pathways.
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. There is a high degree of consistency in the internal training cohort and internal validation cohort in CGGA and the external validation cohort in TCGA. Further KEGG and GSEA analysis showed that focal adhsion and cell cycle pathway were significantly up-regulated in high-risk patients. The signature could distinguish patients with different immune checkpoint gene expression levels, suggesting that the signature could be used to evaluate the response to immune checkpoint inhibitor (ICI) immunotherapy. 
Conclusion: Taken together, we comprehensively evaluated the m6A methylation regulatory genes in LGG, and constructed a prognostic prediction model based on m6A methylation, which promising to be a novel signature to predict the prognosis of patients with LGG.