The Landscape and Prognostic Value of m6A Methylation-related Genes in Low Grade Glioma

Author(s): Lixin Ma, Zhihui Liu, Hongwei Zhang, Weihai Ning, Yanming Qu, Chunjiang Yu

Background: Low-grade glioma (LGG) can behave aggressively, akin to glioblastoma, and prognostic classification is urgently needed. N6-methyladenosine (m6A) modification is a key regulator of transcriptional expression during tumorigenesis and progression. This study aimed to identify transcriptome biomarkers with prognostic predictive value and define molecular subclassifications.

Methods: We selected 21 m6A methylation-related genes for analysis of 529 LGG samples from TCGA LGG datasets and 1,152 brain tissues from the GTEx datasets. Through difference analysis, Protein-protein interactions (PPI) network, and spearman correlation analysis, gene expression and correlation were studied. Consensus cluster, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes, and Genomes (KEGG) analysis were performed for classification and functional analysis. Lasso Cox regression algorithm and univariate and multivariate analyses were used for assessing risk factors.

Results: The expression of m6A methylation-related genes between normal brain and LGG samples was significantly different. Consensus cluster analysis clearly divided LGG samples into two categories, with a p-value for the difference between prognosis close to 0. Through the lasso Cox regression algorithm and univariate and multivariate analyses, four genetic biomarkers (IGF2BP2, IGF2BP3, YTHDC1, and ALKBH3) were screened out, and the cumulative analysis of these effectively predicted patients’ prognosis.

Conclusion: Consensus cluster analysis based on m6A methylation-related genes clearly divided LGG samples into two categories. Moreover, the cumulative analysis of four genetic biomarkers (IGF2BP2, IGF2BP3, YTHDC1, and ALKBH3) effectively predicted prognosis.