<b>Characterization of the DNA Methylation Profile in Atypical Gliomas</b>

Atypical gliomas are a subtype of malignant brain tumors that are understudied, characterized by molecular heterogeneity that complicates their diagnosis and treatment. DNA methylation, a key epigenetic modification, is associated with tumorigenesis and progression of gliomas, but its role in atypical gliomas remains poorly explored. This study aimed to characterize the DNA methylation profile in atypical gliomas using public data from the GSE50923 dataset (GEO). Methylation profiles obtained through Infinium HumanMethylation450 BeadChip microarrays were analyzed, applying a quality filter based on detection values (p-value &lt; 0.01). Beta-values were calculated to quantify methylation, and exploratory analyses were performed using principal component analysis (PCA) and hierarchical clustering. Differentially methylated loci between atypical gliomas and controls were identified using t-tests with Benjamini-Hochberg correction (FDR &lt; 0.05), visualized in volcano plots and heatmaps. The results revealed a bimodal distribution of beta-values and partial separation between cases and controls in the PCA, suggesting specific epigenetic signatures. These findings highlight the potential of differentially methylated loci as biomarkers for the diagnosis or stratification of atypical gliomas. Despite limitations, such as sample size and lack of functional analyses, this study demonstrates the utility of bioinformatics approaches with tools like Python for studying brain cancer epigenetics. Future analyses could validate these results and integrate multi-omics data to improve therapeutic strategies.