Abstract
BACKGROUND: Gliomas represent the most aggressive malignancies of the central nervous system, with posttranslational modifications (PTMs) emerging as critical regulators of oncogenic processes through dynamic protein functional modulation. Despite their established role in tumor biology, the systematic characterization of PTM-mediated molecular mechanisms driving glioma progression remains unexplored. This study aims to uncover the molecular mechanisms of glioma, with a focus on the role of PTMs. METHODS: We analyzed the PTM pathway to classify glioma patients into distinct clusters. Comprehensive analyses compared intercluster differences in clinical outcomes, mutational landscapes, and immune microenvironment profiles. Differentially expressed genes (DEGs) were identified to construct a robust prognostic prediction model with machine learning approaches. Among the genes included in the model, TOM1L1 (Target of Myb1 Like 1 Membrane Trafficking Protein) was selected for in vitro experimental validation to assess its role in glioma progression. RESULTS: PTMs were found to influence glioma prognosis significantly. Dysregulation in specific pathways, such as glutathionylation and citrullination, was correlated with more aggressive clinical features. The prognostic model, comprising DEGs such as TOM1L1, demonstrated high predictive accuracy (c-index = 0.867)-the scores derived from the model strongly correlated with glioma progression indicators. In vitro experiments revealed that TOM1L1 facilitates malignant progression by modulating PTM pathways, confirming its functional role in glioma. CONCLUSION: Our study establishes the first comprehensive PTM atlas in gliomas, revealing subtype-specific modification patterns with clinical and therapeutic implications. TOM1L1 emerges as a promising prognostic biomarker and a potential therapeutic intervention target. Targeting PTM pathways may offer novel strategies for glioma treatment, enhancing patient outcomes.