Dysregulation of m6A, m5C, and m1A Pathways in Gliomas Reveals EIF3A and TET1 as Candidate Biomarkers

Year 2025, Volume: 4 Issue: 2, 99 - 115, 13.12.2025

Gaye Accan Turan Demircan

https://doi.org/10.5281/zenodo.17786737

https://izlik.org/JA66NH23SW

Abstract

Epitranscriptomic modifications such as N6-methyladenosine, 5-methylcytosine, and N1-methyladenosine have recently emerged as critical regulators of cancer biology. These pathways influence tumor initiation, progression, invasion, metastasis, and cellular differentiation. Understanding their contribution to glioblastoma aggressiveness may provide new avenues for therapeutic and prognostic applications. Here, we systematically analyzed m6A, m5C, and m1A pathway regulators in GBM and compared their dynamics with lower-grade gliomas using publicly available datasets. Bioinformatics approaches included mutation profiling, alteration frequency assessment, differential expression analysis, and correlation with overall survival. Our results revealed that m5C regulators exhibited higher mutation frequencies than m6A and m1A regulators in both glioma types. Moreover, a greater number of regulators were significantly associated with OS in LGG compared to GBM, suggesting tumor grade-specific prognostic relevance. Gene Ontology, KEGG, and Gene Set Enrichment Analysis further indicated that each pathway contributes to distinct biological processes and cellular signaling cascades. Receiver operating characteristic analysis identified several regulators with diagnostic and prognostic potential. Notably, EIF3A and TET1 showed strong biomarker potential, as their elevated expression negatively correlated with WHO tumor grade and distinguished between GBM and LGG. In summary, our study highlights the distinct roles of m6A, m5C, and m1A pathways in glioma biology and identifies EIF3A and TET1 as promising biomarkers with potential diagnostic and therapeutic implications. Targeting epitranscriptomic regulators may represent a novel strategy for glioma management.

Keywords

Epitranscriptomics , RNA Modifications , GBM , LGG , Bioinformatics Analysis

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