Rewriting the RNA code: an m6a-centric framework to classify tumors and guide combination therapies

Background The epitranscriptome, particularly N 6 -methyladenosine (m 6 A), represents a dynamic layer of post-transcriptional regulation fundamentally implicated in cancer. However, the clinical translation of this knowledge is hampered by profound context-dependency, where the same m 6 A regulator can exert opposing roles in different tumors. To overcome this barrier, we propose a novel, clinically actionable taxonomic framework that classifies tumors based on their dominant dysregulated m 6 A component. Methods We synthesized current evidence through systematic reviews of primary research and high-impact papers from PubMed and Google Scholar, focusing on the mechanistic role of m 6 A modifications in cancer biology, therapy resistance, and therapeutic targeting. This synthesis was used to integrate pan-cancer molecular data including regulator expression, genetic dependency scores, and modification landscapes to define and characterize m 6 A-driven molecular subtypes. Results We classify tumors into Writer-Dominant (METTL3/14-high, Eraser-High (FTO/ALKBH5-high), Reader-Amplified (IGF2BP/YTHDF-high), and Immune-Modulatory subtypes, each with distinct oncogenic programs, therapy resistance mechanisms, and, crucially, actionable therapeutic vulnerabilities. We provide explicit, evidence-based molecular and functional inclusion criteria for each subtype and acknowledge that tumors can exhibit hybrid features, which directly inform rational combination strategies. Furthermore, we detail a diagnostic-therapeutic roadmap that integrates liquid biopsy-based m 6 A biomarker detection with subtype-specific treatment assignment. Conclusion Targeting the m 6 A epitranscriptome represents a paradigm shift in oncology; our framework provides the essential strategic approach needed to overcome context-dependency, offering a logical structure for tumor classification, vulnerability prediction, and the translation of epitranscriptomic insights into patient benefit through personalized, biomarker-guided combination therapies.