{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:57:36Z","timestamp":1787313456957,"version":"build-2736575974"},"reference-count":146,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      The epitranscriptome, particularly N\n                      <jats:sup>6<\/jats:sup>\n                      -methyladenosine (m\n                      <jats:sup>6<\/jats:sup>\n                      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\n                      <jats:sup>6<\/jats:sup>\n                      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\n                      <jats:sup>6<\/jats:sup>\n                      A component.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:sup>6<\/jats:sup>\n                      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\n                      <jats:sup>6<\/jats:sup>\n                      A-driven molecular subtypes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:sup>6<\/jats:sup>\n                      A biomarker detection with subtype-specific treatment assignment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Targeting the m\n                      <jats:sup>6<\/jats:sup>\n                      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.\n                    <\/jats:p>\n                  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