RNA epigenetic modifications as dynamic biomarkers in cancer: from mechanisms to clinical translation
Bibliographic record
Abstract
RNA modifications are crucial for post-transcriptional gene regulation. Research on RNA modifications has become a novel frontier of epitranscriptomics. Up to now, over 170 kinds of modifications have been identified on mRNA and diverse non-coding RNA. Three classes of proteins (writers, erasers, and readers) regulate the addition, removal, and identification of epigenetic marks, thus affecting RNA biological functions. Increasing evidence identifies the dysregulation of RNA modifications in different cancer types and the therapeutic potential of targeting RNA-modifying enzymes. The ability of RNA modifications to improve mRNA stability and translation efficacy and decrease immunogenicity has been exploited for the clinical use of mRNA cancer vaccines. This review aims to shed light on several vital cap, tail, and internal modifications of RNA with a focus on the connection between RNA epigenetic pathways and cancer pathogenesis. We further explore the clinical potential of RNA modifications as dynamic biomarkers for cancer diagnosis, prognosis, and therapeutic response prediction, addressing both technological challenges and translational opportunities. Finally, we analyze the limitations of current studies and discuss the research focus in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".