RNA epigenetic modifications as dynamic biomarkers in cancer: from mechanisms to clinical translation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it