Epitranscriptomics and cervical cancer: the emerging role of m<sup>6</sup>A, m<sup>5</sup>C and m<sup>1</sup>A RNA modifications
Bibliographic record
Abstract
Abstract Cervical cancer (CC), one of the most prevalent and detrimental gynaecologic cancers, evolves through genetic and epigenetic alterations resulting in the promotion of oncogenic activity and dysfunction of tumour-suppressing mechanisms. Despite medical advancement, the prognosis for advanced-stage patients remains extremely low due to high recurrence rates and resistance to existing treatments. Thereby, the search for potential prognostic biomarkers is heightened to unravel new modalities of CC pathogenesis and to develop novel anti-cancer therapies. Epitranscriptomic modifications, reversible epigenetic RNA modifications, regulate various biological processes by deciding RNA fate to mediating RNA interactions. This narrative review provides insight into the cellular and molecular roles of endogenous RNA-editing proteins and their associated epitranscriptomic modifications, especially N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A), in governing the development, progression and metastasis of CC. We discussed the in-depth epitranscriptomic mechanisms underlying the regulation of over 50 RNAs responsible for tumorigenesis, proliferation, migration, invasion, survival, autophagy, stemness, epithelial-mesenchymal transition, metabolism (glucose, lipid, glutamate and glutamine), resistance (drug and radiation), angiogenesis and recurrence of CC. Additionally, we provided a concise overview of the therapeutic potential of targeting the altered expression of endogenous RNA-editing proteins and aberrant deposition of RNA modifications on both coding and non-coding RNAs in CC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".