N6-Methyladenosine RNA Modification Landscape in the Occurrence and Recurrence of Nasopharyngeal Carcinoma
Bibliographic record
Abstract
Background: Nasopharyngeal carcinoma (NPC) is a type of squamous head and neck cancer with variable geographic distributions, with the highest incidence in Southeast Asia. Its primary treatment is radiotherapy due to its high radio sensitivity. However, the N6-methyladenosine (m6A) landscape in NPC, including recurrent NPC, has not been reported. Methods: In this study, m6A RNA immunoprecipitation (RIP) sequencing and microarray sequencing were performed on 12 tissue samples tissues of patients with primary and recurrent NPC. The expression profiles of m6A-related and non-coding RNAs were constructed and explored. Then, function experiments were performed to evaluate the effects of methyltransferase (METTL)3, METTL14 and WT1 associated protein (WTAP) on progressions of NPC. Finally, immunohistochemistry (IHC) and survival analysis were performed to confirm the correlation between METTL3, METTL14 and WTAP and NPC patients' clinical outcomes. Results: This study mapped m6A RNA modification and RNA expression profiles in normal nasopharynx, primary NPC, and recurrent NPC tissues. This study also explored the role of m6A modificators in NPC development and recurrence. METTL3, METTL14, and WTAP could promote invasion and metastasis of NPC, and that these three proteins could induce radiotherapy resistance in NPC cells through DNA repair. Moreover, we found that METTL3, METTL14, and WTAP promoted an increase in exosomes within NPC microenvironment. Conclusions: This study suggests that the alteration of m6A modification in primary and recurrent NPCs may play an important role in the development and progression of NPC.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".