EZH2 crosstalk with RNA methylation promotes prostate cancer progression through modulation of m6A autoregulation pathway
Bibliographic record
Abstract
N6-methyladenosine (m6A), the most predominant RNA modification in humans, participates in various fundamental and pathological bioprocesses. Dynamic manipulation of m6A deposition in the transcriptome is critical for cancer progression, though how this regulation is achieved remains understudied. Here, we report that, in prostate cancer (PCa), Polycomb group (PcG) protein Enhancer of Zeste Homolog 2 (EZH2) exerts an additional function in m6A regulation via its enzymatic activity. Mechanistically, EZH2 methylates and stabilizes FOXA1 proteins from degradation, which, in turn, facilitates the transcription of m6A reader YTHDF1. Through activating an m6A autoregulation pathway, YTHDF1 enhances the translation of METTL14 and WTAP, 2 critical components of the m6A methyltransferase complex (MTC), and thereby upregulates the global m6A level in PCa cells. We further demonstrate that inhibiting the catalytic activity of EZH2 suppresses the translation process globally through targeting the YTHDF1-m6A axis. By disrupting both the expression and interaction of key m6A MTC subunits, combinational treatment of EZH2 degrader MS8815 and m6A inhibitor STM2457 mitigates prostate tumor growth synergistically. Together, our study decodes a previously hidden interrelationship between EZH2 and mRNA modification, which may be leveraged to advance the EZH2-targeting curative strategies in cancer.
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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".