CRAF Methylation by PRMT6 Regulates Aerobic Glycolysis–Driven Hepatocarcinogenesis via ERK‐Dependent PKM2 Nuclear Relocalization and Activation
Bibliographic record
Abstract
BACKGROUND AND AIMS: Most tumor cells use aerobic glycolysis (the Warburg effect) to support anabolic growth and promote tumorigenicity and drug resistance. Intriguingly, the molecular mechanisms underlying this phenomenon are not well understood. In this work, using gain-of-function and loss-of-function in vitro studies in patient-derived organoid and cell cultures as well as in vivo positron emission tomography-magnetic resonance imaging animal models, we showed that protein arginine N-methyltransferase 6 (PRMT6) regulates aerobic glycolysis in human hepatocellular carcinoma (HCC) through nuclear relocalization of pyruvate kinase M2 isoform (PKM2), a key regulator of the Warburg effect. APPROACH AND RESULTS: We found PRMT6 to methylate CRAF at arginine 100, interfering with its RAS/RAF binding potential, and therefore altering extracellular signal-regulated kinase (ERK)-mediated PKM2 translocation into the nucleus. This altered PRMT6-ERK-PKM2 signaling axis was further confirmed in both a HCC mouse model with endogenous knockout of PRMT6 as well as in HCC clinical samples. We also identified PRMT6 as a target of hypoxia through the transcriptional repressor element 1-silencing transcription factor, linking PRMT6 with hypoxia in driving glycolytic events. Finally, we showed as a proof of concept the therapeutic potential of using 2-deoxyglucose, a glycolysis inhibitor, to reverse tumorigenicity and sorafenib resistance mediated by PRMT6 deficiency in HCC. CONCLUSIONS: Our findings indicate that the PRMT6-ERK-PKM2 regulatory axis is an important determinant of the Warburg effect in tumor cells, and provide a mechanistic link among tumorigenicity, sorafenib resistance, and glucose metabolism.
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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) | 0.002 | 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".