Deregulated ERK1/2 MAP kinase signaling promotes aneuploidy by a Fbxw7β-Aurora A pathway
Bibliographic record
Abstract
Aneuploidy is a common feature of human solid tumors and is often associated with poor prognosis. There is growing evidence that oncogenic signaling pathways, which are universally dysregulated in cancer, contribute to the promotion of aneuploidy. However, the mechanisms connecting signaling pathways to the execution of mitosis and cytokinesis are not well understood. Here, we show that hyperactivation of the ERK1/2 MAP kinase pathway in epithelial cells impairs cytokinesis, leading to polyploidization and aneuploidy. Mechanistically, deregulated ERK1/2 signaling specifically downregulates expression of the F-box protein Fbxw7β, a substrate-binding subunit of the SCFFbxw7 ubiquitin ligase, resulting in the accumulation of the mitotic kinase Aurora A. Reduction of Aurora A levels by RNA interference or pharmacological inhibition of MEK1/2 reverts the defect in cytokinesis and decreases the frequency of abnormal cell divisions induced by oncogenic H-RasV12. Reciprocally, overexpression of Aurora A or silencing of Fbxw7β phenocopies the effect of H-RasV12 on cell division. In vivo, conditional activation of MEK2 in the mouse intestine lowers Fbxw7β expression, resulting in the accumulation of cells with enlarged nuclei. We propose that the ERK1/2/ Fbxw7β/Aurora A axis identified in this study contributes to genomic instability and tumor progression.
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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".