The EMT Transcription Factor ZEB2 Promotes Proliferation of Primary and Metastatic Melanoma While Suppressing an Invasive, Mesenchymal-Like Phenotype
Bibliographic record
Abstract
Abstract Epithelial-to-mesenchymal transition (EMT)-inducing transcription factors (TF) are well known for their ability to induce mesenchymal states associated with increased migratory and invasive properties. Unexpectedly, nuclear expression of the EMT-TF ZEB2 in human primary melanoma has been shown to correlate with reduced invasion. We report here that ZEB2 is required for outgrowth for primary melanomas and metastases at secondary sites. Ablation of Zeb2 hampered outgrowth of primary melanomas in vivo, whereas ectopic expression enhanced proliferation and growth at both primary and secondary sites. Gain of Zeb2 expression in pulmonary-residing melanoma cells promoted the development of macroscopic lesions. In vivo fate mapping made clear that melanoma cells undergo a conversion in state where ZEB2 expression is replaced by ZEB1 expression associated with gain of an invasive phenotype. These findings suggest that reversible switching of the ZEB2/ZEB1 ratio enhances melanoma metastatic dissemination. Significance: ZEB2 function exerts opposing behaviors in melanoma by promoting proliferation and expansion and conversely inhibiting invasiveness, which could be of future clinical relevance.
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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 | 0.001 |
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".