Epithelial-mesenchymal plasticity induced by discontinuous exposure to TGFβ1 promotes tumour growth
Bibliographic record
Abstract
Abstract Transitions between epithelial and mesenchymal cellular states (EMT/MET) contribute to cancer progression. We hypothesize that EMT followed by MET promotes cell population heterogeneity favouring tumour growth. We developed an EMT model by on/off exposure of epithelial EpH4 cells (E-cells) to TGFβ1 that mimics phenotypic EMT (M-cells) and MET. We aimed at understanding whether phenotypic MET is accompanied by molecular and functional reversion back to epithelia, by using RNA sequencing, Immunofluorescence (IF), proliferation, wound healing, focus formation and mamosphere formation assays, as well as cell-xenografts in nude mice. Phenotypic reverted-epithelial cells (RE-cells), obtained after MET induction, presented pure epithelial morphology and proliferation rate resembling E-cells. However, RE transcriptomic profile and IF staining of epithelial and mesenchymal markers revealed a unique and heterogeneous mixture of cell-subpopulations, with high self-renewal ability fed by oxidative phosporylation. RE-cells heterogeneity is stably maintained for long periods after TGFβ1 removal, both in vitro and in large derived tumours in nude mice. Overall, we show that phenotypic reverted-epithelial cells (RE-cells) do not return to the molecular and functional epithelial state, present mesenchymal features related with aggressiveness and cellular heterogeneity that favour tumour growth in vivo . This work strengthens epithelial cells reprogramming and cellular heterogeneity fostered by inflammatory cues as a tumour-growth promoting factor in vivo .
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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".