Epithelial-Mesenchymal Plasticity Induced by Discontinuous Exposure to TGFβ1 Promotes Tumour Growth
Bibliographic record
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 and 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 epithelial morphologies and proliferation rates resembling E cells. However, the RE transcriptomic profile and IF staining of epithelial and mesenchymal markers revealed a uniquely heterogeneous mixture of cell subpopulations with a high self-renewal ability. RE cell heterogeneity was stably maintained for long periods after TGFβ1 removal both in vitro and in large tumours derived from the nude mice. Overall, we show that phenotypic reverted epithelial cells (RE cells) do not return to the molecular and functional epithelial state and present mesenchymal features related to aggressiveness and cellular heterogeneity that favour tumour growth in vivo. This work strengthens epithelial cell reprogramming and cellular heterogeneity fostered by inflammatory cues as a tumour growth-promoting factor in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".