TGF-β1 and TNF-α synergistically induce epithelial to mesenchymal transition of breast cancer cells by enhancing TAK1 activation
Why this work is in the frame
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Bibliographic record
Abstract
TGF-β1 is a main inducer of epithelial to mesenchymal transition (EMT). However, many breast cancer cells are not sensitive to the EMT induction by TGF-β1 alone. So far, the mechanisms underlying the induction of TGF-β1-insensitive breast cancer cells remains unclear. Here we report that TNF-α can induce EMT and invasiveness of breast cancer cells which are insensitive to TGF-β1. Intriguingly, TGF-β1 could cooperate with TNF-α to promote the EMT and invasiveness of breast cancer cells. The prolonged co-stimulation with TGF-β1 and TNF-α could enhance the sustained activation of Smad2/3, p38 MAPK, ERK, JNK and NF-κB pathways by enhancing the activation of TAK1, which was mediated by the gradually up-regulated TβRs. Except for JNK, all of these pathways were required for the effects of TGF-β1 and TNF-α. Importantly, the activation of p38 MAPK and ERK pathways resulted in a positive feed-back effect on TAK1 activation by up-regulating the expression of TβRs, favoring the activation of multiple signaling pathways. Moreover, SLUG was up-regulated and required for the TGF-β1/TNF-α-induced EMT and invasiveness. In addition, SLUG could also enhance the activation of signaling pathways by promoting TβRII expression. These findings suggest that the up-regulation of TβRs contributes to the sustained activation of TAK1 induced by TGF-β1/TNF-α and the following activation of multiple signaling pathways, resulting in EMT and invasiveness of breast cancer cells.
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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 | 0.001 | 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) | 0.000 | 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 it