TGF-β1 and TNF-α synergistically induce epithelial to mesenchymal transition of breast cancer cells by enhancing TAK1 activation
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".