Tea domain transcription factor TEAD4 mitigates TGF-β signaling and hepatocellular carcinoma progression independently of YAP
Bibliographic record
Abstract
Tea domain transcription factor 4 (TEAD4) plays a pivotal role in tissue development and homeostasis by interacting with Yes-associated protein (YAP) in response to Hippo signaling inactivation. TEAD4 and YAP can also cooperate with transforming growth factor-β (TGF-β)-activated Smad proteins to regulate gene transcription. Yet, it remains unclear whether TEAD4 plays a YAP-independent role in TGF-β signaling. Here, we unveil a novel tumor suppressive function of TEAD4 in liver cancer via mitigating TGF-β signaling. Ectopic TEAD4 inhibited TGF-β-induced signal transduction, Smad transcriptional activity, and target gene transcription, consequently suppressing hepatocellular carcinoma cell proliferation and migration in vitro and xenograft tumor growth in mice. Consistently, depletion of endogenous TEAD4 by siRNAs enhanced TGF-β signaling in cancer cells. Mechanistically, TEAD4 associates with receptor-regulated Smads (Smad2/3) and Smad4 in the nucleus, thereby impairing the binding of Smad2/3 to the histone acetyltransferase p300. Intriguingly, these negative effects of TEAD4 on TGF-β/Smad signaling are independent of YAP, as impairing the TEAD4-YAP interaction through point mutagenesis or depletion of YAP and/or its paralog TAZ has little effect. Together, these results unravel a novel function of TEAD4 in fine tuning TGF-β signaling and liver cancer progression in a YAP-independent manner.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".