YAP and TEAD Are Transcriptional Regulators of Neuroendocrine Differentiation and Growth in Carcinoid Cells
Bibliographic record
Abstract
Molecular regulators of variably aggressive carcinoid tumors are unknown. Since carcinoids have low expression of Yes-associated protein (YAP), it was hypothesized that low YAP expression provides a molecular advantage to carcinoids by preventing YAP from binding its partner, TEA domain transcription factor (TEAD). To test this hypothesis, constitutively active YAP and a TEAD-binding defective form of YAP were overexpressed in lung (H727) and pancreatic (BON1) carcinoid cells. It was found that active YAP overexpression inhibited neuroendocrine markers, morphology, cell proliferation, and anchorage-independent cell growth, whereas TEAD-binding defective YAP recovered these features. Through integrated chromatin immunoprecipitation and RNA sequencing analyses, it was found that YAP-TEAD binding down-regulated neuroendocrine transcription factor genes and up-regulated select transforming growth factor (TGF-β) superfamily and Notch genes related to cell growth. It was concluded that low YAP expression permits neuroendocrine differentiation and growth in carcinoid cells by preventing YAP-TEAD binding and subsequent dysregulation of gene targets. These results identify unknown molecular mechanisms in carcinoid development that may apply to the broader family of neuroendocrine cancers.
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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".