A Novel RET Kinase–β-Catenin Signaling Pathway Contributes to Tumorigenesis in Thyroid Carcinoma
Bibliographic record
Abstract
The RET receptor tyrosine kinase has essential roles in cell survival, differentiation, and proliferation. Oncogenic activation of RET causes the cancer syndrome multiple endocrine neoplasia type 2 (MEN 2) and is a frequent event in sporadic thyroid carcinomas. However, the molecular mechanisms underlying RET's potent transforming and mitogenic signals are still not clear. Here, we show that nuclear localization of beta-catenin is frequent in both thyroid tumors and their metastases from MEN 2 patients, suggesting a novel mechanism of RET-mediated function through the beta-catenin signaling pathway. We show that RET binds to, and tyrosine phosphorylates, beta-catenin and show that the interaction between RET and beta-catenin can be direct and independent of cytoplasmic kinases, such as SRC. As a result of RET-mediated tyrosine phosphorylation, beta-catenin escapes cytosolic down-regulation by the adenomatous polyposis coli/Axin/glycogen synthase kinase-3 complex and accumulates in the nucleus, where it can stimulate beta-catenin-specific transcriptional programs in a RET-dependent fashion. We show that down-regulation of beta-catenin activity decreases RET-mediated cell proliferation, colony formation, and tumor growth in nude mice. Together, our data show that a beta-catenin-RET kinase pathway is a critical contributor to the development and metastasis of human thyroid carcinoma.
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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".