BRAFV600E Mutation Enhances Estrogen-Induced Metastatic Potential of Thyroid Cancer by Regulating the Expression of Estrogen Receptors
Bibliographic record
Abstract
Background: Cross-talk between mitogen-activated protein kinase and estrogen has been reported; however, the role of <i>BRAF<sup>V600E</sup></i> in the estrogen responsiveness of thyroid cancer is unknown. We elucidated the effect of <i>BRAF<sup>V600E</sup></i> on the estrogen-induced increase in metastatic potential in thyroid cancer.Methods: Using a pair of cell lines, human thyroid cell lines which harbor wild type <i>BRAF</i> gene (Nthy/WT) and Nthy/<i>BRAF<sup>V600E</sup></i> (Nthy/V600E), the expression of estrogen receptors (ERs) and estrogen-induced metastatic phenotypes were evaluated. Susceptibility to ERα- and ERβ-selective agents was evaluated to confirm differential ER expression. <i>ESR</i> expression was analyzed according to <i>BRAF<sup>V600E</sup></i> status and age (≤50 years vs. >50 years) using The Cancer Genome Atlas (TCGA) data.Results: Estradiol increased the ERα/ERβ expression ratio in Nthy/V600E, whereas the decreased ERα/ERβ expression ratio was found in Nthy/WT. <i>BRAF<sup>V600E</sup></i>-mutated cell lines showed a higher E2-induced increase in metastatic potential, including migration, invasion, and anchorage-independent growth compared with Nthy/WT. An ERα antagonist significantly inhibited migration in Nthy/V600E cells, whereas an ERβ agonist was more effective in Nthy/WT. In the <i>BRAF<sup>V600E</sup></i> group, <i>ESR1/ESR2</i> ratio was significantly higher in younger age group (≤50 years) compared with older age group (>50 years) by TCGA data analysis.Conclusion: Our data show that <i>BRAF<sup>V600E</sup></i> mutation plays a crucial role in the estrogen responsiveness of thyroid cancer by regulating ER expression. Therefore, <i>BRAF<sup>V600E</sup></i> might be used as a biomarker when deciding future hormone therapies based on estrogen signaling in thyroid cancer patients.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".