BRAFV600E Mutation Enhances Estrogen-Induced Metastatic Potential of Thyroid Cancer by Regulating the Expression of Estrogen Receptors
Bibliographic record
Abstract
BACKGRUOUND: Cross-talk between mitogen-activated protein kinase and estrogen has been reported; however, the role of BRAFV600E in the estrogen responsiveness of thyroid cancer is unknown. We elucidated the effect of BRAFV600E 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 BRAF gene (Nthy/WT) and Nthy/BRAFV600E (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. ESR expression was analyzed according to BRAFV600E 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. BRAFV600E-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 BRAFV600E group, ESR1/ESR2 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 BRAFV600E mutation plays a crucial role in the estrogen responsiveness of thyroid cancer by regulating ER expression. Therefore, BRAFV600E 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 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".