The Cancer/Testis Antigen Melanoma-Associated Antigen-A3/A6 Is a Novel Target of Fibroblast Growth Factor Receptor 2-IIIb through Histone H3 Modifications in Thyroid Cancer
Bibliographic record
Abstract
PURPOSE: Fibroblast growth factor (FGF) signals play fundamental roles in development and tumorigenesis. Thyroid cancer is an example of a tumor with nonoverlapping genetic mutations that up-regulate mitogen-activated protein kinase. We reported recently that FGF receptor 2 (FGFR2) is down-regulated through extensive DNA promoter methylation in thyroid cancer. Reexpression of the FGFR2-IIIb isoform impedes signaling upstream of the BRAF/mitogen-activated protein kinase pathway to interrupt tumor progression. In this analysis, we examined a novel target of FGFR2-IIIb signaling, melanoma-associated antigen-A3 and A6 (MAGE-A3/6). EXPERIMENTAL DESIGN: cDNA microarray analysis was done on human WRO thyroid cancer cells transfected with FGFR2-IIIb or empty vector. Identified gene target was confirmed by reverse transcription-PCR and Western blotting. Gene regulation was examined by treatment of WRO cells with the methylation inhibitor 5'-azacytidine followed by methylation-specific PCR and reverse transcription-PCR and by chromatin immunoprecipitation. RESULTS: Gene expression profiling identified the cancer/testis antigen MAGE-A3/6 as a novel target of FGFR2-IIIb signaling. MAGE-A3/6 regulation was mediated through DNA methylation and chromatin modifications. In particular, FGF7/FGFR2-IIIb activation resulted in histone 3 methylation and deacetylation associated with the MAGE-A3/6 promoter to down-regulate gene expression. CONCLUSIONS: These data unmask a complex repertoire of epigenetically controlled signals that govern FGFR2-IIIb and MAGE-A3/6 expression. Our findings provide insights into the interrelationship between novel tumor markers that may also represent overlapping therapeutic targets.
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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".