Fibroblast Growth Factor 2 and Estrogen Control the Balance of Histone 3 Modifications Targeting MAGE-A3 in Pituitary Neoplasia
Bibliographic record
Abstract
PURPOSE: Four members of the fibroblast growth factor receptor (FGFR) family transduce signals of a diverse group of FGF ligands. The FGFR2-IIIb isoform is abundantly present in the normal pituitary gland with contrasting down-regulation in neoplastic pituitary cells. cDNA profiling identified the cancer-testis antigen melanoma-associated antigen A3 (MAGE-A3) as a putative target negatively regulated by FGFR2. EXPERIMENTAL DESIGN: Comparisons were made between normal and neoplastic human and mouse pituitary cells. Gene expression was examined by reverse transcription-PCR, DNA methylation was determined by methylation-specific PCR and combined bisulfite restriction analysis, and histone modification marks were identified by chromatin immunoprecipitation. RESULTS: Normal human pituitary tissue that expresses FGFR2-IIIb does not express MAGE-A3; in contrast, pituitary tumors that are FGFR2 negative show abundant MAGE-A3 mRNA expression. MAGE-A3 expression correlates with the presence and extent of DNA promoter methylation; more frequent and higher-degree methylation is present in the normal gland compared with pituitary tumors. Conversely, pituitary tumors are hypomethylated, particularly in females where MAGE-A3 expression is nearly thrice higher than in males. Estradiol treatment induces MAGE-A3 through enhanced histone 3 acetylation and diminished methylation. The effects of estradiol are directly opposed by FGF7/FGFR2-IIIb. Down-regulation of MAGE-A3 results in p53 transcriptional induction, also through reciprocal histone acetylation and methylation modifications. CONCLUSIONS: These findings highlight MAGE-A3 as a target of FGFR2-IIIb and estrogen action and provide evidence for a common histone-modifying network in the control of the balance between opposing signals.
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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".