GATA-4 Regulates Bcl-2 Expression in Ovarian Granulosa Cell Tumors
Bibliographic record
Abstract
Excessive cell proliferation and decreased apoptosis have been implicated in the pathogenesis of ovarian granulosa cell tumors (GCTs). We hypothesized that transcription factor GATA-4 controls expression of the antiapoptotic factor Bcl-2 and the cell cycle regulator cyclin D2 in normal and neoplastic granulosa cells. To test this hypothesis, a tissue microarray based on 80 GCTs was subjected to immunohistochemistry for GATA-4, Bcl-2, and cyclin D2, and the data were correlated to clinical and histopathological parameters. In addition, quantitative RT-PCR for GATA-4, Bcl-2, and cyclin D2 was performed on 21 human GCTs. A mouse GCT model was used to complement these studies. The role of GATA-4 in the regulation of Bcl2 and ccdn2 (coding for cyclin D2) was studied by transactivation assays, and by disrupting GATA-4 function with dominant negative approaches in mouse and human GCT cell lines. We found that GATA-4 expression correlated with Bcl-2 and cyclin D2 expression in human and murine GCTs. Moreover, GATA-4 enhanced Bcl-2 and cyclin D2 promoter activity in murine GCT cells. Whereas GATA-4 overexpression up-regulated and dominant negative GATA-4 suppressed Bcl-2 expression in human GCT cells, the effects on cyclin D2 were negligible. Our results reveal a previously unknown relationship between GATA-4 and Bcl-2 in mammalian granulosa cells and GCTs, and suggest that GATA-4 influences granulosa cell fate by transactivating Bcl-2.
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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".