RUNX3 Promotes the Tumorigenic Phenotype in KGN, a Human Granulosa Cell Tumor-Derived Cell Line
Bibliographic record
Abstract
Granulosa cell tumors of the ovary (GCT) are the predominant type of ovarian sex cord/stromal tumor. Although prognosis is generally favorable, the outcome for advanced and recurrent GCT is poor. A better understanding of the molecular pathogenesis of GCT is critical to developing effective therapeutic strategies. Here we have examined the potential role of the runt-related transcription factor RUNX3. There are only two GCT cell lines available. While RUNX3 is silenced in the GCT cell line KGN cells, it is highly expressed in another GCT cell line, COV434 cells. Re-expression of RUNX3 promotes proliferation, anchorage-independent growth, and motility in KGN cells in vitro and tumor formation in mice in vivo. Furthermore, expression of a dominant negative form of RUNX3 decreases proliferation of COV434 cells. To address a potential mechanism of action, we examined expression of cyclin D2 and the CDK inhibitor p27Kip1, two cell cycle regulators known to be critical determinants of GCT cell proliferation. We found that RUNX3 upregulates the expression of cyclin D2 at the mRNA and protein level, and decreases the level of the p27Kip1 protein, but not p27Kip1 mRNA. In conclusion, we demonstrate that RUNX proteins are expressed in GCT cell lines and human GCT specimens, albeit at variable levels, and RUNX3 may play an oncogenic role in a subset of GCTs.
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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".