Inhibition of RUNX2 Transcriptional Activity Blocks the Proliferation, Migration and Invasion of Epithelial Ovarian Carcinoma Cells
Bibliographic record
Abstract
Previously, we have identified the RUNX2 gene as hypomethylated and overexpressed in post-chemotherapy (CT) primary cultures derived from serous epithelial ovarian cancer (EOC) patients, when compared to primary cultures derived from matched primary (prior to CT) tumors. However, we found no differences in the RUNX2 methylation in primary EOC tumors and EOC omental metastases, suggesting that DNA methylation-based epigenetic mechanisms have no impact on RUNX2 expression in advanced (metastatic) stage of the disease. Moreover, RUNX2 displayed significantly higher expression not only in metastatic tissue, but also in high-grade primary tumors and even in low malignant potential tumors. Knockdown of the RUNX2 expression in EOC cells led to a sharp decrease of cell proliferation and significantly inhibited EOC cell migration and invasion. Gene expression profiling and consecutive network and pathway analyses confirmed these findings, as various genes and pathways known previously to be implicated in ovarian tumorigenesis, including EOC tumor invasion and metastasis, were found to be downregulated upon RUNX2 suppression, while a number of pro-apoptotic genes and some EOC tumor suppressor genes were induced. Taken together, our data are indicative for a strong oncogenic potential of the RUNX2 gene in serous EOC progression and suggest that RUNX2 might be a novel EOC therapeutic target. Further studies are needed to more completely elucidate the functional implications of RUNX2 and other members of the RUNX gene family in ovarian tumorigenesis.
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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".