The RUNX1 transcription factor is expressed in serous epithelial ovarian carcinoma and contributes to cell proliferation, migration and invasion
Bibliographic record
Abstract
Previously, we have identified the RUNX1 gene as hypomethylated and overexpressed in post-chemotherapy (CT) primary cultures derived from epithelial ovarian cancer (EOC) patients, when compared with primary cultures derived from matched primary (prior to CT) tumors. Here we show that RUNX1 displays a trend of hypomethylation, although not significant, in omental metastases compared with primary EOC tumors. Surprisingly, RUNX1 displayed significantly higher expression not only in metastatic tissue, but also in high-grade primary tumors and even in low malignant potential tumors. The RUNX1 expression levels were almost identical in primary tumors and omental metastases, suggesting that RUNX1 hypomethylation might have a limited impact on its overexpression in advanced (metastatic) stage of the disease. Knockdown of the RUNX1 expression in EOC cells led to sharp decrease of cell proliferation and induced G 1 cell cycle arrest. Moreover, RUNX1 suppression significantly inhibited EOC cell migration and invasion. Gene expression profiling and consecutive network and pathway analyses confirmed these findings, as numerous genes and pathways known previously to be implicated in ovarian tumorigenesis, including EOC tumor invasion and metastasis, were found to be downregulated upon RUNX1 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 RUNX1 gene in EOC progression and suggest that RUNX1 might be a novel EOC therapeutic target. Further studies are needed to more completely elucidate the functional implications of RUNX1 and other members of the RUNX gene family in ovarian tumorigenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".