Suppression of the grainyhead transcription factor 2 gene (GRHL2) inhibits the proliferation, migration, invasion and mediates cell cycle arrest of ovarian cancer cells
Bibliographic record
Abstract
Previously, we have identified the Grainyhead transcription factor 2 gene (GRHL2) as notably hypomethylated in high-grade (HG) serous epithelial ovarian tumors, compared with normal ovarian tissues. GRHL2 is known for its functions in normal tissue development and wound healing. In the context of cancer, the role of GRHL2 is still ambiguous as both tumorigenic and tumor suppressive functions have been reported for this gene, although a role of GRHL2 in maintaining the epithelial status of cancer cells has been suggested. In this study, we report that GRHL2 is strongly overexpressed in both low malignant potential (LMP) and HG serous epithelial ovarian tumors, which probably correlates with its hypomethylated status. Suppression of the GRHL2 expression led to a sharp decrease in cell proliferation, migration and invasion and induced G1 cell cycle arrest in epithelial ovarian cancer (EOC) cells displaying either epithelial (A2780s) or mesenchymal (SKOV3) phenotypes. However, no phenotypic alterations were observed in these EOC cell lines following GRHL2 silencing. Gene expression profiling and consecutive canonical pathway and network analyses confirmed these data, as in both these EOC cell lines, GRHL2 ablation was associated with the downregulation of various genes and pathways implicated in cell growth and proliferation, cell cycle control and cellular metabolism. Taken together, our data are indicative for a strong oncogenic potential of the GRHL2 gene in EOC progression and support recent findings on the role of GRHL2 as one of the major phenotypic stability factors (PSFs) that stabilize the highly aggressive/metastatic hybrid epithelial/mesenchymal (E/M) phenotype of cancer cells.
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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".