Plakoglobin Reduces the in vitro Growth, Migration and Invasion of Ovarian Cancer Cells Expressing N-Cadherin and Mutant p53
Bibliographic record
Abstract
Aberrant expression of cadherins and catenins plays pivotal roles in ovarian cancer development and progression. Plakoglobin (PG, γ-catenin) is a paralog of β-catenin with dual adhesive and signaling functions. While β-catenin has known oncogenic function, PG generally acts as a tumor/metastasis suppressor. We recently showed that PG interacted with p53 and that its growth/metastasis inhibitory function may be mediated by this interaction. Very little is known about the role of PG in ovarian cancer. Here, we investigated the in vitro tumor/metastasis suppressor effects of PG in ovarian cancer cell lines with mutant p53 expression and different cadherin profiles. We showed that the N-cadherin expressing and E-cadherin and PG deficient ES-2 cells were highly migratory and invasive, whereas OV-90 cells that express E-cadherin, PG and very little/no N-cadherin were not. Exogenous expression of PG or E-cadherin or N-cadherin knockdown in ES-2 cells (ES-2-E-cad, ES-2-PG and ES-2-shN-cad) significantly reduced their migration and invasion. Also, PG expression or N-cadherin knockdown significantly decreased ES-2 cells growth. Furthermore, PG interacted with both cadherins and with wild type and mutant p53 in normal ovarian and ES-2-PG cell lines, respectively.
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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".