MUC16 mucin (CA125) regulates the formation of multicellular aggregates by altering β-catenin signaling.
Bibliographic record
Abstract
After shedding from the primary tumor site, ovarian cancer cells form three-dimensional multicellular aggregates that serve as vehicle for cancer cell dissemination in the peritoneal cavity. MUC16 mucin (CA125) is aberrantly expressed by most advanced serous ovarian cancers and can promote proliferation, migration and metastasis. MUC16 associates with E-cadherin and β-catenin, two proteins involved in regulation of cell adhesion and the formation of multicellular aggregates. However, the role of MUC16 in the formation of multicellular aggregates remains to be defined. Here, we show that MUC16 alters E-cadherin cellular localization and expression. Consistent with this, MUC16 knockdown inhibited the formation of multicellular aggregates and, conversely, forced expression of MUC16 C-terminal domain (CTD) enhanced the formation of multicellular aggregates. MUC16 knockdown induces β-catenin relocation from the cell membrane to the cytoplasm, decreases its expression by increasing degradation and decreases β-catenin target gene expression. MUC16 CTD inhibits GSK-3β-mediated phosphorylation and degradation of β-catenin, leading to increased β-catenin levels. Importantly, knockdown of β-catenin inhibited multicellular aggregation. These findings indicate that MUC16 promotes the formation of multicellular aggregates by inhibiting β-catenin degradation.
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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".