Wnt5A regulates the expression of ROR2 tyrosine kinase receptor in ovarian cancer cells
Post-publication record
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Bibliographic record
Abstract
Wnt5A and receptor tyrosine kinase-like orphan receptor 2 (ROR2) proteins both regulate developmental processes, cell movement, and cell polarity. The purpose of this study was to evaluate a possible regulatory role of Wnt5A on ROR2 expression in human ovarian cancer cell lines. Moreover, the expression of Wnt5A and ROR2 mRNA and protein levels were assessed in human epithelial serous ovarian cancer (HSOC) specimens. ROR2 was strongly decreased in cells treated with siRNA against Wnt5A compared with scramble-treated or lipofectamine-treated cells (P < 0.001). There was 34% decreased cell invasion (P < 0.01) in Wnt5A knock-down cells compared with lipofectamine-treated and scramble-treated cells; however, cell invasion remained unchanged upon addition of anti-ROR2 antibody to the culture media of these cells. In contrast, addition of anti-ROR2 antibody to the culture media for lipofectamine-treated and scramble-treated cells led to 32% decreased cell invasion (P < 0.01). Normal ovarian specimens were negative, and variable immunostaining was observed in HSOC for Wnt5A and ROR2 immunostaining. Furthermore, there was a positive correlation between Wnt5A and ROR2 expression in high-grade SOC samples at the mRNA level (P < 0.05; r = 0.38). This is the first report to show the regulatory role of Wnt5A on ROR2 expression in ovarian cancer.
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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 | 0.001 |
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".