Carboxypeptidase E-ΔN promotes migration, invasiveness, and epithelial–mesenchymal transition of human osteosarcoma cells via the Wnt–β-catenin pathway
Bibliographic record
Abstract
Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents, and metastatic OS is the major cause of OS-related death. Carboxypeptidase E (CPE) is known to be highly expressed in some cancer types, and its N-terminal truncated form, CPE-ΔN, is implicated in tumor metastasis and poor prognosis. In this study, we investigated the effect of CPE-ΔN on cell migration, invasiveness, and the epithelial-mesenchymal transition (EMT) of OS cells, and illustrated the molecular mechanisms. We first constructed CPE-ΔN overexpressing human OS cell lines (143B and U2OS cells), and found that ectopic CPE-ΔN expression in OS cells enhanced cell migration and invasiveness, and promoted the EMT process. Further, overexpression of CPE-ΔN increased the levels of c-myc and nuclear β-catenin in OS cells, which suggested the CPE-ΔN promotes activation of the Wnt-β-catenin pathway in OS cells. Treatment with β-catenin small interfering RNA (siRNA) inhibited the migration and invasiveness of CPE-ΔN-overexpressing cells, and reduced the expression of E-cadherin. Together, these results suggest that CPE-ΔN promotes migration, invasiveness, and the EMT of OS cells via the Wnt-β-catenin signaling pathway.
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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".