The EphA2 Receptor Regulates Invasiveness and Drug Sensitivity in Canine and Human Osteosarcoma Cells
Bibliographic record
Abstract
Osteosarcoma is an aggressive bone cancer affecting both humans and dogs, often leading to pulmonary metastasis. Despite surgery and chemotherapy being the primary treatment modalities, survival rates remain low in both species, underscoring the urgent need for more efficacious therapeutic options. Accumulating evidence indicates numerous biological and clinical similarities between human and canine osteosarcoma, making it an ideal choice for comparative oncological research that should benefit both species. The EphA2 receptor has been implicated in controlling invasive responses across different human malignancies, and its expression is associated with poor prognosis. In this study, we utilized a comparative approach to match EphA2 functions in human and canine osteosarcoma models. Our objectives were to assess EphA2 levels and its pro-malignant action in osteosarcoma cells of both species. We found that EphA2 is overexpressed in most of both canine and human osteosarcoma cell lines, while its silencing significantly reduced cell viability, migration, and invasion. Moreover, EphA2 silencing enhanced the sensitivity of osteosarcoma cells to cisplatin, a drug commonly used for treating this cancer. Furthermore, inhibition of EphA2 expression led to a significant reduction in tumor development capability of canine osteosarcoma cells. Our data suggest that these EphA2 effects are likely mediated through various signaling mechanisms, including the SRC, AKT, and ERK-MAPK pathways. Collectively, our findings indicate that EphA2 promotes malignant behaviors in both human and canine osteosarcoma and that targeting EphA2, either alone or in combination with chemotherapy, could offer potential benefits to osteosarcoma patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".