A comparative study of the role EphA2 performs in canine and human osteosarcoma
Bibliographic record
Abstract
Osteosarcoma is an aggressive bone cancer in both humans and dogs with a high rate of pulmonary metastasis. Despite the current treatment options including tumor removal or limb amputation combined with chemotherapy, the survival time in both species is low. This highlights a need for more effective treatment options for this disease. Accumulating evidence suggests various biological and clinical similarities of osteosarcoma in humans and dogs, and therefore, this disease represents a suitable model for comparative oncological research with benefits to both parties. The EphA2 receptor, a member of the Eph family of receptor tyrosine kinases, has been shown to be upregulated in different human malignancies and its function has been linked to poor prognosis and tumor aggressive properties. Accordingly, this receptor is emerging as a potential target for the design of novel cancer therapies based on the inhibition of this molecule. However, the role of this receptor in osteosarcoma biology has been poorly studied, and to my knowledge, this receptor has not been studied in canine malignancies. My aim in this study was first to evaluate the expression of EphA2 in both canine and human osteosarcoma cells when compared to normal osteoblasts, and then to assess if silencing EphA2 results in significant alterations in the malignant characteristics of the osteosarcoma cells, including proliferation, migration, invasion, and drug sensitivity. Here, I present my novel findings, demonstrating that EphA2 is overexpressed in multiple canine and human osteosarcoma cell lines. Moreover, stably silencing EphA2 by specific shRNAs significantly and consistently decreased proliferation and migration of both human and canine osteosarcoma cells. I also showed a significant decrease in canine and human osteosarcoma invasion after EphA2 silencing when tested using a Matrigel invasion assay. In addition, I found that silencing EphA2 increased the sensitivity of both human and canine osteosarcoma cells to cisplatin, a common osteosarcoma treatment. Finally, the tumor development capability of osteosarcoma cells after EphA2 silencing was tested in two murine xenograft models of canine osteosarcoma. I observed that EphA2 silencing significantly reduced osteosarcoma tumour growth when compared to that of non-silenced control tumors. Taken together, this work suggests that EphA2 supports the malignant behaviors of both human and dog osteosarcoma, and that the inhibition of EphA2 alone or in combination with chemotherapy could conceptually benefit osteosarcoma cancer patients.
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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".