The EphB4 receptor tyrosine kinase: a comparative study of the expression and function in canine and human osteosarcoma
Bibliographic record
Abstract
Osteosarcoma is a highly aggressive bone cancer in both canines and humans with a high rate of metastasis and corresponding poor prognosis. Advances in treatment options have been limited, highlighting the need for more effective therapeutic approaches. Osteosarcoma is physiologically and clinically similar between the two species, making it an ideal malignancy for investigation using a comparative oncology approach. The Eph receptor tyrosine kinases are overexpressed in many human malignancies and are associated with tumor aggressiveness, making these receptors attractive targets for therapeutic intervention. Recent evidence suggests that the EphB4 receptor is involved in the regulation of invasion and metastasis of various human cancers. However, the role of the EphB4 receptor in the fitness of human and canine osteosarcoma has been poorly evaluated. The aim of this project was to evaluate the expression of the EphB4 receptor in both canine and human osteosarcoma compared to normal osteoblast cells, and to investigate if silencing EphB4 in osteosarcoma cells affects the oncogenic activities, such as viability, migration, invasion, drug sensitivity, and propagation of tumor-initiating cells (TICs). Initially, I demonstrated upregulation of EphB4 in multiple canine and human osteosarcoma cell lines. EphB4 expression was silenced using specific shRNAs and stable cell lines were created. Subsequently, I investigated the effects of EphB4 inhibition on osteosarcoma aggressive traits, demonstrating reduced migration and invasion in both canine and human osteosarcoma cells, and reduced cell viability and enhanced sensitivity to cisplatin in human osteosarcoma cells. In addition, I found that EphB4 knockdown enhanced TIC proliferation in both canine and human osteosarcoma and promoted tumor initiation in mice. Tumor-initiating cells represent a slow-proliferating and drug resistant sub-population of cancer cells. Interestingly, this original finding suggests that EphB4 inhibition could conceptually make these cells more sensitive to DNA-damaging drugs. Overall, my findings demonstrate that the EphB4 receptor regulates important processes in the development and invasiveness of osteosarcoma and may be a promising target for therapy.
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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 | 0.001 | 0.001 |
| 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".