Differential expression of miRNAs in primary canine appendicular osteosarcoma tissue and pulmonary metastases
Bibliographic record
Abstract
Canine appendicular osteosarcoma (OSA) is a highly metastatic tumor in dogs. Mortality due to metastatic disease is common and frequently occurs within 1 year of diagnosis despite standard-of-care treatment. Treatment includes amputation for palliation and chemotherapy for metastatic disease. Current histologic grading schemes and biomarkers are poor at predicting clinical outcome. Novel prognostic and therapeutic markers are required to improve patient care. MicroRNAs (miRNAs) are small molecules expressed by all cells and released into bodily fluids. Studies in human and canine OSA cell lines, tissues from the primary site, and blood have demonstrated the role of miRNAs in metastatic progression of OSA and its prognostication. We sought to investigate the miRNA profile of primary OSA tissue and compare it to pulmonary metastases and normal lung tissue by real-time quantitative polymerase chain reaction (PCR). Multiple miRNA and multiple variable models were investigated in primary OSA tissue to predict clinical outcome. Thirteen miRNAs had similar expression between primary and metastatic OSA but were different from normal lung tissue. MiR-9-5p, miR-196a-5p, and miR-196b were expressed in metastatic OSA but lacked expression in almost all normal lung samples. In multiple variable models for overall survival and disease-free interval, only miRNAs were selected as significant variables. This study found miRNAs that are nearly exclusively expressed in metastatic pulmonary OSA and could serve as novel therapeutic targets. MiRNAs were also found to be important prognostic biomarkers in tissue and improved prognostic ability as miRNA signatures.
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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".