Significance of EZH2 expression in canine mammary tumors
Bibliographic record
Abstract
BACKGROUND: Current studies report that aberrations in epigenetic regulators or chromatin modifications are related to tumor development and maintenance. EZH2 (Enhancer of zeste homolog 2) is one of the catalytic subunits of Polycomb repressive complex 2, a crucial epigenetic regulator. EZH2 has a master regulatory function in such processes as cell proliferation, stem cell differentiation, and early embryogenesis. In humans, EZH2 is linked to oncogenic function in several carcinomas, including breast cancer, and dysregulation of EZH2 has been particularly associated with loss of differentiation and the development of poorly differentiated breast cancer. In our present study, we were interested in determining whether EZH2 is increased in canine mammary tumors, which show similarities to human breast cancer. RESULTS: Investigation of the expression of EZH2 in canine mammary tumors revealed that EZH2 protein was overexpressed in canine mammary carcinomas, as in human breast cancer. In addition, the immunohistochemical expression level of EZH2 was associated with the degree of malignancy in canine mammary carcinoma. This is the first report to describe EZH2 expression in canine mammary tumors. CONCLUSIONS: Because the expression of EZH2 was similar in canine mammary carcinoma and human breast cancer, spontaneous canine mammary tumors may be a suitable model for studying EZH2 and treatment development.
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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 | 0.000 |
| 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".