Galectin-7 Expression Potentiates HER-2-Positive Phenotype in Breast Cancer
Bibliographic record
Abstract
HER-2 positive tumors are among the most aggressive subtypes of breast cancer and are frequently associated with metastasis and poor outcome. As with other aggressive subtypes of breast cancer, these tumors are associated with abnormally high expression of galectin-7 (gal-7), which confers metastatic breast tumor cells with increased invasive behavior. Although previous studies in the rat model of breast tumorigenesis have shown that gal-7 is also increased in primary breast tumor, its contribution to the development of the primary breast tumors remains unclear. In the present work, we have used genetically-engineered gal-7-deficient mice to examine the role of gal-7 in the development of the mammary gland and of breast cancer. Using histological and immunohistological analysis of whole mammary glands at different stages of development, we detected no significant changes between normal and gal-7-deficient mice. To test the involvement of gal-7 in breast cancer, we next examined the effects of loss of gal-7 on mammary tumor development by crossing gal-7-deficient mice with the mammary tumor transgenic mouse strain FVB-Tg(MMTV-Erbb2)NK1Mul/J. Finally, assessment of mice survival and tumor volume showed a delay of mammary tumor growth in the absence of systemic gal-7. These data suggest that gal-7 could potentiate the phenotype of HER-2 positive primary breast cancer.
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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 | 0.001 |
| 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".