Relationship between cyclooxygenase‐2 and human epidermal growth factor receptor 2 in vascular endothelial growth factor C up‐regulation and lymphangiogenesis in human breast cancer
Bibliographic record
Abstract
Both cyclooxygenase (COX)-2 and human epidermal growth factor receptor (HER)-2 promote breast cancer progression; however, the relationship between the two molecules remains unclear. We utilized human breast cancer tissues and cell lines to examine whether COX-2 and HER-2 played independent or interdependent roles in vascular endothelial growth factor (VEGF)-C up-regulation and lymphangiogenesis. A paired correlation of immunodetectable levels of COX-2, VEGF-C, and HER-2 proteins and lymphovascular density (LVD; D2-40-immunolabeled) in 55 breast cancer specimens revealed a positive correlation between COX-2 and HER-2 irrespective of clinicopathological status. However COX-2 alone positively correlated with LVD. In 10 independent specimens, mRNA levels showed a positive correlation between HER-2 and COX-2 or VEGF-C but not LYVE-1 (lymphovascular endothelial marker). These findings implicate COX-2, but not HER-2, in breast cancer-associated lymphangiogenesis. Manipulation of the COX-2 or HER-2 genes in breast cancer cell lines varying widely in COX-2 and HER-2 expression revealed a direct role of COX-2 and an indirect COX-2 dependent role of HER-2 in VEGF-C up-regulation: (i) high VEGF-C expression in high COX-2/low HER-2 expressing MDA-MB-231 cells was reduced by siRNA-mediated down-regulation of COX-2, but not HER-2; (ii) integration of HER-2 in these cells simultaneously up-regulated COX-2 protein as well as VEGF-C secretion; and (iii) low VEGF-C secretion by high HER-2/low COX-2 expressing SK-BR-3 cells was stimulated by COX-2 overexpression. These findings of the primary role of COX-2 and the COX-2-dependent role of HER-2, if any, in VEGF-C up-regulation and lymphangiogenesis suggest that COX-2 inhibitors may abrogate lymphatic metastasis in breast cancer irrespective of HER-2 status.
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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".