Neuropeptide Y stimulates VEGF production and secretion and promotes angiogenesis in murine and human breast cancer
Bibliographic record
Abstract
The functional impact of NPY on breast cancer progression has been a question of growing interest. Our group has reported augmented mitogenic and migratory effects of NPY on breast cancer cells, however the anigogenic potential of NPY remains to be examined in models of breast cancer. In the current study we hypothesized that NPY promotes angiogenesis by stimulating the production and secretion of vascular endothelial growth factor (VEGF) from cancer cells. Endothelial tube formation assays (human umbilical vein endothelial cells: HUVEC) were conducted using control and NPY treated (10 −11 M to 10 −7 M for 24‐hr) conditioned media (CM) from 4T1 and MDA‐MB‐231 breast cancer cells. Compared to control, there was a concentration‐dependent increase in the number of endothelial tube branch‐points (BP) and endothelial cell networks (EN) in HUVEC exposed to NPY CM. The CM from 10 −8 M NPY treatment induced the greatest angiogenic response, which resulted in a 2‐fold & 5‐fold increase in number of BP (4T1 and MDA‐MB‐231 resp.) and 3‐ fold & 21‐fold increase in EN (4T1 and MDA‐MB‐231 resp.). Congruently, NPY treatment caused a 108% increase in VEGF expression in cancer cells and a 65% increase in VEGF secretion. These data support our ongoing in vivo studies showing a correlation between NPY availability and tumor angiogenesis; thus, highlighting an additional pathological implication of NPY in breast cancer. NSERC & CBCF.
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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".