Flaxseed and Its Lignans Inhibit Estradiol-Induced Growth, Angiogenesis, and Secretion of Vascular Endothelial Growth Factor in Human Breast Cancer Xenografts <i>In vivo</i>
Bibliographic record
Abstract
PURPOSE: Vascular endothelial growth factor (VEGF) is a potent stimulator of angiogenesis, which is crucial in cancer progression. We have previously shown that estradiol (E2) increases VEGF in breast cancer. Phytoestrogens are potential compounds in breast cancer prevention and treatment by poorly understood mechanisms. The main phytoestrogens in Western diet are lignans, and flaxseed is a rich source of the mammalian lignans enterodiol and enterolactone. EXPERIMENTAL DESIGN: In the present study, ovariectomized mice were treated with continuous release of E2. MCF-7 tumors were established and mice were fed with basal diet or 10% flaxseed, and two groups that were fed basal diet received daily injections with enterodiol or enterolactone (15 mg/kg body weight). RESULTS: We show that flaxseed, enterodiol, and enterolactone counteracted E2-induced growth and angiogenesis in solid tumors. Extracellular VEGF in vivo, sampled using microdialysis, in all intervention groups was significantly decreased compared with tumors in the basal diet group. Our in vivo findings were confirmed in vitro. By adding enterodiol or enterolactone, E2-induced VEGF secretion in MCF-7 cells decreased significantly without agonistic effects. The increased VEGF secretion by E2 in MCF-7 cells increased the expression of VEGF receptor-2 in umbilical vein endothelial cells, suggesting a proangiogenic effect by E2 by two different mechanisms, both of which were inhibited by the addition of lignans. CONCLUSIONS: Our results suggest that flaxseed and its lignans have potent antiestrogenic effects on estrogen receptor-positive breast cancer and may prove to be beneficial in breast cancer prevention strategies in the future.
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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".