Omega-3 Eicosapentaenoic Acid Reduces Prostate Tumor Vascularity
Bibliographic record
Abstract
Abstract The impact of omega (ω)-3 fatty acids on prostate cancer is controversial in epidemiological studies but experimental studies suggest a protective effect. However, little is known about the mechanism of action. Here, we studied the effects of purified fatty acid molecules on prostate tumor progression using the TRAMP-C2 syngeneic immunocompetent mouse model. Compared with ω-6 or ω-9–supplemented animals, we observed that late-stage prostate tumor growth was reduced with a monoacylglyceride (MAG)-conjugated form of eicosapentaenoic acid (EPA) supplementation, whereas docosahexanenoic acid (DHA) caused an early reduction. MAG–EPA significantly decreased tumor blood vessel diameter (P < 0.001). RNA sequencing analysis revealed that MAG–EPA downregulated angiogenesis- and vascular-related pathways in tumors. We also observed this tissue vascular phenotype in a clinical trial testing MAG–EPA versus a high oleic sunflower oil placebo. Using anti-CD31 IHC, we observed that MAG–EPA reduced blood vessel diameter in prostate tumor tissue (P = 0.03) but not in normal adjacent tissue. Finally, testing autocrine and paracrine effects in an avascular tumor spheroid growth assay, both exogenous MAG–EPA and endogenous ω3 reduced VEGF secretion and in vitro endothelial cell tube formation and blocked tumor spheroid growth, suggesting that ω3 molecules can directly hinder prostate cancer cell growth. Altogether, our results suggest that fatty acids regulate prostate cancer growth and that a tumor-specific microenvironment is required for the anti-vascular effect of MAG–EPA in patients with prostate cancer. Implications: Increasing the amount of ingested EPA omega-3 subtype for patients with prostate cancer might help to reduce prostate tumor progression by reducing tumor vascularization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".