Omega‐3 Polyunsaturated Fatty Acids (n‐3 PUFAs) Decrease Growth and microRNA‐21 Expression of Estrogen Receptor‐Positive (ER+) MCF‐7 Breast Cancer Cells
Bibliographic record
Abstract
microRNA (miR)-21 plays a role in carcinogenesis and modulates genes regulating cell growth. Consumption of diets rich in the n-3 PUFA alpha-linolenic acid (ALA) in animals and humans results in elevations of serum ALA, docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA). Our objective was to determine in vitro the effect of ALA, alone and combined with EPA and DHA (at levels found in serum of animals or humans fed ALA-rich diets) on growth of MCF-7 cells and miR-21 expression changes as an underlying mechanism. Cells were treated with 112μM fatty acids as either ALA alone, or with EPA and DHA at ratios found in serum of animals (AnR; 1:0.4:3.1) or humans (HuR; 1:1:2.5) or control (no PUFA) plus 1nM estrogen, 40μM oleic acid and 40μM linoleic acid. Cell growth was assessed after 24 and 48 hours treatment. miR-21 expression was analyzed by qPCR after 1 hour treatment. ALA reduced growth by 9% and 28% at 24 and 48 hours, respectively. AnR and HuR reduced cell growth by 82% and 37%, respectively, at 24 hours, and 37% for both at 48 hours. No change in growth was observed at 1 hour. miR-21 displayed 0.83-fold, 0.25-fold and 0.58-fold changes in expression for ALA, AnR and HuR, respectively, following 1 hour treatment, with AnR miR-21 significantly downregulated (p=0.0097) compared to control and ALA. A significant relationship was observed between fatty acid effects on miR-21 at 1 hour and cell growth at 24 hours (R2= 0.9980; p= 0.0231). AnR contained the highest level of DHA, suggesting it is most active in reducing cell growth via effects on miR-21. ALA, when combined with DHA and EPA reduces the growth of ER+ breast cancer cells potentially through early modulation of miR-21. Funding: NSERC.
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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) | 0.002 | 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 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".