Omega‐3 polyunsaturated fatty acids alter raft lipid composition and decrease epidermal growth factor receptor levels in lipid rafts of human breast cancer cells
Bibliographic record
Abstract
To determine the mechanism by which the n‐3 fatty acids (FA) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) decrease proliferation and induce apoptosis in MDA‐MB‐231 human breast cancer cells, we examined the effects of EPA and DHA on the lipid composition of lipid rafts as well as epidermal growth factor receptor (EGFR) raft localization and phosphorylation. N‐3 FA (a combination of EPA and DHA) inhibited the growth of MDA‐MB‐231 cells by 48–62% (p<0.05) in the presence and absence, respectively, of linoleic acid. EPA and DHA were incorporated significantly into lipid rafts isolated from MDA‐MB‐231 cells after treatment with n‐3 FA (p<0.05). EPA and DHA treatment significantly decreased (p<0.05) lipid raft sphingomyelin, cholesterol, and diacylglycerol content and, in the absence of linoleic acid, EPA and DHA increased raft ceramide levels. Furthermore, there was a marked decrease in EGFR levels in lipid rafts, accompanied by increases in the phosphorylation of both EGFR and p38 MAPK, in EPA/DHA‐treated cells. As sustained activation of the EGFR and p38 MAPK has been associated with apoptosis in human breast cancer cells, our results indicate that omega‐3 FA modify the lipid composition of membrane rafts and alter EGFR signaling in a way that decreases the growth of breast tumors.
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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 | 0.001 |
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".