Docosahexaenoic Acid Incorporation Is Not Affected by Doxorubicin Chemotherapy in either Whole Cell or Lipid Raft Phospholipids of Breast Cancer Cells <i>in vitro</i> and Tumor Phospholipids <i>in vivo</i>
Bibliographic record
Abstract
To better understand how docosahexaenoic acid (DHA) improves the effects of doxorubicin (DOX), we examined DHA ± DOX on changes in whole cell and lipid raft phospholipids (PL) of MDA-MB-231 and MCF-7 breast cancer cells. We sought to confirm whether the relative changes in PL DHA content of MDA-MB-231 cells could be extended to PL from MDA-MB-231 tumors grown in mice fed a DHA supplemented diet ±DOX. Treatment with DHA did not change PL composition yet DOX increased the proportion of phosphatidylserine in MCF-7 cell lipid rafts by two-fold (p < 0.001). Regardless of DOX, the relative percent incorporation of DHA was higher in MDA-MB-231 cells compared to MCF-7 cells in phosphatidylserine, phosphatidylethanolamine, and phosphatidylcholine (whole cell and lipid rafts); and higher in phosphatidylethanolamine vs. phosphatidylcholine (4.4-fold in MCF-7 and 6-fold in MDA-MB-231 cells respectively). DHA treatment increased eicosapentaenoic acid and docosapentaenoic acid in MDA-MB-231 cells but not MCF-7 cells. Increased DHA content in MDA-MB-231 cells, MCF-7 cells, and MDA-MB-231 tumors in all PL moieties (except sphingomyelin) corresponded with reduced arachidonic acid (p < 0.05). Feeding mice 2.8% (w/w of fat) DHA ± DOX increased tumor necrotic regions (p < 0.05). This study established differential incorporation of DHA into whole cell and lipid rafts between human breast cancer cell lines. However, within each cell line, this incorporation was not altered by DOX confirming that DOX does not change membrane lipid composition. Furthermore, our findings indicate that membrane changes observed in vitro are translatable to in vivo changes and that DHA + DOX could contribute to the anticancer effects through increased necrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".