Docosahexaenoic Acid and Doxorubicin Act Synergistically to Disrupt Cell Cycle and Increase Cell Death of MDA‐MB‐231 Breast Cancer Cells
Bibliographic record
Abstract
We have previously demonstrated that incubating breast cancer cells with docosahexanoic acid (DHA) prior to treatment with doxorubicin (Dox) increased (P<0.05) the action of the drug (measured by cell death and apoptosis). The objective of the current study was to expolore how pre-treating MDA-MB-231 cells with DHA prior to Dox treatment alter gene expression. Cells were treated with control medium (containing 40 µM oleic acid / 40 µM linoleic acid) or DHA (60 µM) in control media for 48 hrs and then treated for 24 hrs with Dox (4.1 x 10-7M). RNA was extracted for microarray analysis (Affymetrix GeneChip Human Gene 2.0). A selection criterion of fold change 蠅 1.5 (p蠄0.05) was used to define up or down regulated genes in the Dox treated cells, compared to cells in control media (using Ingenuity Pathway Analysis software). DHA + Dox treated cells had more genes up or down regulated (155 and 184 respectively) than Control + Dox treated cells (131 and 150). Two canonical pathways found to have multiple genes up or down regulated by DHA + Dox were cell cycle and cell death. More specifically key genes involved in cell cycle were down regulated with DHA + Dox treated cells, but not Control + Dox included Cyclin B1 (-2.1), WEE (-1.5) and CDC25C (-1.8). In the apoptosis pathway, DHA +Dox treated cells had up regulated CD95 (1.3) and Caspase 10 (1.3) and down regulated BCL2 (-1.3). Changes in expression of these key genes were confirmed by protein quantification using western blot analysis. Our results suggest that treating MDA-MB-231 cells with DHA may be improving the cytotoxic effects of Dox through several cellular pahtways (cell cycle regulation and apoptosis). Funding from the Canadian Institutes of Health Research and the Alberta Canola Producers Commission.
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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".