Docosahexaenoic acid alters cell death, cancer and cell‐cycle signaling networks in breast cancer cell lines
Bibliographic record
Abstract
Long‐chain n‐3 polyunsaturated fatty acids reduce viability of breast cancer cells. While the products of several genes are altered, the changes in gene expression responsible for these antitumor effects have not been clearly established. The objective of this study was to measure alterations in gene expression in breast cancer cells treated with docosahexaenoic (DHA) and eicosapentaneoic (EPA) acid. MDA‐MB‐231 (estrogen receptor, ER‐) and MCF‐7 (ER+) human breast cancer cells were treated with control media, 100 μM linoleic acid, DHA or EPA for 48 h. Gene expression was analyzed using Affymetrix GeneChip Human Gene ST 1.0 microarray chips and data analyzed using Partek Genomics Suite and Ingenuity Pathway Analysis. Principal Component Analysis revealed that treatment accounted for 40% of the alteration in gene expression in both cell lines. Compared to control, fatty acids changed networks of lipid metabolism and small molecule biochemistry (P<0.05). DHA altered cancer, cell death and tumor morphology cellular signaling networks, compared with other conditions, while EPA impacted an array of non‐cancer related networks (P<0.05). A number of previously unidentified genes were differentially expressed in response to DHA treatment. This study provides an understanding of the genomic changes induced by n‐3 PUFA in breast cancer cells. Funding from CIHR and JBE received a CIHR postdoctoral fellowship.
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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 | 0.001 |
| 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".