Upregulation of cellular triacylglycerol – free fatty acid cycling by oleate is associated with long-term serum-free survival of human breast cancer cells
Bibliographic record
Abstract
We previously showed that exogenous oleate protects human breast cancer cells against palmitate-induced apoptosis in part by increasing esterification of this free fatty acid (FFA) into triacylglycerol (TG). Here, we studied the mechanism whereby oleate protects these cells against apoptosis induced by serum withdrawal. The metabolism of FFA, TG, and glucose, in parallel with long-term cell survival in the absence of serum, was investigated in a panel of human breast cancer cell lines and in nontransformed MCF-10A cells after treatment with exogenous oleate. Short-term (3-24 h) exposure of MDA-MB-231 human breast cancer cells to exogenous oleate resulted in a dose-dependent long-term (10 day) serum-free survival that correlated with the accumulation of TG in lipid droplets and with upregulation of lipolysis. Both effects persisted for several days after oleate removal. Rapid TG lipolysis and FFA re-esterification, supported by high rates of glycolysis that provide the glycerol backbone for TG synthesis, are consistent with the presence of very active TG-FFA cycling in human breast cancer cells. Only the cancer cell lines capable of accumulating TG showed long-term serum-free survival after oleate treatment. The results suggest that upregulation of TG-FFA cycling induced by oleate may be involved in maintenance of human breast cancer cell survival.
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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".