γ-Tocotrienol-induced endoplasmic reticulum stress and autophagy act concurrently to promote breast cancer cell death
Bibliographic record
Abstract
The anticancer effects of γ-tocotrienol are associated with the induction of autophagy and endoplasmic reticulum (ER) stress-mediated apoptosis, but a direct relationship between these events has not been established. Treatment with 40 μmol/L of γ-tocotrienol caused a time-dependent decrease in cancer cell viability that corresponds to a concurrent increase in autophagic and endoplasmic reticulum (ER) stress markers in MCF-7 and MDA-MB-231 human breast cancer cells. γ-Tocotrienol treatment was found to cause a time-dependent increase in early phase (Beclin-1, LC3B-II) and late phase (LAMP-1 and cathepsin-D) autophagy markers, and pretreatment with autophagy inhibitors Beclin-1 siRNA, 3-MA or Baf1 blocked these effects. Furthermore, blockage of γ-tocotrienol-induced autophagy with Beclin-1 siRNA, 3-MA, or Baf1 induced a modest, but significant, reduction in γ-tocotrienol-induced cytotoxicity. γ-Tocotrienol treatment was also found to cause a decrease in mitogenic Erk1/2 signaling, an increase in stress-dependent p38 and JNK1/2 signaling, as well as an increase in ER stress apoptotic markers, including phospho-PERK, phospho-eIF2α, Bip, IRE1α, ATF-4, CHOP, and TRB3. In summary, these finding demonstrate that γ-tocotrienol-induced ER stress and autophagy occur concurrently, and together act to promote human breast cancer cell death.
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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) | 0.002 | 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".