Carnosic acid activates AMPK, inhibits Akt and inhibits H1299 human lung cancer cell survival
Bibliographic record
Abstract
Non‐small cell lung cancer (NSCLC) represents 80% of all lung cancers and is characterized by low survival rates due to chemotherapy and radiation resistance. Thus, finding alternative treatments to reduce NSCLC proliferation and survival is of high importance. The survival pathways of Akt, the energy sensor AMP‐activated protein kinase (AMPK), and the apoptotic protein poly (ADP‐ribose) polymerase (PARP) are key modulators of cancer cell growth and survival. In previous studies, we found inhibition of NSCLC proliferation and survival by rosemary extract but the exact components responsible for the anticancer effects and the cellular mechanisms involved are not known. Rosemary extract contains many polyphenols including carnosic acid (CA). The objectives of the present study were to examine the effects of CA on NSCLC cell survival and apoptosis, and to investigate its effects on Akt, AMPK and PARP. The human NSCLC cell line H1299 was used. Clonogenic cell survival assays were performed to examine the effects of CA on cell survival, and immunoblotting with phospho‐specific antibodies was performed to examine signaling events. CA dose‐dependently inhibited H1299 cell survival. A significant inhibition of Akt phosphorylation/activation and enhanced AMPK phosphorylation/activation was seen with 25 and 50 mM CA while the total levels of each protein were not affected. PARP cleavage, an indicator of apoptosis, was enhanced by carnosic acid treatment. Our findings indicate that CA may have robust anticancer properties in NSCLC, and strongly support the need for further studies to investigate its role against lung cancer. Support or Funding Information Supported by a Brock University Advancement Fund (BUAF) grant to ET This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".