Anti-Cancer Effects of a New Docosahexaenoic Acid Monoacylglyceride in Lung Adenocarcinoma
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. Despite advances in research, diagnosis and treatment, lung cancer remains a highly lethal disease, often diagnosed at advanced stages and with a very poor prognosis. Therefore, new strategies for the prevention and treatment of lung cancer are urgently needed. The aim of the present study was to determine the anti-tumorigenic effects of docosahexaenoic acid monoacylglyceride (MAG-DHA), a newly patented DHA derivative in lung adenocarcinoma. Our results demonstrate that MAG-DHA treatments decreased cell proliferation and induced apoptosis in A549 human lung carcinoma cells whereas MAG-DHA treatment did not induce apoptosis of normal bronchial epithelial BEAS-2B cells. MAG-DHA decreased NFκB activation leading to a reduction in COX-2 expression level in both A549 cells and lung adenocarcinoma tissues. Furthermore, MAG-DHA treatment increased PTEN expression and activation concomitant with a decrease in AKT phosphorylation levels and enhanced apoptosis. Oral administration of MAG-DHA significantly reduced tumor growth in a mouse A549 xenograft model. Lastly, MAG-DHA markedly decreased COX-2 and enhanced PTEN protein expression in tumor tissue sections. Altogether, these data provide new evidence regarding the mode of action of MAG-DHA and strongly suggest that this compound could be of clinical interest in cancer treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".