Inhibition of human lung cancer cell proliferation and survival by wine
Bibliographic record
Abstract
BACKGROUND: Compounds of plant origin and food components have attracted scientific attention for use as agents for cancer prevention and treatment. Wine contains polyphenols that were shown to have anti-cancer and other health benefits. The survival pathways of Akt and extracellular signal-regulated kinase (Erk), and the tumor suppressor p53 are key modulators of cancer cell growth and survival. In this study, we examined the effects of wine on proliferation and survival of human Non-small cell lung cancer (NSCLC) cells and its effects on signaling events. METHODS: Human NSCLC adenocarcinoma A549 and H1299 cells were used. Cell proliferation was assessed by thymidine incorporation. Clonogenic assays were used to assess cell survival. Immunoblotting was used to examine total and phosphorylated levels of Akt, Erk and p53. RESULTS: In A549 cells red wine inhibited cell proliferation and reduced clonogenic survival at doses as low as 0.02%. Red wine significantly reduced basal and EGF-stimulated Akt and Erk phosphorylation while it increased the levels of total and phosphorylated p53 (Ser15). Control experiments indicated that the anti-proliferative effects of wine were not mediated by the associated contents of ethanol or the polyphenol resveratrol and were independent of glucose transport into cancer cells. White wine also inhibited clonogenic survival, albeit at a higher doses (0.5-2%), and reduced Akt phosphorylation. The effects of both red and white wine on Akt phosphorylation were also verified in H1299 cells. CONCLUSIONS: Red wine inhibits proliferation of lung cancer cells and blocks clonogenic survival at low concentrations. This is associated with inhibition of basal and EGF-stimulated Akt and Erk signals and enhancement of total and phosphorylated levels of p53. White wine mediates similar effects albeit at higher concentrations. Our data suggest that wine may have considerable anti-tumour and chemoprevention properties in lung cancer and deserves further systematic investigation in animal models of lung cancer.
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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".