Proliferative endocrine effects of adipose tissue from obese animals on MCF7 cells are ameliorated by resveratrol supplementation
Bibliographic record
Abstract
Obesity is clearly associated with an increased risk of breast cancer in postmenopausal women. The purpose was to determine if obesity alters the adipocyte adipokine secretion profile, thereby altering the adipose-dependent paracrine/endocrine growth microenvironment surrounding breast cancer cells (MCF7). Additionally, we determined whether resveratrol (RSV) supplementation can counteract any obesity-dependent effects on breast cancer tumor growth microenvironment. Obese ZDF rats received standard chow diet or diet supplemented with 200 mg/kg body weight RSV. Chow-fed Zucker rats served as lean controls. After 6 weeks, conditioned media (CM) prepared from inguinal subcutaneous adipose tissue (scAT) was added to MCF7 cells for 24 hrs. Experiments were also conducted using purified isolated adipocytes to determine whether any endocrine effects could be attributed specifically to the adipocyte component of adipose tissue. scAT from ZDF rats promoted cell cycle entry in MCF7 cells which was counteracted by RSV supplementation. RSV-CM had a higher ratio of ADIPO:LEP compared to ZDF-CM. This altered composition of the CM led to increased levels of pAMPKT172, p27, p27T198 and AdipoR1 while decreasing pAktT308 in MCF7 cells grown in RSV-CM compared to ZDF-CM. RSV-CM increased number of cells in G0/G1 and decreased cells in S-phase compared to ZDF-CM. Co-culture experiments revealed that these obesity-dependent effects were driven by the adipocyte component of the adipose tissue. Obesity decreased the ratio of adiponectin:leptin secreted by adipocytes, altering the adipose-dependent growth microenvironment resulting in increased breast cancer cell proliferation. Supplementation with RSV reversed these adipose-dependent effects suggesting a potential for RSV as a nutritional supplementation to improve breast cancer treatment in obese patients.
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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 | 0.001 |
| 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".