Paracrine effects of adipocytes on mammary epithelial cell cycle regulation
Bibliographic record
Abstract
To establish a molecular link between obesity and cancer we examined the paracrine role of adipocytes on MCF7 cell cycle regulation. Arrested MCF7 cells were treated with leptin (LEP) and adiponectin (ADIPO). p27 protein levels decreased and increased with LEP and ADIPO, respectively, suggesting adipokine‐mediated MCF7 cell cycle regulation. Addition of LEP overcame the effects of ADIPO on p27 protein levels. Selective JAK2 inhibition had no effect on LEP‐dependent cell cycle entry. Inhibition of AKT enhanced the ADIPO effects on p27 protein and prevented LEP‐dependent amelioration of ADIPO effects on p27 protein. Co‐culture of MCF7 cells with adipocytes from lean rats, which secreted low levels of LEP and high levels of ADIPO, increased p27 and decreased cyclin E protein while inducing cell cycle withdrawal, as measured by FACS analyses, in MCF7 cells. Co‐culture with adipocytes from obese rats, which secreted high LEP and low ADIPO, reduced p27 and cyclin E protein levels and induced MCF7 cell cycle entry. Addition of exogenous ADIPO to obese adipocyte co‐cultures inhibited the effects of these adipocytes on MCF7 cell cycle regulation, indicated by an increase in p27 and decrease in cyclin E protein and MCF7 cell cycle withdrawal. Our data suggest that ADIPO and LEP exert opposite effects on mammary cell cycle regulation and that altering the ratio of these adipokines has dramatic effects on MCF7 cell cycle status.
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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".