Genistein and quercetin increase connexin43 and suppress growth of breast cancer cells
Bibliographic record
Abstract
Connexin proteins form gap junctions, which permit direct exchange of cytoplasmic contents between neighboring cells. Evidence indicates that gap junctional intercellular communication (GJIC) is important for maintaining homeostasis and preventing cell transformation. Furthermore, connexins may have independent functions including tumor growth suppression. Most tumors express less connexins, have reduced GJIC and have increased growth rates compared with non-tumorigenic cells. The purpose of this study was to determine whether common flavonoids, genistein and quercetin, increase connexin43 (Cx43) levels, improve GJIC and suppress growth of a metastatic human breast tumor cell line (MDA-MB-231). Quercetin (2.5, 5 microg/ml) and genistein (0.5, 2.5, 15 microg/ml) upregulated Cx43 but failed to increase GJIC. Cx43 localized to the plasma membrane following genistein treatment (2.5, 15 mug/ml). In contrast, Cx43 aggregated in the perinuclear region following quercetin treatment (0.5, 2.5, 5, 15 microg/ml). Both genistein (15 microg/ml) and quercetin (2.5, 5, 15 microg/ml) significantly reduced MDA-MB-231 cell proliferation. In summary, genistein and quercetin increase Cx43 and suppress MDA-MB-231 cell proliferation at physiologically relevant concentrations. These results demonstrate that genistein and quercetin are potential anti-breast cancer agents.
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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".