Naringenin and 17β‐estradiol coadministration prevents hormone‐induced human cancer cell growth
Bibliographic record
Abstract
Flavonoids have been described as health-promoting, disease-preventing dietary components. In vivo and in vitro experiments also support a protective effect of flavonoids to reduce the incidence of certain hormone-responsive cancers. In particular, our previous results indicate that the flavanone naringenin (Nar), decoupling estrogen receptor alpha (ERalpha) action mechanisms, drives cancer cells to apoptosis. Because these studies were conducted in the absence of the endogenous hormone 17beta-estradiol (E2), the physiological relevance of these findings is not clear. We investigate whether the antiproliferative Nar effect persists in the presence of physiological E2 concentration (i.e. 10 nM), using both ERalpha-transfected (HeLa cells) and ERalpha-containing (HepG2 cells) cancer cell lines. Ligand saturation experiments indicate that Nar decreases the binding of E2 to ERalpha without impairing the estrogen response element (ERE)-driven reporter plasmid activity. In contrast, Nar stimulation prevents E2-induced extracellular regulated kinases (ERK1/2) and AKT activation and still induces the activation of p38, the proapoptotic member of mitogen-activating protein kinase (MAPK) family. As a consequence, Nar stimulation impedes the E2-induced transcription of cyclin D1 promoter and reverts the E2-induced cell proliferation, driving cancer cell to apoptosis. Thus, these results suggest that coexposure to this low-affinity, low-potency ligand for ERalpha specifically antagonizes the E2-induced ERalpha-dependent rapid signals by reducing the effect of the endogenous hormone in promoting cellular proliferation. As a whole, these data indicate that Nar is an excellent candidate as a chemopreventive agent in E2-dependent cancers.
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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) | 0.003 | 0.001 |
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".