Abstract B114: Soy isoflavones mediated inhibition of DLD-1 human colon adenocarcinoma cells is associated with up-regulation of estrogen receptor-β expression
Bibliographic record
Abstract
Abstract B114 We have previously demonstrated that soy isoflavones elicit a colon cancer preventive effect when exposed throughout the lifetime of rats, inclusive of in utero and post-natal stages using an experimental model. We also showed that soy isoflavones increased the expression of colon tumor estrogen receptor (ER)-β, one of the main candidates in endocrine disruption during colon carcinogenesis. To further understand the relationship between the role of soy isoflavones and ER-β in colon carcinogenesis, we examined the effects of soy isoflavones in DLD-1 human colon adenocarcinoma cells in the presence of ER-β or when expression was decreased by RNA interference (siRNA) in vitro. DLD-1 cells were treated with increasing concentrations of soy isoflavones composed of genistein, daidzein and glycitein at a ratio of 1:1:0.2 representing human soy isoflavone-rich food intake. Markers of cell signaling were determined following treatment with or without soy isoflavones in the presence or absence of ER-β gene silencing. Soy isoflavones inhibited the growth of DLD-1 cells dose dependently, with an IC50 for cytotoxicity at 24.82 µg/mL and an IC50 for cell viability at 17.01 µg/mL. At sub-cytotoxic doses, soy isoflavones modulated the expression of markers associated with MAP kinase, AKT and NFκB signalling pathways, cell proliferation and cell cycle regulation, all conducive to a growth restrictive effect. A 75% knockdown of ER-β at the gene and protein level was achieved in DLD-1 cells using siRNA, and this caused a differential expression of the molecular markers studied above. Our results suggest that ER-β appears crucial in mediating the growth suppressive effects of soy isoflavones. ER-β may play an important role in the action of several endocrine disruptors including food chemical contaminants during colon carcinogenesis. Citation Information: Cancer Prev Res 2008;1(7 Suppl):B114.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".