Estrogen Receptor-β Mediates the Inhibition of DLD-1 Human Colon Adenocarcinoma Cells by Soy Isoflavones
Bibliographic record
Abstract
To understand the relationship between the role of soy isoflavones and estrogen receptor (ER)-β in colon tumorigenesis, we investigated the cellular effects of soy isoflavones (composed of genistein, daidzein, and glycitein) in DLD-1 human colon adenocarcinoma cells with or without ER-β gene silencing by RNA interference (RNAi). Soy isoflavones decreased the expression of proliferating cell nuclear antigen (PCNA), extracellular signal-regulated kinase (ERK)-1/2, AKT, and nuclear factor (NF)-κB. Soy isoflavones dose-dependently caused G2/M cell cycle arrest and downregulated the expression of cyclin A. This was associated with inhibition of cyclin dependent kinase (CDK)-4 and up-regulation of its inhibitor p21(cip1) expressions. ER-β gene silencing lowered soy isoflavone-mediated suppression of cell viability and proliferation. ERK-1/2 and AKT expressions were unaltered and NF-κB was modestly upregulated by soy isoflavones after transient knockdown of ER-β expression. Soy isoflavone-mediated arrest of cells at G2/M phase and upregulation of p21(cip1) expression were not observed when ER-β gene was silenced. These findings suggest that maintaining the expression of ER-β is crucial in mediating the growth-suppressive effects of soy isoflavones against colon tumors. Thus upregulation of ER-β status by specific food-borne ER-ligands such as soy isoflavones could potentially be a dietary prevention or therapeutic strategy for colon cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".