Estrogen receptor β deficiency enhances small intestinal tumorigenesis in <i>Apc</i><sup>Min/+</sup> mice
Bibliographic record
Abstract
Clinical evidence suggests that estradiol replacement therapy reduces colon cancer risk in 'post'menopausal women. In colon epithelial cells, the estrogen receptor beta (ERbeta) is the predominant ER subtype and is thought to mediate the genomic effect of estrogens. The first aim of this study was to investigate the consequence of ERbeta deficiency on intestinal tumorigenesis in the Apc(Min/+) mouse model. Furthermore, to explore the biological mechanisms by which estrogens may influence the pathogenesis of colorectal cancer, we performed gene expression profiles in colonocytes from ovariectomized wild-type (WT) vs. ERbeta(-/-) mice, treated with estradiol (E(2)) or vehicle. Specifically in female, ERbeta deficiency was found to be associated with higher adenoma multiplicity in the small intestine, but not in the colon. Furthermore, tumors from ERbeta(-/-)Apc(Min/+) female mice were on average significantly larger than those from control Apc(Min/+) mice. Higher steady-state proliferation in epithelial cells of the jejunum and colon from ERbeta(-/-)Apc(Min/+) vs. Apc(Min/+) female mice was confirmed by BrdU incorporation assay. Interestingly, functional categorization of microarray results revealed the TGFbeta signaling pathway to be modulated in colonocytes, especially for the WT + E(2) vs. WT + Vehicle and the ERbeta(-/-) + E(2) vs. WT + E(2) comparisons. Using quantitative PCR analysis, we observed transcripts from ligands of the TGFbeta pathway to be upregulated in colonocytes from E(2)-treated WT and ERbeta(-/-) mice and downregulated in ERbeta-deficient mice, mostly in an E(2)-independent manner. Therefore, our results demonstrate that ERbeta deficiency enhances small intestinal tumorigenesis and suggest that modulation of the TGFbeta signaling pathway could contribute to the protective role of estrogens on intestinal tumorigenesis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".