Identification of Estrogen-Responsive Genes by Complementary Deoxyribonucleic Acid Microarray and Characterization of a Novel Early Estrogen-Induced Gene:<i>EEIG1</i>
Bibliographic record
Abstract
Estrogen receptors (ERs) are nuclear transcription factors that regulate gene expression in response to estrogen and estrogen-like compounds. Identification of estrogen-regulated genes in target cells is an essential step toward understanding the molecular mechanisms of estrogen action. Using cDNA microarray examinations, 19 genes were identified as induced by 17 beta-estradiol in MCF-7 cells, 10 of which have been reported previously to be estrogen responsive or to be linked with ER status. Five known estrogen-regulated genes, E2IG4, IGFBP4, SLC2A1, XBP1 and B4GALT1, and AFG3L1, responded quickly to estrogen treatment. A novel estrogen-responsive gene was identified and named EEIG1for early estrogen-induced gene 1. EEIG1 was clearly induced by 17 beta-estradiol within 2 h of treatment, and was widely responsive to a group of estrogenic compounds including natural and synthetic estrogens and estrogenic environmental compounds. EEIG1 was expressed in ER-positive but not in ER-negative breast cancer cell lines. EEIG1 expression was repressed by antiestrogens 4-OH-tamoxifen and ICI 182,780 but not by protein synthesis inhibitors cycloheximide and puromycin. These results provide evidence that some estrogenic compounds differentially enhance the transcription of estrogen-regulated genes and suggest a role for EEIG1 in estrogen action.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".