Aberrant Expression of Cyr61, a Member of the CCN (CTGF/Cyr61/Cef10/NOVH) Family, and Dysregulation by 17β-Estradiol and Basic Fibroblast Growth Factor in Human Uterine Leiomyomas
Bibliographic record
Abstract
Uterine leiomyomas are the most common tumors of the reproductive tract, afflicting women between the ages of 30--55 yr. Although considered to be the leading cause of hysterectomies in the United States, little is known of the etiology and mechanisms of pathogenesis in leiomyomas. Accordingly, rapid analysis of differential expression (RADE) was employed to identify genes that are abnormally expressed in leiomyomas. Of the several genes identified, Cyr61, a member of the CCN family of growth and angiogenic regulators, was shown to be markedly down-regulated at the messenger ribonucleic acid (mRNA) and protein levels in leiomyoma tumors compared with the matched uterine myometrial controls (n = 38). In addition, in situ hybridization experiments corroborated the lack of Cyr61 expression in leiomyoma cells, whereas abundant transcript levels were identified in adjacent myometrial smooth muscle cells. To elucidate the mechanisms of Cyr61 gene regulation in leiomyomas, we determined the effects of ovarian steroids, basic fibroblast growth factor (bFGF), and serum, on Cyr61 expression using an ex vivo culture system. Treatment of human myometrial explants with 17 beta-estradiol and bFGF up-regulated Cyr61 transcripts, whereas the progesterone receptor agonist, R5020 (alone or in combination with 17 beta-estradiol), had no effect. Paradoxically, neither 17 beta-estradiol nor bFGF was capable of up-regulating Cyr61 mRNA in leiomyoma explants despite elevated levels of ER alpha mRNA, suggesting a possible defect in steroid and growth factor regulation. Thus, dysregulation of Cyr61 by estrogen and bFGF may contribute to down-regulation of Cyr61 in leiomyomas, which, in turn, may predispose uterine smooth muscle cells toward sustained growth.
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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 | 0.000 |
| 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".