Gene expression studies provide clues to the pathogenesis of uterine leiomyoma: new evidence and a systematic review
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Bibliographic record
Abstract
BACKGROUND: Uterine leiomyomas are extremely common and a major cause of pelvic pain, bleeding, infertility, and the leading indication for hysterectomy. Familial and epidemiological studies provide compelling evidence that genetic alterations play an important role in leiomyoma development. METHODS: Using Affymetrix U133A GeneChip we analysed expression profiles of 22,283 genes in paired samples of leiomyoma and adjacent normal myometrium. We compared our results with previously published data on gene expression in uterine leiomyoma and identified the overlapping gene alterations. RESULTS: We detected 80 genes with average differences of > or = 2-fold and false discovery rates of < 5% (14 overexpressed and 66 underexpressed). A comparative analysis including eight previous gene expression studies revealed eight prominent genes (ADH1, ATF3, CRABP2, CYR61, DPT, GRIA2, IGF2, MEST) identified by at least five different studies, eleven genes (ALDH1, CD24, CTGF, DCX, DUSP1, FOS, GAGEC1, IGFBP6, PTGDS, PTGER3, TYMS) reported by four studies, twelve genes (ABCA, ANXA1, APM2, CCL21, CDKN1A, CRMP1, EMP1, ESR1, FY, MAP3K5, TGFBR2, TIMP3) identified by three studies, and 40 genes reported by two different studies. CONCLUSIONS: Review of gene expression data revealed concordant changes in genes regulating retinoid synthesis, IGF metabolism, TGF-beta signaling and extracellular matrix formation. Gene expression studies provide clues to the relevant pathways of leiomyoma development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.000 | 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 it