Gene expression studies provide clues to the pathogenesis of uterine leiomyoma: new evidence and a systematic review
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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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".