Molecular analyses of 6 different types of uterine smooth muscle tumors: Emphasis in atypical leiomyoma
Bibliographic record
Abstract
BACKGROUND: Uterine smooth muscle tumors (USMTs) constitute a group of histologic, genetic, and clinical heterogeneous tumors that include at least 6 major histologically defined tumor types: leiomyoma (ULM), mitotically active leiomyoma (MALM), cellular leiomyoma (CLM), atypical leiomyoma (ALM), uncertain malignant potential (STUMP), and leiomyosarcoma (LMS). Apart from ULM and LMS, the nature of these variants is not well defined. METHODS: A total of 167 cases of different USMT variants were collected, reviewed, and diagnostically confirmed based on the World Health Organization and Stanford schemes. These included 38 cases of LMS, 18 cases of STUMP, 42 cases of ALM, 22 cases of CLM, 7 cases of MALM, and 40 cases of ULM. Molecular analysis included selected microRNAs (miRNAs), oncogenes, and tumor suppressors that are highly relevant to USMT. RESULTS: Overall, 49% (17/35) of LMS cases and 7% (1/14) of STUMP cases died due to their USMT, but no deaths were attributed to ALM. miRNA profiling revealed that ALM and LMS shared similar miRNA signatures. P53 mutations and PTEN deletions were significantly higher in LMS, ALM, and STUMP compared with other USMT variants (P < .01). In contrast, MED12 mutations were extremely common in ULM and MALM (> 74%) but were significantly less common (< 15%) in CLM, ALM, STUMP, and LMS (P < .01). CONCLUSION: Six types of USMT have different gene mutation fingerprints. ALM shares many molecular alterations with LMS. Our findings suggest that ALM may be a precursor lesion of LMS or have similar genetic changes during its early stage.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".