Biological Significance of the Proteasome Subunit LMP2/b1i as a Tumor Suppressor in Human Uterine Leiomyosarcoma
Bibliographic record
Abstract
Uterine leiomyosarcoma (Ut-LMS) develops more often in the muscle tissue layer of the uterine body than in the uterine cervix. The development of gynecologic tumors is often correlated with female hormone secretion; however, the development of Ut-LMS is not substantially correlated with hormonal conditions, and the risk factors are not yet known. Importantly, a diagnostic-biomarker which distinguishes malignant Ut-LMS from other uterine mesenchymal tumors including leiomyoma (LMA) is yet to be established. Accordingly, it is necessary to analyze risk factors associated with Ut-LMS, to establish a clinical treatment method. Proteasome subunit, low-molecular mass polypeptide(LMP2)/b1i-deficient mice spontaneously develop Ut-LMS, with a disease prevalence of ~40% by 14 months of age. Recent experiments with human and mouse uterine tissues revealed defective LMP2/b1i expression in human Ut-LMS that was traced to the interferon (IFN)-g pathway and a specific effect of Janus kinase (JAK)-1 somatic mutations on LMP2/b1i transcriptional activation. Furthermore, analysis of a human Ut-LMS cell line clarified the biological significance of LMP2/b1i in malignant myometrium transformation and the cell cycle, thus implicating LMP2/b1i as an anti-tumorigenic candidate. Therefore, defective-LMP2/b1i expression may be a risk factor for human Ut-LMS. LMP2/b1i is a potential diagnostic-biomarker for Ut-LMS, and may be a targeted-molecule for a new clinical therapeutic approach.
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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".