A Role for Versican in the Development of Leiomyosarcoma
Bibliographic record
Abstract
Leiomyosarcoma (LMS) is a mesenchymal cancer that occurs throughout the body. Although LMS is easily recognized histopathologically, the cause of the disease remains unknown. Versican, an extracellular matrix proteoglycan, increases in LMS. Microarray analyses of 80 LMSs and 24 leiomyomas showed a significant elevated expression of versican in human LMS versus benign leiomyomas. To explore the importance of versican in this smooth muscle cell tumor, we used versican-directed siRNA to knock down versican expression in a LMS human cell line, SK-LMS-1. Decreased versican expression was accompanied by slower rates of LMS cell proliferation and migration, increased adhesion, and decreased accumulation of the extracellular matrix macromolecule hyaluronan. Addition of purified versican to cells expressing versican siRNA restored cell proliferation to the level of LMS controls, increased the pericellular coat and the retention of hyaluronan, and decreased cell adhesion in a dose-dependent manner. The presence of versican was not only synergistic with hyaluronan in increasing cell proliferation, but the depletion of versican decreased hyaluronan synthase expression and decreased the retention of hyaluronan. When LMS cells stably expressing versican siRNA were injected into nude mice, the resulting tumors displayed significantly less versican and hyaluronan staining, had lower volumes, and had reduced levels of mitosis as compared with controls. Collectively, these results suggest a role for using versican as a point of control in the management and treatment of LMS.
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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".