Stathmin expression alters the antiproliferative effect of eribulin in leiomyosarcoma cells
Bibliographic record
Abstract
Uterine leiomyosarcoma is an aggressive soft tissue tumor. Stathmin, a phosphoprotein that modulates microtubule dynamics, is highly expressed in many malignancies including leiomyosarcoma. The microtubule-depolymerizing agent eribulin has been recently approved for treating malignant soft tissue tumors. Although eribulin inhibits microtubule polymerization, little is known about the relationship between eribulin treatment and stathmin dynamics. In this study, we explored the role of stathmin expression in the action of eribulin in leiomyosarcoma cells. Eribulin induced phosphorylation of stathmin and reduced expression of subunits A and C of protein phosphatase 2A (PP2A) in a leiomyosarcoma cell line. The PP2A activator FTY720 reduced levels of phosphorylated stathmin. Eribulin decreased stathmin protein levels without affecting stathmin mRNA expression. Furthermore, stathmin knockdown attenuated the inhibitory effects of eribulin on cell viability, whereas stathmin overexpression enhanced the anti-proliferative effect of eribulin. Eribulin-resistant leiomyosarcoma cell lines had enhanced expression of the class Ⅰ β-tubulin TUBB1, multi-drug resistance 1 protein MDR1 and breast cancer-resistance protein BCRP, and decreased expression of stathmin. Taken together, these results suggest that stathmin expression modulates the pharmacological efficacy of eribulin in uterine leiomyosarcoma cells.
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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".