Increased cytoplasmic level of migfilin is associated with higher grades of human leiomyosarcoma
Bibliographic record
Abstract
AIMS: Leiomyosarcomas (LMS) are malignant neoplasms composed of cells that exhibit distinct smooth muscle differentiation. The molecular and cytogenetic features of LMS are complex and no consistent aberrations have been reported to date. Mitogen inducible gene-2 (Mig-2), kindlin and migfilin are recently identified cell-matrix adhesion proteins. The aim was to determine the expression and distribution of these proteins in human smooth muscle tumours of somatic soft tissue. METHODS AND RESULTS: Immunohistochemistry was performed on a human LMS tissue microarray and on sections of human leiomyomas (LM) and normal smooth muscle. Migfilin was barely detectable in normal smooth muscle cells, whereas increased levels of migfilin were observed in the majority of LM and LMS. Furthermore, the cytoplasmic level of migfilin was strongly associated with higher tumour grades. Additionally, the cytoplasmic levels of migfilin and Mig-2 were correlated with each other, suggesting an association between the two in the cytoplasm. Kindlin was expressed in normal smooth muscle, LM and LMS, and its level did not correlate with tumour grade. CONCLUSIONS: Our results suggest a role for cytoplasmic migfilin in the progression of LMS and identify cytoplasmic migfilin as a potentially important biological marker for human LMS progression.
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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 | 0.001 | 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) | 0.002 | 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".