High-Expression of PTEN and an Absence of PCNA in Osteoclast-Like Multinucleated Giant Cells of Giant Cell Tumors in Bone
Bibliographic record
Abstract
Giant cell tumors (GCTs) found in bone are so named for the conspicuous presence of numerous osteoclast-like multinucleated giant cells (OLMGCs). Although GCT studies have revealed that the OLMGCs are the cells responsible for tumor formation, these cells continue to receive a good deal of research attention. The tumor -suppressor gene, PTEN, is known to be involved in various malignancies. Recently, however, PTEN has been reported to be important for neuron enlargement and cardiomyocyte hypertrophy. Given the role of PTEN in both carcinomas as well as cell hypertrophy, we sought to elucidate the relationship between PTEN and OLMGCs. In this study, we confirmed the existence of PTEN in GCTs in bone using PCR. In particular, exons-3,4 and 5 of the PTEN gene was detected. Exons-3,4,5 of PTEN gene were found by PCR in all of 8 cases. Single cells microdissection was used to isolate OLMGCs from GCTs and verify the existence of the PTEN gene in the osteoclast-like multinucleated giant cells through PCR amplication of PTEN exon-3. Exon-3 of PTEN were detected by PCR in 5 of the 10 microdissected samples. PTEN mRNA expression was detected by in situ hybridization and the expressions of PTEN protein and proliferating cell nuclear antigen (PCNA) in GCTs were detected by immunohistochemistry. High expression levels of PTEN mRNA was detected only in OLMGCs in 23 of 27 GCT cases. Likewise,high expression of PTEN protein was also found only in OLMGCs in 21 of the 27 GCT cases and the giant cells did not express PCNA. In contrast, the neoplastic stromal cells with high PCNA labeling were almost always PTEN-negative by immunohistochemical staining. These results suggested that high-expression of PTEN in OLMGCs may involve in the formation size of GCTs.
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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 | 0.001 |
| 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".