Siglec-15 as a New Perspective Therapy Target in Human Giant Cell Tumor of Bone
Bibliographic record
Abstract
The main features of a giant cell tumor of bone (GCTB) are frequent recurrence and aggressive osteolysis, which leads to a poor prognosis in patients. Although the treatment methods for a GCTB, such as scraping and resection, effectively inhibit the disease, the tendency toward malignant transformation remains. Therefore, it is important to identify new treatment methods for a GCTB. In this study, we first found high Siglec-15 expression in GCTB tissues, which was significantly associated with Campanacci staging and tumor recurrence. In Spearman's analysis, Siglec-15 expression was significantly correlated with Ki-67 levels in tumor tissues. In vitro, the mRNA and protein levels of Siglec-15 were high in GCTB stromal cells (Hs737. T), and Siglec-15 knockdown inhibited the biological characteristics of GCTB stromal cells. The RNA sequencing results enabled a prediction of the downstream genes by using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and MCODE analyses, and the findings showed that CXCL8 was significantly regulated by Siglec-15 and might be a promising downstream target gene of Siglec-15. Therefore, Siglec-15 may be a potential immunotherapy target for a GCTB.
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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".