Investigation of FGFR2-IIIC Signaling via FGF-2 Ligand for Advancing GCT Stromal Cell Differentiation
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Giant cell tumor of bone (GCT) is an aggressive bone tumor consisting of multinucleated osteoclast-like giant cells and proliferating osteoblast-like stromal cells. The signaling mechanism involved in GCT stromal cell osteoblastic differentiation is not fully understood. Previous work in our lab reported that GCT stromal cells express high levels of TWIST1, a master transcription factor in skeletal development, which in turn down-regulates Runx2 expression and prevents terminal osteoblastic differentiation in these cells. The purpose of this study was to determine the upstream regulation of TWIST1 in GCT cells. Using GCT stromal cells obtained from patient specimens, we demonstrated that fibroblast growth factor receptor (FGFR)-2 signaling plays an essential role in bone development and promotes differentiation of immature osteoblastic cells. Fibroblast growth factor (FGF)-2 stimulates FGFR-2 expression, resulting in decreased TWIST1 expression and increased Runx2, alkaline phosphastase (ALP) and osteopontin (OPN) expression. Inhibition of FGFR-2 through siRNA decreased the expression of ALP, Runx2 and OPN in GCT stromal cells. Our study also confirmed that FGF-2 ligand activates downstream ERK1/2 signaling and pharmacological inhibition of the ERK1/2 signaling pathway suppresses FGF-2 stimulated osteogenic differentiation in these cells. Our results indicate a significant role of FGFR-2 signaling in osteoblastic differentiation in GCT stromal 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".