A Genome-Wide Scan Identifies Variants in <i>NFIB</i> Associated with Metastasis in Patients with Osteosarcoma
Bibliographic record
Abstract
UNLABELLED: Metastasis is the leading cause of death in patients with osteosarcoma, the most common pediatric bone malignancy. We conducted a multistage genome-wide association study of osteosarcoma metastasis at diagnosis in 935 osteosarcoma patients to determine whether germline genetic variation contributes to risk of metastasis. We identified an SNP, rs7034162, in NFIB significantly associated with metastasis in European osteosarcoma cases, as well as in cases of African and Brazilian ancestry (meta-analysis of all cases: P = 1.2 × 10(-9); OR, 2.43; 95% confidence interval, 1.83-3.24). The risk allele was significantly associated with lowered NFIB expression, which led to increased osteosarcoma cell migration, proliferation, and colony formation. In addition, a transposon screen in mice identified a significant proportion of osteosarcomas harboring inactivating insertions in Nfib and with lowered NFIB expression. These data suggest that germline genetic variation at rs7034162 is important in osteosarcoma metastasis and that NFIB is an osteosarcoma metastasis susceptibility gene. SIGNIFICANCE: Metastasis at diagnosis in osteosarcoma is the leading cause of death in these patients. Here we show data that are supportive for the NFIB locus as associated with metastatic potential in osteosarcoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".