Cytotoxic T-Lymphocyte Antigen-4 Polymorphisms and Susceptibility to Ewing's Sarcoma
Bibliographic record
Abstract
The development of Ewing's sarcoma (ES) is a complex process, resulting from interplay between mutations in oncogenes and tumor suppressors, host susceptibility factors, and cellular context. Cytotoxic T-lymphocyte antigen-4 (CTLA-4) plays important roles in downregulating the T-cell activation. Polymorphisms in the CTLA-4 gene have been shown to be associated with different autoimmune diseases and cancers. The current study evaluated the association of two CTLA-4 gene polymorphisms, -318C/T (rs5742909) and +49G/A (rs231775) with ES in the Chinese population. CTLA-4 polymorphisms were detected by polymerase chain reaction-restriction fragment length polymorphism in 223 ES cases and 302 age-matched healthy controls. Data were analyzed using the chi-square test. Results showed that prevalence of the CTLA-4 gene +49AA genotype and +49A allele were significantly increased in ES patients compared to controls (odds ratio [OR]=2.03, 95% confidence interval [CI], 1.13-3.66, p=0.018; and OR=1.33, 95%CI, 1.03-1.72, p=0.027). Also, subjects with CA (-318, +49) haplotype had a 1.37-fold increased risk to develop ES (p=0.032). In addition, ES patients with metastasis had higher numbers of +49AA genotype than those with localized cases (OR=2.66, 95%CI, 1.14-6.22, p=0.022). These results indicate that the CTLA-4+49G/A polymorphism is a new risk factor for ES and may affect the prognosis of this cancer.
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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 | 0.000 |
| 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".