Polymorphisms in the <i>FAS</i> and <i>FASL</i> Genes and Survival of Early Stage Non–small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: This study was conducted to investigate the impact of functional polymorphisms in the FAS and FASL genes on the survival of early stage non-small cell lung cancer (NSCLC) patients. EXPERIMENTAL DESIGN: Three hundred and thirty-eight consecutive patients with surgically resected NSCLC were enrolled. The FAS -1377G>A (rs2234767) and -670A>G (rs1800682) and FASL -844C>T (rs763110) polymorphisms were investigated. Immunohistochemistry was used to assess FAS protein expression in tumors. The genotype and haplotype associations with survival were analyzed using Cox proportional hazards model, Kaplan-Meier method, and the log-rank test. RESULTS: Patients with the GG and combined AG+GG genotypes of the FAS -670A>G locus had a significantly decreased survival when compared with patients with the AA genotype [adjusted hazard ratio=1.71, 95% confidence interval (95% CI)=1.06-2.77, and P=0.03; and adjusted hazard ratio=1.48, 95% CI=1.01-2.20, and P=0.047, respectively]. In addition, the FAS -1377G/-670G and -1377A/-670G haplotypes exhibited a significantly lower survival compared with the -1377G/-670A haplotype (adjusted hazard ratio=1.87, 95% CI=1.20-2.91, and P=0.006; and adjusted hazard ratio=1.31, 95% CI=1.05-1.65, P=0.02, respectively). Strongly positive FAS immunostaining was significantly less frequent in patients with the FAS -670 AG+GG genotype than in patients with the -670 AA genotype (4.5% versus 10.8%; P=0.04). CONCLUSION: The FAS -670A>G polymorphism may affect survival in early-stage NSCLC. The analysis of the FAS -670A>G polymorphism can help identify patients at high risk for a poor disease outcome.
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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 | 0.000 | 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".