Lung cancer susceptibility and genetic polymorphisms of Exo1 gene in Taiwan.
Bibliographic record
Abstract
AIM: To evaluate the association between the polymorphisms of the Exo1 gene and the risk of lung cancer in central Taiwan. PATIENTS AND METHODS: In this hospital-based study, the association of Exol A-1419G (rs3754093), C-908G (rs10802996), A238G (rs1776177), C498T (rs1635517), K589E (rs1047840), G670E (rs1776148), C723R (rs1635498), L757P (rs9350) and C3114T (rs851797) polymorphisms with lung cancer risk in a central Taiwanese population was investigated. In total, 358 patients with lung cancer and 358 age- and gender-matched healthy controls recruited from the China Medical Hospital in central Taiwan were genotyped. RESULTS: A significantly different distribution was found in the frequency of the Exo1 K589E genotype, but not the other genotypes, between the lung cancer and control groups. The A allele Exo1 K589E conferred a significantly (p = 0.0097) increased risk of lung cancer. As for the rest of the polymorphisms, there was no difference in distribution between the lung cancer and control groups. Gene environment interactions with smoking were significant for Exo1 K589E polymorphism. The Exo1 K589E AG and AA genotype in association with smoking conferred an increased risk of 1.7208 (95% confidence interval = 1.2188-2.4295) for lung cancer. CONCLUSION: Our results provide the first evidence that the A allele of Exo1 K589E may be associated with the development of lung cancer and may be a novel useful marker for primary prevention and anticancer intervention.
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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 | 0.001 |
| 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".