DNA repair in lung cancer: a large-scale quantitative analysis for polymorphisms in DNA repairing pathway genes and lung cancer susceptibility
Bibliographic record
Abstract
The results of associations between single nucleotide polymorphisms (SNPs) of genes in DNA repairing pathway and lung cancer (LC) risk are inconsistent. We applied allele, dominant and recessive models to explore the risk of researched variants to LC in total LC and subgroups by ethnicity or LC subtypes with a cutoff point of p < 0.05. A total of 76,935 cases and 88,649 controls from 192 articles were included. Among the analyzed 40 variants from 20 genes, we found 9 statistically significant variants in overall populations by allele model, including five SNPs (rs1760944, rs9344, rs13181, rs1001581, and rs915927) increasing LC risk (odd ratios [ORs] = 1.10–1.71) and four SNPs (rs1042522, rs3213245, rs11615, and rs238406) decreasing the risk (ORs = 0.75–0.94). We identified rs1042522 and rs13181 as significant variants for LC in three models. Additionally, we identified differential significant SNPs in ethnic and subtype’s analysis with comparison to total population. There are five SNPs in DNA repairing pathway associated with increased LC risk and four others decreased LC risk. Besides, the risky SNPs in different ethnicities and various LC subtypes were partly different, and the contribution of different genotypes to risk alleles were various as well.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".