<scp>G</scp>enetic variants of JNK and p38α pathways and risk of non‐small cell lung cancer in an <scp>E</scp>astern <scp>C</scp>hinese population
Bibliographic record
Abstract
The JNK and p38α pathways play an important role in carcinogenesis. Therefore, we hypothesize that single nucleotide polymorphisms (SNPs) of genes involved in these pathways are associated with risk of lung cancer. We first selected and genotyped 11 independent SNPs of the JNK and p38α pathway-related genes in a discovery set of 1,002 non-small cell lung cancer (NSCLC) cases and 1,025 cancer-free controls of Eastern Chinese. Then, we validated those significant SNPs in a replication set of 1,333 NSCLC cases and 1,339 cancer-free controls of Eastern Chinese. Multifactor dimensionality reduction (MDR) and classification and regression tree (CART) analyses were used to identify interactions between significant SNPs and other covariates. In both discovery and replication as well as their pooled analysis, carriers of GADD45G rs8252T variant genotypes had a significantly lower risk of NSCLC (adjusted OR = 0.81 and 0.79, 95% CI = 0.72-0.92 and 0.64-0.99 and p = 0.001 and 0.040 for dominant and recessive genetic models, respectively) and carriers of MAP2K7 rs3679T variant genotypes had an increased risk of NSCLC (adjusted OR = 1.19 and 1.29, 95% CI = 1.05-1.34 and 1.09-1.54 and p = 0.005 and 0.004 for dominant and recessive genetic models, respectively). Furthermore, rs8252 variant CT/TT carriers showed significantly higher levels of GADD45G mRNA expression than CC carriers in the target tissues. We observed some evidence of interactions between rs8252 genotypes and sex in NSCLC risk. These results indicate that GADD45G rs8252 and MAP2K7 rs3679 SNPs may be susceptibility biomarkers for NSCLC in Eastern Chinese populations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".