Novel genetic variants in the P38MAPK pathway gene <i>ZAK</i> and susceptibility to lung cancer
Bibliographic record
Abstract
The P38MAPK pathway participates in regulating cell cycle, inflammation, development, cell death, cell differentiation, and tumorigenesis. Genetic variants of some genes in the P38MAPK pathway are reportedly associated with lung cancer risk. To substantiate this finding, we used six genome‐wide association studies (GWASs) to comprehensively investigate the associations of 14 904 single nucleotide polymorphisms (SNPs) in 108 genes of this pathway with lung cancer risk. We identified six significant lung cancer risk‐associated SNPs in two genes (CSNK2B and ZAK) after correction for multiple comparisons by a false discovery rate (FDR) <0.20. After removal of three CSNK2B SNPs that are located in the same locus previously reported by GWAS, we performed the LD analysis and found that rs3769201 and rs7604288 were in high LD. We then chose two independent representative SNPs of rs3769201 and rs722864 in ZAK for further analysis. We also expanded the analysis by including these two SNPs from additional GWAS datasets of Harvard University (984 cases and 970 controls) and deCODE (1319 cases and 26 380 controls). The overall effects of these two SNPs were assessed using all eight GWAS datasets (OR = 0.92, 95%CI = 0.89‐0.95, and P = 1.03 × 10−5 for rs3769201; OR = 0.91, 95%CI = 0.88‐0.95, and P = 2.03 × 10−6 for rs722864). Finally, we performed an expression quantitative trait loci (eQTL) analysis and found that these two SNPs were significantly associated with ZAK mRNA expression levels in lymphoblastoid cell lines. In conclusion, the ZAK rs3769201 and rs722864 may be functional susceptibility loci for lung cancer risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".