A multi-omics study links TNS3 and SEPT7 to long-term former smoking NSCLC survival
Bibliographic record
Abstract
Abstract The genetic architecture of non-small cell lung cancer (NSCLC) is relevant to smoking status. However, the genetic contribution of long-term smoking cessation to the prognosis of NSCLC patients remains largely unknown. We conducted a genome-wide association study primarily on the prognosis of 1299 NSCLC patients of long-term former smokers from independent discovery (n = 566) and validation (n = 733) sets, and used in-silico function prediction and multi-omics analysis to identify single nucleotide polymorphisms (SNPs) on prognostics with NSCLC. We further detected SNPs with at least moderate association strength on survival within each group of never, short-term former, long-term former, and current smokers, and compared their genetic similarity at the SNP, gene, expression quantitative trait loci (eQTL), enhancer, and pathway levels. We identified two SNPs, rs34211819TNS3 at 7p12.3 (P = 3.90 × 10−9) and rs1143149SEPT7 at 7p14.2 (P = 9.75 × 10−9), were significantly associated with survival of NSCLC patients who were long-term former smokers. Both SNPs had significant interaction effects with years of smoking cessation (rs34211819TNS3: Pinteraction = 8.0 × 10−4; rs1143149SEPT7: Pinteraction = 0.003). In addition, in silico function prediction and multi-omics analysis provided evidence that these QTLs were associated with survival. Moreover, comparison analysis found higher genetic similarity between long-term former smokers and never-smokers, compared to short-term former smokers or current smokers. Pathway enrichment analysis indicated a unique pattern among long-term former smokers that was related to immune pathways. This study provides important insights into the genetic architecture associated with long-term former smoking NSCLC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".