Molecular Signature of Smoking in Human Lung Tissues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cigarette smoking is the leading risk factor for lung cancer. To identify genes deregulated by smoking and to distinguish gene expression changes that are reversible and persistent following smoking cessation, we carried out genome-wide gene expression profiling on nontumor lung tissue from 853 patients with lung cancer. Gene expression levels were compared between never and current smokers, and time-dependent changes in gene expression were studied in former smokers. A total of 3,223 transcripts were differentially expressed between smoking groups in the discovery set (n = 344, P < 1.29 × 10(-6)). A substantial number of smoking-induced genes also were validated in two replication sets (n = 285 and 224), and a gene expression signature of 599 transcripts consistently segregated never from current smokers across all three sets. The expression of the majority of these genes reverted to never-smoker levels following smoking cessation, although the time course of normalization differed widely among transcripts. Moreover, some genes showed very slow or no reversibility in expression, including SERPIND1, which was found to be the most consistent gene permanently altered by smoking in the three sets. Our findings therefore indicate that smoking deregulates many genes, many of which reverse to normal following smoking cessation. However, a subset of genes remains altered even decades following smoking cessation and may account, at least in part, for the residual risk of lung cancer among former smokers. Cancer Res; 72(15); 3753-63. ©2012 AACR.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 it