Aberrant methylation of multiple genes in the upper aerodigestive tract epithelium of heavy smokers
Bibliographic record
Abstract
An important method for silencing tumor suppressor genes in cancers is by aberrant methylation (referred to as methylation) of CpG islands in gene promoter regions. In lung cancer, methylation of the genes retinoic acid receptor beta-2 (RARbeta-2), CDH13 (H-cadherin), p16(INK4a) (p16), RASSF1A (RAS association domain family I) is frequent. Thus, we investigated methylation of these genes in 4 different types of specimens (oropharyngeal brushes, sputum samples, bronchial brushes and bronchioloalveolar lavage [BAL] samples) of the upper aerodigestive tract epithelium from heavy smokers without evidence of cancer but with morphometric evidence of sputum atypia and compared the frequencies of methylation in the different types of specimens. In addition, we also analyzed sputum samples from 30 never smokers for methylation of these genes. Our major findings are: (i) At least one gene was methylated in one or more specimens from 48% of the smokers. However, methylation was statistically significant less frequently in never smokers compared to smokers. (ii) In general, methylation occurred more frequently in samples from the central airways (sputum, bronchial brushes) compared to the peripheral airways (BAL) and only occasionally in the oropharynx. (iii) RARbeta-2 was the most frequently methylated gene, whereas the frequency of methylation for the other genes was lower. (iv) Data from sputum samples and bronchial brushes were comparable. Our findings suggest that detection of methylation should be investigated as an intermediate marker for lung cancer risk assessment and response to chemopreventive regimens.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".