DNA methylation changes associated with risk factors in tumors of the upper aerodigestive tract
Bibliographic record
Abstract
Cancers of the upper aerodigestive tract (UADT) are common forms of malignancy associated with tobacco and alcohol exposures, although human papillomavirus and nutritional deficiency are also important risk factors. While somatically acquired DNA methylation changes have been associated with UADT cancers, what triggers these events and precise epigenetic targets are poorly understood. In this study, we applied quantitative profiling of DNA methylation states in a panel of cancer-associated genes to a case-control study of UADT cancers. Our analyses revealed a high frequency of aberrant hypermethylation of several genes, including MYOD1, CHRNA3 and MTHFR in UADT tumors, whereas CDKN2A was moderately hypermethylated. Among differentially methylated genes, we identified a new gene (the nicotinic acetycholine receptor gene) as target of aberrant hypermethylation in UADT cancers, suggesting that epigenetic deregulation of nicotinic acetycholine receptors in non-neuronal tissues may promote the development of UADT cancers. Importantly, we found that sex and age is strongly associated with the methylation states, whereas tobacco smoking and alcohol intake may also influence the methylation levels in specific genes. This study identifies aberrant DNA methylation patterns in UADT cancers and suggests a potential mechanism by which environmental factors may deregulate key cellular genes involved in tumor suppression and contribute to UADT cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".