The Effect of Tobacco Smoke N-Nitrosamines, NNK and NDEA, and Nicotine, on DNA Mismatch Repair Mechanism and miRNA Markers, in Hypopharyngeal Squamous Cell Carcinoma: An In Vivo Model and Clinical Evidence
Bibliographic record
Abstract
Deregulation of the DNA mismatch repair (MMR) mechanism has been linked to poor prognosis of upper aerodigestive tract cancers. Our recent in vitro data have provided evidence of crosstalk between deregulated miRNAs and MMR genes, caused by tobacco smoke (TS) N-Nitrosamines, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), in hypopharyngeal cells. Here, we explored whether chronic exposure to TS components can affect MMR mechanism and miRNA profiles in hypopharyngeal mucosa. Using a mouse model (C57Bl/6J wild type) of in vivo 14-week exposure to NNK (0.2 mmol/L) and N-Nitrosodiethylamine (NDEA; 0.004 mmol/L), with or without nicotine (0.02 μmol/L), we provide direct evidence that TS components can promote dysplasia, significant downregulation of Msh2 and Mlh1 genes and deregulation of miR-21, miR-155, miR-34a, and miR-451a. By analyzing eight human specimens from tobacco smokers and eight controls, we provide clinical evidence of a significant reduction in hMSH2 and hMLH1 mRNAs in hypopharyngeal squamous cell carcinoma (HSCC). In summary, deregulation of the MMR mechanism and miRNAs is caused by chronic exposure to TS-related N-Nitrosamines, with or without nicotine, in the early stages of upper aerodigestive tract carcinogenesis, and can also be detected in human HSCC. Thus, we encourage future studies to further elucidate a possible in vivo dose-dependent effect of individual or combined N-Nitrosamines, NNK and/or NDEA, and nicotine, on the MMR mechanism and their clinical testing to elaborate prognosis and risk assessment.
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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 | 0.000 | 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) | 0.001 | 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".