Expression of Nicotinic Acetylcholine Receptor Subunit Genes in Non–Small-Cell Lung Cancer Reveals Differences between Smokers and Nonsmokers
Bibliographic record
Abstract
Nicotine and its derivatives, by binding to nicotinic acetylcholine receptors (nAChR) on bronchial epithelial cells, can regulate cellular proliferation and apoptosis via activating the Akt pathway. Delineation of nAChR subtypes in non-small-cell lung cancers (NSCLC) may provide information for prevention or therapeutic targeting. Expression of nAChR subunit genes in 66 resected primary NSCLCs, 7 histologically non-involved lung tissues, 13 NSCLC cell lines, and 6 human bronchial epithelial cell lines (HBEC) was analyzed with quantitative PCR and microarray analysis. Five nonmalignant HBECs were exposed to nicotine in vitro to study the variation of nAChR subunit gene expression with nicotine exposure and removal. NSCLCs from nonsmokers showed higher expression of nAChR alpha6 (P < 0.001) and beta3 (P = 0.007) subunit genes than those from smokers, adjusted for gender. In addition, nAChR alpha4 (P < 0.001) and beta4 (P = 0.029) subunit gene expression showed significant difference between NSCLCs and normal lung. Using Affymetrix GeneChip U133 Sets, 65 differentially expressed genes associated with NSCLC nonsmoking nAChR alpha6beta3 phenotype were identified, which gave high sensitivity and specificity of prediction. nAChR alpha1, alpha5, and alpha7 showed significant reversible changes in expression levels in HBECs upon nicotine exposure. We conclude that between NSCLCs from smokers and nonsmokers, different nAChR subunit gene expression patterns were found, and a 65-gene expression signature was associated with nonsmoking nAChR alpha6beta3 expression. Finally, nicotine exposure in HBECs resulted in reversible differences in nAChR subunit gene expression. These results further implicate nicotine in bronchial carcinogenesis and suggest targeting nAChRs for prevention and therapy in lung cancer.
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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".