Molecular signatures of non-small cell lung cancer (NSCLC) obtained from gene expression profiling of the benign bronchial mucosa of smokers with and without NSCLC
Bibliographic record
Abstract
Background: Cigarette smoking accounts for 85-90% of lung cancers. Large-scale gene-expression profiling analyses have been undertaken to identify genes and pathways associated with lung carcinogenesis, and uncover lung cancer biomarkers. Tumor-distant, histologically normal bronchial biopsies have hitherto not been considered in such studies. Aims: In order to identify molecular signatures of smoking-related NSCLC, we have compared the gene expression profiles of histologically normal bronchial biopsies from current smokers with or without NCSCL, as well as non-smokers. Methods: RNA samples (97 biopsies) were used for hybridization with Affymetrix HG-U133 Plus 2.0 arrays. Differentially expressed genes were used to compare non-smokers (NS), smokers without cancer (SNC), and smokers with cancer (SC). Functional analysis was carried out using Ingenuity Pathway Analysis. Gene signatures of cigarette smoking and NSCLC were identified using the prediction analysis of microarray (PAM) method. Results: We identified 3 genes signatures that distinguished, respectively, SNC and NS (16 genes, 95.2% accuracy), SC and NS (8 genes, 100% accuracy), and SNC and SC (15 genes, 83% accuracy). This latter signature contains several genes that have been linked to lung disease/carcinogenesis, including genes encoding xenobiotic biotransformation proteins that protect the airway from the chemicals in cigarette smoke or contribute to lung carcinogenesis. Conclusions: Gene expression profiling of histologically normal bronchial biopsies resulted in a gene signature of NSCLC in smokers. Its potential as biomarker remains to be tested.
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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".