Epithelial to mesenchymal transition (EMT) changes in patients with non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
Introduction: Our previous studies have shown active EMT in smokers and COPD patients, which is central to lung cancer development in these patients. Aim: We aim to evaluate EMT changes in extensive patient groups who were diagnosed with NSCLC (adenocarcinoma and squamous cell carcinoma) compared to normal controls (NC). Method: Resected lung tissue from NSCLC patients (n=35), sub-grouped as COPD current and ex-smokers, patients with small airway (SA) disease and normal lung function smokers compared to NC (n=11), were immuno-stained for EMT biomarkers: E-cadherin, N-cadherin, S100A4, Vimentin, and epidermal growth factor receptor (EGFR). Biomarkers were analysed in the SA epithelium and sub-epithelial layers. Tissue analysis was done with microscope-assisted Image-ProPlus 7.0 software. Results: Compared to NC, in all pathological groups, SA wall thickness was significantly increased (p<0.05); SA epithelial E-cadherin expression markedly decreased (p<0.01), whereas N-cadherin, Vimentin, S100A4, and EGFR expression were notably increased (p<0.01). Vimentin expression in sub-epithelium showed a similar trend to epithelium across all pathological groups (p<0.05). However, such changes were only seen in Rbm for S100A4 (p<0.05). EGFR and N-cadherin expressions in both cancer phenotypes were markedly higher than Vimentin and S100A4 (p<0.0001). EMT markers expression positively correlated to smoking history. Conclusion: EMT is a crucial and active process in NSCLC patients with COPD, resulting in SA remodelling and cancer development. This is the first study to show such changes in broadly phenotyped individuals, suggesting EMT as a key mechanism and novel therapeutic target.
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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".