Clinical Significance of Epithelial–Mesenchymal Transition–Related Molecules in Lung Adenocarcinoma
Bibliographic record
Abstract
Background: Epithelial-mesenchymal transition (emt) refers to the biologic process in which epithelial cells are transformed into interstitial phenotypes by specific pathways. This transition plays an important biologic role in the process by which epithelium-derived malignant tumour cells acquire the ability to migrate and invade. We explored the relationship between emt-associated molecules and patient-related clinical factors to determine whether any clinical characteristics could be used as biomarkers for emt-related protein alterations in lung cancer-especially lung adenocarcinoma. Methods: Tumour specimens were collected from 80 patients with lung adenocarcinoma who underwent surgery or lung biopsy, with 4 patients being evaluated a 2nd time after re-biopsy. Expression of emt-related proteins, including E-cadherin and vimentin, was evaluated by immunohistochemistry. We analyzed the relationship between clinicopathologic characteristics and expression level of the emt markers. Results: Positive expression of E-cadherin was observed in 63 patients (79%), and vimentin, in 46 patients (57.5%). No significant relationships between E-cadherin or vimentin expression and smoking history, sex, age, driving gene mutations, or cell differentiation were identified. A significant correlation was observed between vimentin expression and pathologic stage. Of the 4 patients who were evaluated a 2nd time after re-biopsy, 3 showed the same emt-related protein expression status as in the first analysis. In the remaining patient, E-cadherin had changed completely. Conclusions: Clinicopathologic factors in cancer patients did not help to diagnose emt status in lung adenocarcinoma; however, TNM stage might be associated with vimentin expression.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".