Biomarkers of epithelial-mesenchymal transition: E-cadherin and beta-catenin in malignant transformation of oral lesions.
Bibliographic record
Abstract
Objective: Detecting oral lesions at high risk of becoming cancer may enable early interventions to prevent oral cancer. The diagnosis of dysplasia in an oral lesion is used to predict this risk but is subject to interobserver and intraobserver variability. Studying biomarkers or molecular markers that reflect underlying molecular alterations can serve as an additional and objective method of risk assessment. E-cadherin and beta-catenin, molecular markers of epithelial-mesenchymal transition (EMT), potentially contribute to early malignant progression in oral tissue. This narrative review provides an overview of EMT, its relation to oral cancer, and the interaction among E-cadherin, beta-catenin, and the Wnt pathway in malignant progression of oral tissue. Methods: Full-text literature on EMT, E-cadherin, beta-catenin, oral epithelial dysplasia, and oral cancer was retrieved from PubMed and Google Scholar. Results: Sixty original research articles, reviews, and consensus statements were selected for review. Discussion: EMT, a biological mechanism characterized by epithelial and mesenchymal changes, can contribute to cancer development. Molecular markers of EMT including TWIST, vimentin, and N-cadherin may serve as prognostic markers of oral cancer. Dependent on Wnt pathway activity and the loss of membranous E-cadherin, E-cadherin and beta-catenin can play various roles along the spectrum of malignant progression, including tumour inhibition, early tumour progression, and late-stage tumour progression. Cross-sectional immunohistochemical research has found changes in expression patterns of E-cadherin and beta-catenin from normal oral tissue, oral epithelial dysplasia, to oral squamous cell carcinoma. Conclusion: Future research should explore the longitudinal role of EMT markers in predicting malignant progression in oral tissue.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".