S100A7 overexpression is a predictive marker for high risk of malignant transformation in oral dysplasia
Bibliographic record
Abstract
Early detection of oral lesions (OLs) at high risk of cancer development is of utmost importance for intervention. There is an urgent unmet clinical need for biomarkers that allow identification of high-risk OLs. Recently, we identified and verified a panel of five candidate protein biomarkers namely S100A7, prothymosin alpha, 14-3-3ζ, 14-3-3σ and heterogeneous nuclear ribonucleoprotein K using proteomics to distinguish OLs with dysplasia and oral cancers from normal oral tissues. The objective of our study was to evaluate the potential of these candidate protein biomarkers for identification of oral dysplastic lesions at high risk of cancer development. Using immunohistochemistry, we analyzed expressions of these five candidate protein biomarkers in 110 patients with biopsy-proven oral dysplasia and known clinical outcome and determined their correlations with p16 expression and HPV 16/18 status. Kaplan-Meier survival analysis showed reduced oral cancer-free survival (OCFS) of 68.6 months (p = 0.007) in patients showing cytoplasmic S100A7 overexpression when compared to patients with weak or no S100A7 immunostaining in cytoplasm (mean OCFS = 122.8 months). Multivariate Cox regression analysis revealed cytoplasmic S100A7 overexpression as the most significant candidate marker associated with cancer development in dysplastic lesions (p = 0.041, hazard ratio = 2.36). In conclusion, our study suggested the potential of S100A7 overexpression in identifying OLs with dysplasia at high risk of cancer development.
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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 | 0.001 |
| 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".