Role of S100 A7 as a diagnostic biomarker in oral potentially malignant disorders and oral cancer
Bibliographic record
Abstract
Background: S100 proteins have been implicated in the tumorigenesis of different human cancers and in oral dysplasia, as they are keratinocytes. Materials and Methods: In the present study, we have attempted to compare the expression of S100-A7 within young-onset (age ≤45 years, Group 1) oral squamous cell carcinoma (OSCC), OSCC in older age groups (age >45 years Group 2), oral potentially malignant disorders (OPMDs, Group 3) and inflammatory lesions (Group 4). The tissue sections were scored based on the percentage of immunostained cells and staining intensity. Nuclear, cytoplasmic and membrane immunoreactivity were also scored. Results: The present study comprised 153 histopathologically diagnosed case subjects of OSCC >45 years ( n = 41), OSCC <45 years ( n = 36), OPMD ( n = 40) and inflammatory lesions ( n = 36). The present study revealed a statistically significant difference of distribution with regard to S100A7 staining (cytoplasmic and nuclear) between OPMDs and OSCC ( P < 0.05). The nuclear, cytoplasmic and membrane staining as well as the staining intensity had significantly different scoring patterns among the OSCC group, OPMD group and the inflammatory lesions with the OSCC group having the highest scoring of the S100A7 staining (irrespective of the age). Conclusions: The present study concludes that S100A7 can be used as a diagnostic biomarker to differentiate between OPMDs and OSCC lesions. However, the marker is unable to distinguish between OSCCs in younger and older patients as the molecular pathogenesis of tumors in either of these age groups is probably similar.
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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 | 0.001 |
| 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".