SNAI1 expression and the mesenchymal phenotype: an immunohistochemical study performed on 46 cases of oral squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: SNAI1 can initiate epithelial-mesenchymal transition (EMT), leading to loss of epithelial characteristics and, in cancer, to invasion and metastasis. We hypothesized that SNAI1 reactivation occurs in oral squamous cell carcinoma (OSCC) where it might also be associated with focal adhesion kinase (FAK) expression and p63 loss. METHODS: Immunohistochemistry was performed on 46 tumors and 26 corresponding lymph node metastases. Full tissue sections were examined to account for rare and focal expression. Clinical outcome data were collected and analyzed. RESULTS: SNAI1-positivity (nuclear, >/= 5% tumor cells) was observed in 10 tumors and 5 metastases (n = 12 patients). Individual SNAI1(+) tumor cells were seen in primary tumors of 30 patients. High level SNAI1 expression (>10% tumor cells) was rare, but significantly associated with poor outcome. Two cases displayed a sarcomatoid component as part of the primary tumor with SNAI1(+)/FAK(+)/E-cadherin(-)/p63(-) phenotype, but disparate phenotypes in corresponding metastases. All cases had variable SNAI1(+) stroma. A mesenchymal-like immunoprofile in primary tumors characterized by E-cadherin loss (n = 29, 63%) or high cytoplasmic FAK expression (n = 10, 22%) was associated with N(+) status and tumor recurrence/new primary, respectively. CONCLUSIONS: SNAI1 is expressed, although at low levels, in a substantial proportion of OSCC. High levels of SNAI1 may herald a poor prognosis and circumscribed SNAI1 expression can indicate the presence of a sarcomatoid component. Absence of p63 in this context does not exclude squamous tumor origin. Additional EMT inducers may contribute to a mesenchymal-like phenotype and OSCC progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".