Secretory Leukocyte Protease Inhibitor (SLPI) Expression and Tumor Invasion in Oral Squamous Cell Carcinoma
Bibliographic record
Abstract
Differential expression of secretory leukocyte protease inhibitor (SLPI) impacts on tumor progression. SLPI directly inhibits elastase and other serine proteases, and regulates matrix metalloproteinases, plasminogen activation, and plasmin downstream targets to influence invasion. We examined tissues from human oral squamous cell carcinoma (OSCC) for SLPI expression in parallel with proteases associated with tumor progression and evaluated their relationships using tumor cell lines. Significantly decreased SLPI was detected in OSCC compared to normal oral epithelium. Furthermore, an inverse correlation between SLPI and histological parameters associated with tumor progression, including stage of invasion, pattern of invasion, invasive cell grade, and composite histological tumor score was evident. Conversely, elevated plasmin and elastase were positively correlated with histological parameters of tumor invasion. In addition to its known inhibition of elastase, we identify SLPI as a novel inhibitor of plasminogen activation through its interaction with annexin A2 with concomitant reduced plasmin generation by macrophages and OSCC cell lines. In an in vitro assay measuring invasive activity, SLPI blocked protease-dependent tumor cell migration. Our data suggest that SLPI may possess antitumorigenic activity by virtue of its ability to interfere with multiple requisite proteolytic steps underlying tumor cell invasion and may provide insight into potential stratification of oral cancer according to risk of occult metastasis, guiding treatment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".