The Secretory Leukocyte Protease Inhibitor Is a Type 1 Insulin-Like Growth Factor Receptor–Regulated Protein that Protects against Liver Metastasis by Attenuating the Host Proinflammatory Response
Bibliographic record
Abstract
The secretory leukocyte protease inhibitor (SLPI) can attenuate the host proinflammatory response by blocking nuclear factor kappaB (NF-kappaB)-mediated tumor necrosis factor alpha (TNF-alpha) production in macrophages. We have previously shown that highly metastatic human and mouse carcinoma cells, on their entry into the hepatic microcirculation, trigger a rapid host proinflammatory response by inducing TNF-alpha production in resident Kupffer cells. Using GeneChip microarray analysis, we found that in mouse Lewis lung carcinoma subclones, SLPI expression was inversely correlated with tumor cell ability to induce a proinflammatory response and metastasize to the liver and with type 1 insulin-like growth factor receptor expression levels. To establish a causal relationship between SLPI expression and the metastatic phenotype, we generated, by transfection, multiple clones of the highly metastatic subline (H-59) that overexpress SLPI. We show here that the ability of these cells to elicit a host proinflammatory response in the liver was markedly decreased, as evidenced by reduced TNF-alpha production and vascular E-selectin expression, relative to controls. Moreover, these cells formed significantly fewer hepatic metastases (up to 80% reduction) as compared with mock-transfected controls. Our findings show that SLPI can decrease the liver-metastasizing potential of carcinoma cells and that this protective effect correlates with a decrease in the production of hepatic TNF-alpha and E-selectin. They suggest that factors that attenuate the host proinflammatory response may have a therapeutic potential in the prevention of liver metastasis.
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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 | 0.000 |
| 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) | 0.001 | 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".