p11 Regulates extracellular plasmin production and invasiveness of HT1080 fibrosarcoma cells
Bibliographic record
Abstract
The defining characteristic of a tumor cell is its ability to escape the constraints imposed by neighboring cells, invade the surrounding tissue, and metastasize to distant sites. This invasive property of tumor cells is dependent on activation of proteases at the cell surface. Most cancer cells secrete the urokinase-type plasminogen activator, which converts cell-bound plasminogen to plasmin. Here we address the issue of whether the plasminogen binding protein, p11, plays a significant role in this process. Transfection of human HT1080 fibrosarcoma cells with the human p11 gene in the antisense orientation resulted in a loss of p11 protein from the cell surface and concomitant decreases in cellular plasmin production, ECM degradation, and cellular invasiveness. The transfected cells demonstrated reduced development of lung metastatic foci in SCID mice. In contrast, HT1080 cells transfected with the p11 gene in the sense orientation displayed increased cell surface p11 protein and concomitant increases in cellular plasmin production, as well as enhanced ECM degradation and enhanced cellular invasiveness. The p11 overexpressing cells showed enhanced development of lung metastatic foci. These data establish that changes in the extracellular expression of the plasminogen receptor protein, p11, dramatically affect tumor cell-mediated pericellular proteolysis.
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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".