Thrombin Induces Tumor Cell Cycle Activation and Spontaneous Growth by Down-regulation of p27Kip1, in Association with the Up-regulation of Skp2 and MiR-222
Bibliographic record
Abstract
The effect of thrombin on tumor cell cycle activation and spontaneous growth was examined in synchronized serum-starved tumor cell lines and a model of spontaneous prostate cancer development in TRAMP mice. BrdUrd incorporation and propidium iodide staining of prostate LNCaP cells arrested in G(0) and treated with thrombin or serum revealed a 48- and 29-fold increase in S phase cells, respectively, at 8 hours. Similar results were obtained with TRAMP cells and a glioblastoma cell line, T98G. Cell cycle kinases and inhibitors in synchronized tumor cells revealed high levels of p27(Kip1) and low levels of Skp2 and cyclins D1 and A. Addition of thrombin, TFLLRN, or serum down-regulated p27(Kip1) with concomitant induction of Skp2, Cyclin D1, and Cyclin A with similar kinetics. LNCaP p27(Kip1)-transfected cells or Skp2 knockdown cells were refractory to thrombin-induced cell cycle activation. MicroRNA 222, an inhibitor of p27(Kip1), was robustly up-regulated by thrombin. The in vitro observations were tested in vivo with transgenic TRAMP mice. Repetitive thrombin injection enhanced prostate tumor volume 6- to 8-fold (P < 0.04). Repetitive hirudin, a specific potent antithrombin, decreased tumor volume 13- to 24-fold (P < 0.04). Thus, thrombin stimulates tumor cell growth in vivo by down-regulation of p27(Kip1).
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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".