Hypoxia Enhances Metastatic Efficiency in HT1080 Fibrosarcoma Cells by Increasing Cell Survival in Lungs, Not Cell Adhesion and Invasion
Bibliographic record
Abstract
This study examined possible mechanisms for hypoxia-increased metastasis in a green fluorescent protein-labeled human fibrosarcoma cell line (HT1080). The efficiency of the lung arrest of tumor cells, which can be dependent on the adhesive potential of the tumor cells, was assessed by measuring the level of integrin alpha3beta1 protein and by adhesion assays, whereas the extravasation potential was examined by an invasion assay. These properties were not changed by exposure to hypoxia, indicating that lung arrest and extravasation are unlikely to play a major role in the effect of hypoxia on metastasis in this model. The main effect of hypoxic exposure was found to be increased survival after lung arrest as determined by clonogenic assay of tumor cells recovered from mouse lungs after i.v. injection. Concomitantly, apoptosis was identified as responsible for the death of lung-arrested cells, suggesting the involvement of an altered apoptotic response following hypoxic exposure of these cells. Consistent with this finding, we found that the effect of hypoxia on both increased metastasis and survival of arrested cells was inhibited by treatment with farnesylthiosalicylic acid. However, this effect was not due to down-regulation of hypoxia-inducible factor-1alpha, a mechanism of action of this drug reported by previous studies. Further detailed studies of the mechanisms of action of the drug are needed.
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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".