Endothelin-1 (ET-1) promotes MMP-2 and MMP-9 induction involving the transcription factor NF-κB in human osteosarcoma
Bibliographic record
Abstract
In the present study, we have investigated the effect of (i) ET-1 (endothelin-1) and its precursor, big ET-1, on MMP (matrix metalloproteinase)-2 and MMP-9 synthesis and activity in osteosarcoma tissue, and (ii) ET-1 receptor antagonists on cell invasion. Using Western blotting, zymography, RT-PCR (reverse transcription-PCR), immunohistochemistry, immunofluorescence and Northern blotting, we have shown that ET-1 and ET-1 receptors (ET(A) and ET(B)) were expressed in these cells. Additionally, we have demonstrated that ET-1 markedly induced the synthesis and activity of MMP-2, which was significantly increased when compared with MMP-9. Furthermore, inhibition of NF-kappaB (nuclear factor kappaB) activation blocked MMP-2 production and activity, indicating the involvement of NF-kappaB, a ubiquitous transcription factor playing a central role in the differentiation, proliferation and malignant transformation. Since ET-1 acts as an autocrine mediator through gelatinase induction and because inhibition of ET(A) receptor is beneficial for reducing both basal and ET-1-induced osteosarcoma cell invasion, targeting this receptor could be an attractive therapeutic alternative for the successful treatment of osteosarcoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".