Bone morphogenetic protein 4 (BMP-4) and epidermal growth factor (EGF) inhibit metalloproteinase-9 (MMP-9) expression in cancer cells.
Bibliographic record
Abstract
Matrix metalloproteinase-9 (MMP-9) plays a central role in the progression of the cancer. While a large number of studies have contributed to our understanding of the molecular mechanisms responsible for upregulating MMP-9 gene expression in normal and cancer cells, our knowledge on the signals that suppress MMP-9 expression is much more limited. Here, we report that EGF and BMP-4 cooperate to inhibit MMP-9 expression in cancer cells. Treatment with EGF reduces the expression of MMP-9 at both mRNA while augmenting BMP-4 expression. Interestingly, recombinant BMP-4 suppressed constitutive and PMA-induced MMP-9 expression in both fibrosarcoma and breast cancer cells. Addition of gremlin a natural inhibitor of BMP-4, inhibited the suppression of MMP-9 by EGF. The suppression of MMP-9 by BMP-4 likely occurs at the transcriptional level since BMP-4 suppressed MMP-9 mRNA expression and activation of a reporter vector encoding the human MMP-9 promoter. The suppressive effect of BMP-4 occurs via Smad1/5/8 and is specific since BMP-4 did not inhibit MMP-2 while BMP-2 was ineffective in suppressing MMP-9. Taken together, these results are consistent with a new paradigm for the role of EGF and BMPs in controlling MMP gene expression in cancer cells.
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) | 0.002 | 0.001 |
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".