<scp>MMP</scp>‐2 regulates Src activation via repression of the <scp>CHK</scp>/<scp>MATK</scp> tumor suppressor in osteosarcoma
Bibliographic record
Abstract
BACKGROUND: Doxorubicin, a first-line anticancer drug for osteosarcoma treatment, has been the subject of recent research exploring the mechanisms behind its chemoresistance and its ability to enhance cell migration at sublethal concentrations. Matrix metalloproteinase-2 (MMP-2), a type IV collagenase and zinc-dependent endopeptidase, is well-known for degrading the extracellular matrix and promoting cancer metastasis. Our previous work demonstrated that nuclear MMP-2 regulates ribosomal RNA transcription via histone clipping, thereby controlling gene expression. Additionally, MMP-2 activity is regulated by the non-receptor tyrosine kinase and oncogene, Src, which plays a crucial role in cell adhesion, invasion, and metastasis. Src kinase is primarily regulated by two endogenous inhibitors: C-terminal Src kinase (Csk) and Csk homologous kinase (CHK/MATK). AIM: In this study, we reveal that the MMP-2 gene acts as an upstream regulator of Src kinase activity by suppressing its endogenous inhibitor, CHK/MATK, in osteosarcoma cells. METHODS AND RESULTS: We show that enhanced osteosarcoma cell migration which is induced by sublethal concentrations of doxorubicin can be overcome by inactivating the MMP-2 gene or overexpressing CHK/MATK. Our findings highlight the MMP-2 gene as a promising additional target for combating cancer cell migration and metastasis. This is due to its role in suppressing on the gene and protein expression of the tumor suppressor CHK/MATK in osteosarcoma. CONCLUSION: By targeting the MMP-2 gene, we can potentially enhance the effectiveness of doxorubicin treatment and reduce chemoresistance in osteosarcoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".