The chemokine stromal cell derived factor 1α(SDF-1α )upregulates MT2-MMP expression in human glioma cells
Bibliographic record
Abstract
Proc Amer Assoc Cancer Res, Volume 45, 2004 1758 Chemokines have been found to alter tumor growth and metastasis. In previous work, we described that CXCR4 was predominantly expressed amongst several chemokine receptors in glioma cells (JBC 277:49481, 2002). As the only known ligand of CXCR4 is SDF-1, we tested the response of 2 CXCR4-bearing human glioma cell lines, LN827 and U373, to SDF-1α. We found that 100ng/ml SDF-1α increased the expression of transcripts encoding membrane-type 2-matrix metalloproteinase (MT2-MMP) (p<0.01) in both cell lines. This was selective in that the expression level of MT1-, MT3-, and MT5-MMP did not change in response to SDF-1α. Similarly, 2 growth factors of relevance to glioma cells, VEGF and HGF, did not vary when treated with SDF-1α. Flow cytometry and western blot analyses confirmed that SDF-1α upregulated MT2-MMP protein expression. Ongoing experiments on motility, chemotaxis and survival are aimed at addressing the phenotype of glioma cells responding to SDF-1α. We are also using RNAi, a simple and powerful gene silencing technique, to downregulate MT2-MMP so as to further analyze its functional role in glioma biology. Our results emphasize the need to understand the activities of CXCR4 in glioma cells, so that therapies aimed at CXCR4 and MT2-MMP may prove to be of utility for this incurable cancer.
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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) | 0.002 | 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".