Leptin induces migration and invasion of glioma cells through MMP‐13 production
Bibliographic record
Abstract
Leptin, the product of the obese gene, plays an important role in the regulation of body weight by coordinating metabolism, feeding behavior, energy balance, and neuroendocrine responses. However, regulation of leptin gene expression in the central nervous system is different from that in the adipocytes. In addition, leptin has been found in many tumor cell lines and has been shown to have mitogenic and angiogenic activity in a number of cell types. Glioma is the most common primary adult brain tumor with poor prognosis because of the spreading of tumor cell to the other regions of brain easily. Here we found that malignant C6 glioma cells expressed more leptin and leptin receptors than nonmalignant astrocytes. Furthermore, it was found that exogenous application of leptin enhanced the migration and invasion of C6 glioma cells. In addition, we found that the expression of matrix metalloproteinase-13 (MMP-13) but not of MMP-2 and MMP-9 was increased in response to leptin stimulation. The leptin-induced increase of cell migration and invasion was antagonized by MMP-13 neutralizing antibody or silencing MMP-13. The up-regulation of MMP-13 induced by leptin was mainly through p38 MAP kinase and NF-kappaB pathway. In addition, migration-prone sublines demonstrate that cells with increasing migration ability had more expression of MMP-13 and leptin. Taken together, these results indicate that leptin enhanced migration and invasion of C6 glioma cells through the increase of MMP-13 production.
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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 | 0.001 |
| 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".