Targeting the base excision repair pathway to overcome therapeutic resistance to alkylating agents in pediatric glioblastoma
Bibliographic record
Abstract
Understanding the molecular mechanism of invasion could help in designing novel therapeutic approaches in order to prevent the need for repeat surgery, decrease morbidity and improve patient survival.The aim of this study was to identify the key factors and underlying mechanisms which govern invasive properties of meningiomas.Methods: Towards this end, we performed gene expression profiling of invasive and non-invasive meningioma tumors.Matrix metalloproteinases (MMPs) 16 and 19 were among the genes associated with cell movement and invasion.Results & Discussion: We establish that the expression level of MMP16 was significantly elevated in both bone-invasive and brain invasive meningiomas.Gain-and loss-of-function experiments indicated a role for MMP16 in meningioma cell movement, invasion and tumor cell growth.Furthermore, MMP16 was shown to positively regulate MMP2, suggesting this mechanism may modulate meningioma invasion in invasive meningiomas.Conclusions: Overall, the results support a role for MMP16 in promoting invasive properties of the meningioma tumors.Further studies to explore the potential value for clinical use of matrix metalloproteinases inhibitors, perhaps specifically targeting MMP16 are warranted.
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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) | 0.001 | 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".