Current insights into matrix metalloproteinases and glioma progression: transcending the degradation boundary
Bibliographic record
Abstract
Abstract: Glioblastoma multiforme (GBM) remains one of the most deadly cancers, with modest advances in overall survival despite significant improvements in imaging, surgery, and molecular genomic understanding. The highest-grade glioma, GBM is a primary brain cancer that is molecularly heterogeneous among patients and even within the same patient. Key hallmarks include glioma-cell invasion, angiogenesis, and therapeutic resistance. While once considered a major player in glioma invasion, members of the MMP family are also associated with other key pathological hallmarks of glioma. Investigations into understanding MMP function in GBM were slowed due to the failed MMP-inhibitor trials for GBM in the 2000s. In contrast, the field of MMPs in other brain pathologies has flourished in such areas as traumatic brain injury, multiple sclerosis, and stroke. In the past decade, the increase in publicly available data sets documenting patient-biopsy molecular information has empowered laboratory investigations into the spectrum of genomic, transcriptomic, and proteomic changes associated with glioma, including MMPs. In this review, we selected one of these data sets to illustrate a small sample of information that can be obtained from such analyses. Combined with recent reports on the use of MMP-cleavable peptides for imaging and the multifunctionality of MMPs, including intracellular nonproteolytic actions in various cell types, this paves the way for new avenues of MMP research. Understanding the function of MMPs in host–tumor interactions both spatially and temporally during tumor progression and in response to treatment will be crucial for the advancement of targeting specific MMPs in GBM. The opportunities to explore MMP regulation, expression, and function further in GBM have never been so great with progress in modern bioinformatics and molecular techniques, and it is hoped that advancements will translate in some way to patients diagnosed with GBM. Keywords: glioblastoma, matrix metalloproteinase, glioma, brain
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".