The Role of MMP8 in Cancer: A Systematic Review
Bibliographic record
Abstract
Matrix metalloproteinases (MMPs) have traditionally been considered as tumor promoting enzymes as they degrade extracellular matrix components, thus increasing the invasion of cancer cells. It has become evident, however, that MMPs can also cleave and alter the function of various non-matrix bioactive molecules, leading to both tumor promoting and suppressive effects. We applied systematic review guidelines to study MMP8 in cancer including the use of MMP8 as a prognostic factor or as a target/anti-target in cancer treatment, and its molecular mechanisms. A total of 171 articles met the inclusion criteria. The collective evidence reveals that in breast, skin and oral tongue cancer, MMP8 inhibits cancer cell invasion and proliferation, and protects patients from metastasis via cleavage of non-structural substrates. Conversely, in liver and gastric cancers, high levels of MMP8 worsen the prognosis. Expression and genetic alterations of MMP8 can be used as a prognostic factor by examination of the tumor and serum/plasma. We conclude, that MMP8 has differing effects on cancers depending on their tissue of origin. The use of MMP8 as a prognostic factor alone, or with other factors, seems to have potential. The molecular mechanisms of MMP8 in cancer further emphasize its role as an important regulator of bioactive molecules.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".