MMP2 expression is a prognostic marker for primary melanoma patients
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinase 2 (MMP2) is a collagenase, which aids tumor growth and invasion by digesting the extracellular matrix surrounding the tumor tissue. Our study examined MMP2 expression in various stages of melanoma progression and tested the prognostic significance of MMP2 expression. We also analyzed the correlation between p-Akt status and MMP2 expression in melanoma patients. METHODS: Tissue microarray (TMA) and immunohistochemistry were employed to study the expression of MMP2. A total of 482 melanoma (330 primary and 152 metastatic) tumor biopsies and 149 nevi biopsies (49 normal and 100 dysplastic nevi) were used for the analysis. MMP2 expression was correlated with melanoma progression. Kaplan-Meier survival curve and multivariate Cox regression analysis were applied to verify the prognostic significance of MMP2 expression. The correlation between MMP2 and p-Akt expression was analyzed in 92 cases which were common in the present and the previous study on p-Akt expression. RESULTS: Strong MMP2 expression is significantly increased in primary (25 %) and metastatic melanoma (43 %) compared to normal (5 %) and dysplastic nevi (10 %). Patients with strong MMP2 had significantly poorer survival compared to those with negative-to-moderate MMP2 expression. MMP2 expression could predict the patient survival independent of tumor thickness and ulceration. Furthermore, in our cohort study MMP2 expression was associated with p-Akt status and patient survival. CONCLUSIONS: Strong MMP2 staining is associated with worse survival of melanoma patients and is an independent molecular prognostic factor for primary melanoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".