Higher expression and activity of metalloproteinases in human cervical carcinoma cell lines is associated with HPV presence
Bibliographic record
Abstract
Matrix metalloproteinases (MMPs) MMP-2, MMP-9, and MT1-MMP are required for basement membrane degradation in cervical carcinoma. We evaluated the expression and activity of MMPs and their inhibitors RECK and TIMP-2 in 3 human invasive cervical carcinoma cell lines. Two HPV16-positive cell lines (SiHa and CaSki) and an HPV-negative cell line (C33A) were cultured either onto a type-I collagen gel, Matrigel, or plastic, to recreate their three-dimensional growth environment and evaluate the expression of these genes using quantitative real-time PCR. We also analyzed the gelatinolytic activity of MMP-2 and MMP-9 by zymography. We found that HPV (human papillomavirus)-positive cell lines express higher levels of MMP-2, MT1-MMP, and TIMP-2 than the HPV negative cell line. In addition, MMP-9 was expressed at very low levels in both HPV-negative and HPV-positive cell lines. We also observed that the expression of the RECK gene is higher in CaSki cells, being associated with higher pro-MMP-2 activity. Furthermore, Matrigel substrate influences MMP-2 expression in both SiHa and CaSki cells. On the other hand, we found that type-I collagen gel, but not Matrigel, can enhance pro-MMP-2 activity in all cell lines. Our results suggest that the presence of HPV is related to increased expression of MMP-2, MT1-MMP, and TIMP-2, and that pro-MMP-2 activity is higher in HPV-positive than in HPV-negative cells.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".