Relaxin Enhances the Collagenolytic Activity and <i>In Vitro</i> Invasiveness by Upregulating Matrix Metalloproteinases in Human Thyroid Carcinoma Cells
Bibliographic record
Abstract
In this study, we identified differential expression of immunoreactive matrix metalloproteinase 2 (MMP2)/gelatinase A, membrane-anchored MT1-MMP/MMP14, and human relaxin-2 (RLN2) in human benign and malignant thyroid tissues. MMP2 and MT1-MMP were detected in the majority of thyroid cancer tissues and colocalized with RLN2-positive cells. MMP2 was mostly absent in goiter tissues and, similar to RLN2, may serve as a marker for thyroid cancer. MMP2 and MT1-MMP were identified as novel RLN2 targets. RLN2 caused a significant downregulation of tissue inhibitor of MMP (TIMP) 3 protein levels but did not change the expression levels of MMP13, and TIMP1, TIMP2, and TIMP4 in human thyroid carcinoma cells. RLN2 failed to affect the expression of MMP1, 3, 8, and 9 in the thyroid carcinoma cells investigated. Stable RLN2 transfectants secreted enhanced levels of bioactive MMP2 which contributed to the increased collagenolytic activity and in vitro invasiveness into collagen matrix by human thyroid cancer cells. Three-dimensional reconstitution of confocal fluorescent microscopy images revealed larger-sized invadopodia, with intense MT1-MMP accumulation at the leading migrating edge in RLN2 transfectants when compared with enhanced green fluorescent protein clones. In RLN2 transfectants actin stress fibers contributed to pseudopodia formation. In conclusion, enhanced tumor cell invasion by RLN2 involves the formation of MT1-MMP-enriched invadopodia that lead to increased collagenolytic cell invasion by human thyroid cancer 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 | 0.000 | 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".