Relaxin Enhances S100A4 and Promotes Growth of Human Thyroid Carcinoma Cell Xenografts
Bibliographic record
Abstract
Relaxin increases cell motility and in vitro invasiveness in human thyroid carcinoma cells but the underlying molecular mechanisms of this action are largely unknown. In the present study, we show that relaxin transcriptionally upregulates the calcium-binding protein S100A4 (metastasin) and increases the cytosolic 10-kDa monomer and the 20-kDa dimer form of S100A4 in human thyroid carcinoma cells. The relaxin-induced increase in cell motility was blocked completely when S100A4 expression was diminished using an S100A4 small interfering RNA knockdown approach. We have shown previously the expression of the insulin-like family member relaxin in human thyroid carcinoma tissues but not in benign thyroid tissues. Human thyroid carcinoma tissues expressing relaxin also stained positive for S100A4. In nude mouse experiments, human thyroid carcinoma cell transfectants with constitutive expression of relaxin generated large and fast-growing tumors with significantly increased numbers of proliferating cells. We provide evidence in our cell model that the relaxin target protein S100A4 secreted by the thyroid carcinoma transfectants may not only enhance tumor cell motility but also promote xenograft angiogenesis as determined by the higher density of tumor microvessels and the angiogenic potential of S100A4 in in vitro tube formation assays. In conclusion, we have identified S100A4 as a major mediator of the actions of relaxin in thyroid carcinoma cell motility and in vivo thyroid tumor angiogenesis.
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 | 0.000 |
| 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 | 0.001 |
| 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".