RAGE Mediates the Pro-Migratory Response of Extracellular S100A4 in Human Thyroid Cancer Cells
Bibliographic record
Abstract
BACKGROUND: Expression of the small calcium-binding protein S100A4 is associated with poor prognosis in patients with thyroid cancer (TC). The authors have previously shown that S100A4 is a target for relaxin and insulin-like peptide 3 signaling in TC cells and that S100A4 is secreted from human TC cells. Although the pro-migratory role of intracellular S100A4 in binding to non-muscle myosin is well known, this study investigated here whether extracellular S100A4 contributes to TC migration. METHODS: Human cell lines of follicular, papillary, and undifferentiated thyroid cancer, primary patient TC cells, and TC tissues were utilized to discover the presence of the receptor of advanced glycation end products (RAGE) in TC cells and TC tissues. Fluorescence imaging, protein pull-down assays, Western blot, siRNA protein silencing, small GTPase inhibitors, cell proliferation, and cell migration assays were used to investigate the interaction of extracellular S100A4 with RAGE in promoting a TC migratory response. RESULTS: It was demonstrated that RAGE served as receptor for extracellular S100A4 mediating cell migration in TC cells. The RAGE-mediated increase in cell migration was dependent on the intracellular RAGE signaling partner diaphanous-1 (Dia-1) and involved the activation of the small GTPases Cdc42 and RhoA. Although extracellular S100A4 consistently activated ERK signaling in TC cells, it was shown that ERK signaling was not mediated by RAGE and not essential for the migratory response in TC cells. CONCLUSION: The data have identified the RAGE/Dia-1 signaling system as a mediator for the pro-migratory response of extracellular S100A4 in human TC. Thus, therapeutic targeting of the RAGE/Dia-1/small GTPases signaling may successfully reduce local invasion and metastasis in TC.
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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) | 0.002 | 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".