S100A4 signaling in thyroid cancer
Bibliographic record
Abstract
Thyroid cancer (TC) is one of the most frequently occurring cancers of endocrine glands in Canada and the United States. Most differentiated TC are curable, but some TCs develop a more aggressive phenotype with resulting metastasis and treatment failures. Our group has shown that relaxin‐like peptides promote TC cell tumor growth and migration, in part mediated via S100A4 which is known to promote metastasis and to confer poor prognosis in patients with TC. Several S100 proteins signal via receptor for advanced glycation end products (RAGE). This study investigates S100A4 signaling in promoting TC cell survival, growth and metastasis. TC cells were stimulated with S100A4 to determine cell motility, proliferation and apoptosis and to identify S100A4‐induced signaling pathways. Using PCR and immunohistochemistry expression of RAGE was detected in TC cells and in TC tissues, but not in normal thyroid tissues. RAGE pull‐down experiments demonstrated S100A4 to bind to RAGE in TC. ERK and JNK/SAPK signaling pathways were activated in TC cells upon S100A4 stimulation. We used RAGE blocking antibodies and diaphanous‐1 knock‐down to prevent RAGE‐mediated S100A4‐signaling in TC. S100A4‐induced ERK‐signaling appeared to be independent of RAGE in TC. We identified TLR4 expression in TC cells and demonstrate here that the effects of S100A4 are, in part, mediated via TLR4 in TC. This research is funded by NSERC of Canada and Dept of surgery research fund. Grant Funding Source : n/a
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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 | 0.001 |
| 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".