Coordinated expression of galectin‐3 and caveolin‐1 in thyroid cancer
Bibliographic record
Abstract
Galectin-3 (Gal3) is the single most accurate marker for the diagnosis of differentiated thyroid cancer (DTC). Gal3 overrides the tumour suppressor activity of caveolin-1 (Cav1) and functions in concert with Cav1 to promote focal adhesion turnover and tumour cell migration and invasion. To study their coordinated role in progression of a human cancer, we investigated the expression of Gal3 and Cav1 in specimens of human benign thyroid lesions, DTC and anaplastic thyroid cancer (ATC). Gal3 and Cav1 expression is significantly associated with DTC and ATC, but not benign nodules. Essentially all Cav1-positive DTC cancers express Gal3, supporting the synergistic activity of these two proteins in DTC progression. Similarly, coordinated elevated Gal3/Cav1 expression was observed in three DTC-derived cell lines (papillary TCP1 and KTC1 and follicular FTC133) but only one (ACT1) of five ATC-derived cell lines. Using siRNA knockdown, Gal3 and Cav1 were shown to be required for RhoA GTPase activation, stabilization of focal adhesion kinase (FAK; a measure of focal adhesion signalling and turnover) and increased migration of the DTC cell lines studied, but not the ATC cell lines, including ACT1, which expresses elevated levels of Gal3 and Cav1. Co-expression of Gal3 and Cav1 in the T238 anaplastic cell line stabilized FAK-GFP in focal adhesions. Gal3 and Cav1 therefore function synergistically to promote focal adhesion signalling, migration and progression of DTC.
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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) | 0.000 | 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".