FGFR2 Isoforms Support Epithelial–Stromal Interactions in Thyroid Cancer Progression
Bibliographic record
Abstract
Alternate splicing yields two distinct isoforms of the fibroblast growth factor (FGF) receptor FGFR2-IIIb and FGFR2-IIIc varying their extracellular structure in human thyroid cancer, in which FGFR expression is commonly dysregulated. In this study, we characterized the function of these variants in modulating thyroid cancer behavior. Enforced expression of either FGFR2-IIIb or FGFR2-IIIc in thyroid epithelial cancer cells reduced expression of fibronectin, MAGE-A3 and MMP9, while increasing p21 and enhancing Rb dephosphorylation. Consistent with these tumor-suppressive properties, FGFR2-IIIb and FGFR2-IIIc each diminished invasive behavior in vitro and reduced tumor growth and metastasis in vivo. Notably, these effects contrasted with those produced by expression of these FGFR isoforms in fibroblasts, in which they both stimulated cell growth. Moreover, in xenograft tumors generated by coimplantation of epithelial and fibroblast cells expressing that same isoform, there was no significant effect on tumor progression. Conversely, FGFR2-IIIb expression in epithelial cells yielded higher FGF4/FGF7 expression that, in the presence of FGFR2-IIIc-expressing fibroblasts, enhanced tumor progression. Together, our findings highlight the importance of cellular context in assigning growth properties to growth factor receptor isoforms. More specifically, they show how alternative splicing of FGFR2 yields heteroisoforms critical to the growth-promoting actions of FGFs that exert distinct epithelial-stromal effects in thyroid cancer.
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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".