RET-Mediated Cell Adhesion and Migration Require Multiple Integrin Subunits
Bibliographic record
Abstract
CONTEXT: The RET receptor tyrosine kinase is an important mediator of several human diseases, most notably of neuroendocrine cancers. These diseases are characterized by aberrant cell migration, a process tightly regulated by integrins. OBJECTIVE: Our goals were to investigate the role of integrins in RET-mediated migration in two neoplastic cell models: the neural-derived cell line SH-SY5Y, and the papillary thyroid carcinoma cell line TPC-1. We also evaluated whether multiple integrin subunits have a role in RET-mediated cell migration. DESIGN: We evaluated the expression and activation of integrins in response to RET activation using standard cell adhesion and migration (wound-healing) assays. We examined focal adhesion formation, using integrin-paxillin coimmunoprecipitations and immunofluorescence, as an indicator of integrin activity. RESULTS: Our data indicate that β1 integrin (ITGB1) is expressed in both SH-SY5Y and TPC-1 cell lines and that these cells adhere strongly to matrices preferentially associated with ITGB1. We showed that RET can activate ITGB1, and that RET-induced cell adhesion and migration require ITGB1. Furthermore, we showed that β3 integrin (ITGB3) also plays a role in RET-mediated cell adhesion and migration in vitro and ITGB3 expression correlates with RET-mediated invasion in a mouse tumor xenograft model, suggesting that RET mediates the activity of multiple integrin subunits. CONCLUSIONS: Our data are the first to show that multiple integrin subunits contribute to cell adhesion and migration downstream of RET, suggesting that coordinated signaling through these pathways is important for cell interactions with the microenvironment during tumor invasion and progression.
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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".