Integrin β7-mediated regulation of multiple myeloma cell adhesion, migration, and invasion
Bibliographic record
Abstract
Integrin-β7 (ITGB7) mRNA is detected in multiple myeloma (MM) cells and its presence is correlated with MAF gene activation. Although the involvement of several integrin family members in MM-stoma cell interaction is well documented, the specific biologic functions regulated by integrin-β7 in MM are largely unknown. Clinically, we have correlated integrin-β7 expression in MM with poor survival outcomes post autologous stem cell transplantation and postsalvage therapy with bortezomib. Functionally, we have found that shRNA-mediated silencing of ITGB7 reduces MM-cell adhesion to extra-cellular matrix elements (fibronectin, E-cadherin) and reverses cell-adhesion-mediated drug resistance (CAM-DR) sensitizing them to bortezomib and melphalan. In addition, ITGB7 silencing abrogated MM-cell transwell migration in response to SDF1α gradients, reduced vessel density in xenografted tumors, and altered MM cells in vivo homing into the BM. Mechanistically, ITGB7 knockdown inhibited focal adhesion kinase (FAK) and Src phosphorylation, Rac1 activation, and SUMOylation, reduced VEGF production in MM-BM stem cell cocultures and attenuated p65-NF-κB activity. Our findings support a role for integrin-β7 in MM-cell adhesion, migration, and BM homing, and pave the way for a novel therapeutic approach targeting this molecule.
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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".