Cell adhesion induces p27Kip1-associated cell-cycle arrest through down-regulation of the SCFSkp2 ubiquitin ligase pathway in mantle-cell and other non-Hodgkin B-cell lymphomas
Bibliographic record
Abstract
Mounting evidence suggests that dynamic interactions between a tumor and its microenvironment play a critical role in tumor development, cell-cycle progression, and response to therapy. In this study, we used mantle cell lymphoma (MCL) as a model to characterize the mechanisms by which stroma regulate cell-cycle progression. We demonstrated that adhesion of MCL and other non-Hodgkin lymphoma (NHL) cells to bone marrow stromal cells resulted in a reversible G(1) arrest associated with elevated p27(Kip1) and p21 (WAF1) proteins. The adhesion-mediated p27(Kip1) and p21 increases were posttranslationally regulated via the down-regulation of Skp2, a subunit of SCF(Skp2) ubiquitin ligase. Overexpression of Skp2 in MCL decreased p27(Kip1), whereas inhibition of Skp2 by siRNA increased p27(Kip1) and p21 levels. Furthermore, we found cell adhesion up-regulated Cdh1 (an activating subunit of anaphase-promoting complex [APC] ubiquitin ligase), and reduction of Cdh1 by siRNA induced Skp2 accumulation and hence p27(Kip1) degradation, thus implicating Cdh1 as an upstream effector of the Skp2/p27(Kip1) signaling pathway. Overall, this report, for the first time, demonstrates that cell-cell contact controls the tumor cell cycle via ubiquitin-proteasome proteolytic pathways in MCL and other NHLs. The understanding of this novel molecular pathway may prove valuable in designing new therapeutic approaches for modifying tumor cell growth and response to therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".