P120-Catenin Isoforms 1 and 3 Regulate Proliferation and Cell Cycle of Lung Cancer Cells via β-Catenin and Kaiso Respectively
Bibliographic record
Abstract
BACKGROUND: The different mechanisms involved in p120-catenin (p120ctn) isoforms' 1/3 regulation of cell cycle progression are still not elucidated to date. METHODS AND FINDINGS: We found that both cyclin D1 and cyclin E could be effectively restored by restitution of p120ctn-1A or p120ctn-3A in p120ctn depleted lung cancer cells. When the expression of cyclin D1 was blocked by co-transfection with siRNA-cyclin D1 in p120ctn depleted cells restoring p120ctn-1A or 3A, the expression of cyclin E was slightly decreased, not increased, implying that p120ctn isoforms 1 and 3 cannot up-regulate cyclin E directly but may do so through up-regulation of cyclin D1. Interestingly, overexpression of p120ctn-1A increased β-catenin and cyclin D1 expression, while co-transfection with siRNA targeting β-catenin abolishes the effect of p120ctn-1A on up-regulation of cyclin D1, suggesting a role of β-catenin in mediating p120ctn-1A's regulatory function on cyclin D1 expression. On the other hand, overexpression of p120ctn isoform 3A reduced nuclear Kaiso localization, thus decreasing the binding of Kaiso to KBS on the cyclin D1 promoter and thereby enhancing the expression of cyclin D1 gene by relieving the repressor effect of Kaiso. Because overexpressing NLS-p120ctn-3A (p120ctn-3A nuclear target localization plasmids) or inhibiting nuclear export of p120ctn-3 by Leptomycin B (LMB) caused translocation of Kaiso to the nucleus, it is plausible that the nuclear export of Kaiso is p120ctn-3-dependent. CONCLUSIONS: Our results suggest that p120ctn isoforms 1 and 3 up-regulate cyclin D1, and thereby cyclin E, resulting in the promotion of cell proliferation and cell cycle progression in lung cancer cells probably via different protein mediators, namely, β-catenin for isoform 1 and Kaiso, a negative transcriptional factor of cyclin D1, for isoform 3.
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".