Role of the E3 ubiquitin ligase RNF157 as a novel downstream effector linking PI3K and MAPK signaling pathways to the cell cycle
Bibliographic record
Abstract
The interconnected PI3K and MAPK signaling pathways are commonly perturbed in cancer. Dual inhibition of these pathways by the small-molecule PI3K inhibitor pictilisib (GDC-0941) and the MEK inhibitor cobimetinib (GDC-0973) suppresses cell proliferation and induces cell death better than either single agent in several preclinical models. Using mass spectrometry-based phosphoproteomics, we have identified the RING finger E3 ubiquitin ligase RNF157 as a target at the intersection of PI3K and MAPK signaling. We demonstrate that RNF157 phosphorylation downstream of the PI3K and MAPK pathways influences the ubiquitination and stability of RNF157 during the cell cycle in an anaphase-promoting complex/cyclosome–CDH1-dependent manner. Deletion of these phosphorylation-targeted residues on RNF157 disrupts binding to CDH1 and protects RNF157 from ubiquitination and degradation. Expression of the cyclin-dependent kinase 2 (CDK2), itself a downstream target of PI3K/MAPK signaling, leads to increased phosphorylation of RNF157 on the same residues modulated by PI3K and MAPK signaling. Inhibition of PI3K and MEK in combination or of CDK2 by their respective small-molecule inhibitors reduces RNF157 phosphorylation at these residues and attenuates RNF157 interaction with CDH1 and its subsequent degradation. Knockdown of endogenous RNF157 in melanoma cells leads to late S phase and G 2 /M arrest and induces apoptosis, the latter further potentiated by concurrent PI3K/MEK inhibition, consistent with a role for RNF157 in the cell cycle. We propose that RNF157 serves as a novel node integrating oncogenic signaling pathways with the cell cycle machinery and promoting optimal cell cycle progression in transformed cells.
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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".