Ubiquitin ligase RNF8 suppresses Notch signaling to regulate mammary development and tumorigenesis
Bibliographic record
Abstract
The E3 ubiquitin ligase RNF8 plays critical roles in maintaining genomic stability by promoting the repair of DNA double-strand breaks (DSBs) through ubiquitin signaling. Abnormal activation of Notch signaling and defective repair of DSBs promote breast cancer risk. Here, we found that low expression of the full-length RNF8 correlated with poor prognosis for breast cancer patients. Our data revealed that in addition to its role in the repair of DSBs, RNF8 regulated Notch1 signaling and cell-fate determination of mammary luminal progenitors. Mechanistically, RNF8 acted as a negative regulator of Notch signaling by ubiquitylating the active NOTCH1 protein (N1ICD), leading to its degradation. Consistent with abnormal activation of Notch signaling and impaired repair of DSBs in Rnf8-mutant mammary epithelial cells, we observed increased risk of mammary tumorigenesis in mouse models for RNF8 deficiency. Notably, deficiency of RNF8 sensitized breast cancer cells to combination of pharmacological inhibitors of Notch signaling and poly(ADP-ribose) polymerase (PARP), suggesting implications for treatment of breast cancer associated with impaired RNF8 expression or function.
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".