Reduced SKP1 and CUL1 expression underlies increases in Cyclin E1 and chromosome instability in cellular precursors of high-grade serous ovarian cancer
Bibliographic record
Abstract
BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most common and lethal ovarian cancer histotype. Chromosome instability (CIN, an increased rate of chromosome gains and losses) is believed to play a fundamental role in the development and evolution of HGSOC. Importantly, overexpression of Cyclin E1 protein induces CIN, and genomic amplification of CCNE1 contributes to HGSOC pathogenesis in ~20% of patients. Cyclin E1 levels are normally regulated in a cell cycle-dependent manner by the SCF (SKP1-CUL1-FBOX) complex, an E3 ubiquitin ligase that includes the proteins SKP1 and CUL1. Conceptually, diminished SKP1 or CUL1 expression is predicted to underlie increases in Cyclin E1 levels and induce CIN. METHODS: This study employs fallopian tube secretory epithelial cell models to evaluate the impact diminished SKP1 or CUL1 expression has on Cyclin E1 and CIN in both short-term (siRNA) and long-term (CRISPR/Cas9) studies. RESULTS: Single-cell quantitative imaging microscopy approaches revealed changes in CIN-associated phenotypes and chromosome numbers and increased Cyclin E1 in response to diminished SKP1 or CUL1 expression. CONCLUSIONS: These data identify SKP1 and CUL1 as novel CIN genes in HGSOC precursor cells that may drive early aetiological events contributing to HGSOC development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".