Evaluating the impact diminished SKP1 and CUL1 expression have on chromosome instability and high-grade serous ovarian cancer pathogenesis
Bibliographic record
Abstract
High-grade serous ovarian cancer (HGSOC) is the most common and lethal ovarian cancer subtype. 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. The current study aims to evaluate the underlying mechanisms responsible for inducing CIN in HGSOC. Importantly, overexpression of Cyclin E1 protein induces CIN and genomic amplification contributes to HGSOC pathogenesis in ~20% of patients. Misregulation of Cyclin E1 at the protein level (i.e. aberrant protein turnover) is also expected to be causally linked to CIN and HGSOC development, but has never been evaluated in this context. 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, loss of SCF complex function stemming from diminished SKP1 or CUL1 expression is predicted to underlie increases in Cyclin E1 protein levels and induce CIN. This study evaluates the impact of diminished SKP1 or CUL1 expression in a fallopian tube secretory epithelial cell model (a cell of origin for HGSOC) using two complementary approaches (siRNA and CRISPR/Cas9). Single-cell quantitative imaging microscopy approaches were employed to evaluate changes in CIN-associated phenotypes in response to diminished SKP1 or CUL1 expression. Our data identify SKP1 and CUL1 as novel CIN genes in HGSOC precursor cells that may contribute to the early development and pathogenesis of HGSOC.
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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 | 0.001 |
| 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".