Reduced RBX1 expression induces chromosome instability and promotes cellular transformation in high-grade serous ovarian cancer precursor cells
Bibliographic record
Abstract
Despite high-grade serous ovarian cancer (HGSOC) being the most common and lethal gynecological cancer in women, the early etiological events driving disease development remain largely unknown. Emerging evidence now suggests that chromosome instability (CIN; ongoing changes in chromosome numbers) may play a central role in the development and progression of HGSOC. Importantly, genomic amplification of the Cyclin E1 gene (CCNE1) contributes to HGSOC pathogenesis in ~20% of patients, while Cyclin E1 overexpression induces CIN in model systems. Cyclin E1 levels are normally regulated by the SCF (SKP1-CUL1-FBOX) complex, an E3 ubiquitin ligase that includes RBX1 as a core component. Interestingly, RBX1 is heterozygously lost in ~80% of HGSOC cases and reduced expression corresponds with worse outcomes, suggesting it may be a pathogenic event. Using both short (siRNA) and long (CRISPR/Cas9) term approaches, we show that reduced RBX1 expression corresponds with significant increases in CIN phenotypes in fallopian tube secretory epithelial cells, a cellular precursor of HGSOC. Moreover, reduced RBX1 expression corresponds with increased Cyclin E1 levels and anchorage-independent growth. Collectively, these data identify RBX1 as a novel CIN gene with pathogenic implications for 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".