Emerging roles of the cancerous inhibitor of protein phosphatase 2A (CIP2A) in ovarian cancer
Bibliographic record
Abstract
Ovarian cancer (OvCa) is the sixth most common gynaecological cancer in the UK, accounting for over 200,000 deaths worldwide. Cancerous Inhibitor of Phosphatase 2 A (CIP2A) is an oncoprotein and an endogenous inhibitor of PP2A. CIP2A is a key regulator for cellular processes (e.g. proliferation, DNA damage) and is involved in the progression of many malignancies. In this study we provide a comprehensive overview of its role in OvCa making use of in silico tools, clinical samples and in vitro models. CIP2A is overexpressed in OvCa patients, with metastatic patients having significantly higher expression when compared to patients with malignant and benign ovarian tumours. High CIP2A expression reduces both overall-and progression-free survival, whereas an R530T mutation is predicted to cause structural destabilisation of the CIP2A dimer. We also provide evidence for microRNA (miRNA) and mRNA target interactions with CIP2A. Finally, we have studied the effects of CIP2A inhibition in an in vitro BRCA2 model compared to BRCA2 wild-type OvCa cells, using RNA-sequencing. Gene enrichment pointed towards changes p53 pathway, protein metabolism, transporter activity, DNA replication, and cell cycle. Our data provide a novel insight into the role of CIP2A in OvCa and the potential of drug repurposing for therapeutic interventions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".