ArticleNAR genomics and bioinformatics2024
A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- TargetPrior: a miRNA-signature embedded evolutionary learning framework for prioritizing drug targets in acute myeloid leukemia.Bioinformatics (Oxford, England) · 2026Article
- Review
- Unraveling the molecular mechanisms of paclitaxel in high-grade serous ovarian cancer through network pharmacology.Scientific reports · 2025Article
- Prognostic Significance of CDK1 in Ovarian and Cervical Cancers.Journal of Cancer · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mucinous ovarian carcinoma (MOC) is a subtype of ovarian cancer that is distinct from all other ovarian cancer subtypes and currently has no targeted therapies. To identify novel therapeutic targets, we developed and applied a new method of differential network analysis comparing MOC to benign mucinous tumours (in the absence of a known normal tissue of origin). This method mapped the protein-protein network in MOC and then utilised structural bioinformatics to prioritise the proteins identified as upregulated in the MOC network for their likelihood of being successfully drugged. Using this protein-protein interaction modelling, we identified the strongest 5 candidates, CDK1, CDC20, PRC1, CCNA2 and TRIP13, as structurally tractable to therapeutic targeting by small molecules. siRNA knockdown of these candidates performed in MOC and control normal fibroblast cell lines identified CDK1, CCNA2, PRC1 and CDC20, as potential drug targets in MOC. Three targets (TRIP13, CDC20, CDK1) were validated using known small molecule inhibitors. Our findings demonstrate the utility of our pipeline for identifying new targets and highlight potential new therapeutic options for MOC patients.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.