Journal article
A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma
O Craig, S Lee, C Pilcher, R Saoud, S Abdirahman, C Salazar, N Williams, DB Ascher, R Vary, J Luu, KJ Cowley, S Ramm, MX Li, N Thio, J Li, T Semple, KJ Simpson, KL Gorringe, JK Holien
Nar Genomics and Bioinformatics | OXFORD UNIV PRESS | Published : 2024
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, CC..
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Awarded by University of Melbourne
Funding Acknowledgements
We thank the following Peter MacCallum Cancer Centre Core Facilities: Bioinformatics and Molecular Genomics. These facilities are supported by the Peter MacCallum Foundation.