title: Ovarian Cancer Differential Interactome and Network Entropy Analysis Reveal New Candidate Biomarkers.
abstract: Ovarian cancer is one of the most common cancers and has a high mortality rate due to insidious symptoms and lack of robust diagnostics. A hitherto understudied concept in cancer pathogenesis may offer new avenues for innovation in ovarian cancer biomarker development. Cancer cells are characterized by an increase in network entropy, and several studies have exploited this concept to identify disease-associated gene and protein modules. We report in this study the changes in protein-protein interactions (PPIs) in ovarian cancer within a differential network (interactome) analysis framework utilizing the entropy concept and gene expression data. A compendium of six transcriptome datasets that included 140 samples from laser microdissected epithelial cells of ovarian cancer patients and 51 samples from healthy population was obtained from Gene Expression Omnibus, and the high confidence human protein interactome (31,465 interactions among 10,681 proteins) was used. The uncertainties of the up- or downregulation of PPIs in ovarian cancer were estimated through an entropy formulation utilizing combined expression levels of genes, and the interacting protein pairs with minimum uncertainty were identified. We identified 105 proteins with differential PPI patterns scattered in 11 modules, each indicating significantly affected biological pathways in ovarian cancer such as DNA repair, cell proliferation-related mechanisms, nucleoplasmic translocation of estrogen receptor, extracellular matrix degradation, and inflammation response. In conclusion, we suggest several PPIs as biomarker candidates for ovarian cancer and discuss their future biological implications as potential molecular targets for pharmaceutical development as well. In addition, network entropy analysis is a concept that deserves greater research attention for diagnostic innovation in oncology and tumor pathogenesis.
authors.affiliation: 1 Department of Bioengineering, Marmara University , Istanbul, Turkey .|2 Department of Biomedical Sciences and Biotechnology, University of Udine , Udine, Italy .
authors.forename: Dilara
authors.initials: D
authors.lastname: Ayyildiz
authors.affiliation: 1 Department of Bioengineering, Marmara University , Istanbul, Turkey .|3 Department of Bioengineering, Adana Science and Technology University , Adana, Turkey .
authors.forename: Esra
authors.initials: E
authors.lastname: Gov
authors.affiliation: 4 Department of Biochemistry and Molecular Biology, Penn State College of Medicine , Hershey, Pennsylvania.
authors.forename: Raghu
authors.initials: R
authors.lastname: Sinha
authors.affiliation: 1 Department of Bioengineering, Marmara University , Istanbul, Turkey .
authors.forename: Kazim Yalcin
authors.initials: KY
authors.lastname: Arga
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D049109:Cell Proliferation; D004260:DNA Repair; D015536:Down-Regulation; D019277:Entropy; D005109:Extracellular Matrix; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism*; D025941:Protein Interaction Mapping / Q000379:methods; D060066:Protein Interaction Maps; D020543:Proteome; D011960:Receptors, Estrogen / Q000235:genetics / Q000378:metabolism; D059467:Transcriptome; D015854:Up-Regulation
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome; D011960:Receptors, Estrogen
keywords: cancer biology; differential interactome; entropy minimization; ovarian cancer; protein–protein interaction