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      A combined blood based gene expression and plasma protein abundance signature for diagnosis of epithelial ovarian cancer - a study of the OVCAD consortium

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          Abstract

          Background

          The immune system is a key player in fighting cancer. Thus, we sought to identify a molecular ‘immune response signature’ indicating the presence of epithelial ovarian cancer (EOC) and to combine this with a serum protein biomarker panel to increase the specificity and sensitivity for earlier detection of EOC.

          Methods

          Comparing the expression of 32,000 genes in a leukocytes fraction from 44 EOC patients and 19 controls, three uncorrelated shrunken centroid models were selected, comprised of 7, 14, and 6 genes. A second selection step using RT-qPCR data and significance analysis of microarrays yielded 13 genes (AP2A1, B4GALT1, C1orf63, CCR2, CFP, DIS3, NEAT1, NOXA1, OSM, PAPOLG, PRIC285, ZNF419, and BC037918) which were finally used in 343 samples (90 healthy, six cystadenoma, eight low malignant potential tumor, 19 FIGO I/II, and 220 FIGO III/IV EOC patients). Using new 65 controls and 224 EOC patients (thereof 14 FIGO I/II) the abundances of six plasma proteins (MIF, prolactin, CA125, leptin, osteopondin, and IGF2) was determined and used in combination with the expression values from the 13 genes for diagnosis of EOC.

          Results

          Combined diagnostic models using either each five gene expression and plasma protein abundance values or 13 gene expression and six plasma protein abundance values can discriminate controls from patients with EOC with Receiver Operator Characteristics Area Under the Curve values of 0.998 and bootstrap .632+ validated classification errors of 3.1% and 2.8%, respectively. The sensitivities were 97.8% and 95.6%, respectively, at a set specificity of 99.6%.

          Conclusions

          The combination of gene expression and plasma protein based blood derived biomarkers in one diagnostic model increases the sensitivity and the specificity significantly. Such a diagnostic test may allow earlier diagnosis of epithelial ovarian cancer.

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          Most cited references13

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Carcinoma of the ovary. FIGO 26th Annual Report on the Results of Treatment in Gynecological Cancer.

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              L1-regularization path algorithm for generalized linear models

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                Author and article information

                Journal
                BMC Cancer
                BMC Cancer
                BMC Cancer
                BioMed Central
                1471-2407
                2013
                3 April 2013
                : 13
                : 178
                Affiliations
                [1 ]Department of Obstetrics and Gynecology, Molecular Oncology Group, Medical University of Vienna, European Union, Vienna, Austria
                [2 ]Ludwig Boltzmann Cluster “Translational Oncology”, General Hospital Vienna, European Union, Waehringer Guertel 18-20, Room-No.: 5.Q9.27, Vienna, A-1090, Austria
                [3 ]Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, European Union, Vienna, Austria
                [4 ]Department of Gynecology, Campus Virchow Klinikum, Charite Medical University, European Union, Berlin, Germany
                [5 ]Department of Obstetrics and Gynecology, Division of Gynecological Oncology, University Hospitals Leuven, Katholieke Universiteit Leuven, European Union, Leuven, Belgium
                [6 ]Division of Gynaecological Oncology, Department of Obstetrics and Gynaecology, MUMC+, GROW – School for Oncology and Developmental Biology, European Union, Maastricht, The Netherlands
                [7 ]Department of Gynecology and Gynecologic Oncology, University Medical Center Hamburg-Eppendorf, European Union, Hamburg, Germany
                [8 ]Department of Gynecology and Obstetrics, Innsbruck Medical University, European Union, Innsbruck, Austria
                Article
                1471-2407-13-178
                10.1186/1471-2407-13-178
                3639192
                23551967
                61cd5c22-1ffd-4267-a69c-4ebaa1bc2637
                Copyright ©2013 Pils et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 July 2012
                : 18 March 2013
                Categories
                Research Article

                Oncology & Radiotherapy
                peripheral blood leukocytes,biomarker,transcriptomics,plasma protein,diagnosis,ovarian cancer

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