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Open Access Research article

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

Dietmar Pils12*, Dan Tong1, Gudrun Hager1, Eva Obermayr1, Stefanie Aust1, Georg Heinze3, Maria Kohl3, Eva Schuster1, Andrea Wolf1, Jalid Sehouli4, Ioana Braicu4, Ignace Vergote5, Toon Van Gorp56, Sven Mahner7, Nicole Concin8, Paul Speiser1 and Robert Zeillinger12

Author 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

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BMC Cancer 2013, 13:178  doi:10.1186/1471-2407-13-178

Published: 3 April 2013

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.

Keywords:
Peripheral blood leukocytes; Biomarker; Transcriptomics; Plasma protein; Diagnosis; Ovarian cancer