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Discovery of serum biomarkers of ovarian cancer using complementary proteomic profiling strategies

Authors :
John F, Timms
Elif, Arslan-Low
Musarat, Kabir
Jenny, Worthington
Stephane, Camuzeaux
John, Sinclair
Joanna, Szaub
Babak, Afrough
Vladimir N, Podust
Evangelia-Ourania, Fourkala
Myriam, Cubizolles
Florian, Kronenberg
Eric T, Fung
Aleksandra, Gentry-Maharaj
Usha, Menon
Ian, Jacobs
Source :
Proteomics. Clinical Applications
Publication Year :
2014

Abstract

Purpose Ovarian cancer is a devastating disease and biomarkers for its early diagnosis are urgently required. Serum may be a valuable source of biomarkers that may be revealed by proteomic profiling. Herein, complementary serum protein profiling strategies were employed for discovery of biomarkers that could discriminate cases of malignant and benign ovarian cancer. Experimental design Identically collected and processed serum samples from 22 cases of invasive epithelial ovarian cancer, 45 benign ovarian neoplasms, and 64 healthy volunteers were subjected to immunodepletion and protein equalization coupled to 2D‐DIGE/MS and multidimensional fractionation coupled to SELDI‐TOF profiling with MS/MS for protein identification. Selected candidates were verified by ELISA in samples from malignant (n = 70) and benign (n = 89) cases and combined marker panels tested against serum CA125. Results Both profiling platforms were complementary in identifying biomarker candidates, four of which (A1AT, SLPI, APOA4, VDBP) significantly discriminated malignant from benign cases. However, no combination of markers was as good as CA125 for diagnostic accuracy. SLPI was further tested as an early marker using prediagnosis serum samples. While it rose in cases toward diagnosis, it did not discriminate prediagnosis cases from controls. Conclusions and clinical relevance The candidate biomarkers warrant further validation in independent sample sets.

Details

ISSN :
18628354
Volume :
8
Issue :
11-12
Database :
OpenAIRE
Journal :
Proteomics. Clinical applications
Accession number :
edsair.pmid..........c49f149fe58df31770270e80b0a219f3