Background: Protein and antibody arrays have emerged as a promising technology to study protein expression and protein function in a high-throughput manner. These arrays also represent a new opportunity to profile protein expression levels in cancer patients' samples and to identify useful biosignatures for clinical diagnosis, disease classification, prediction, drug development and patient care. We applied antibody arrays to discover a panel of proteins which may serve as biomarkers to distinguish between patients with ovarian cancer and normal controls.
Methodology/principal Findings: Using a case-control study design of 34 ovarian cancer patients and 53 age-matched healthy controls, we profiled the expression levels of 174 proteins using antibody array technology and determined the CA125 level using ELISA. The expression levels of those proteins were analyzed using 3 discriminant methods, including artificial neural network, classification tree and split-point score analysis. A panel of 5 serum protein markers (MSP-alpha, TIMP-4, PDGF-R alpha, and OPG and CA125) was identified, which could effectively detect ovarian cancer with high specificity (95%) and high sensitivity (100%), with AUC =0.98, while CA125 alone had an AUC of 0.87.
Conclusions/significance: Our pilot study has shown the promising set of 5 serum markers for ovarian cancer detection.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3792870 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0076795 | PLOS |
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