Literature DB >> 15754417

An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer.

Jie-kai Yu1, Shu Zheng, Yong Tang, Li Li.   

Abstract

OBJECTIVE: To find new potential biomarkers and establish the patterns for the detection of ovarian cancer.
METHODS: Sixty one serum samples including 32 ovarian cancer patients and 29 healthy people were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The protein fingerprint data were analyzed by bioinformatics tools. Ten folds cross-validation support vector machine (SVM) was used to establish the diagnostic pattern.
RESULTS: Five potential biomarkers were found (2085 Da, 5881 Da, 7564 Da, 9422 Da, 6044 Da), combined with which the diagnostic pattern separated the ovarian cancer from the healthy samples with a sensitivity of 96.7%, a specificity of 96.7% and a positive predictive value of 96.7%.
CONCLUSIONS: The combination of SELDI with bioinformatics tools could find new biomarkers and establish patterns with high sensitivity and specificity for the detection of ovarian cancer.

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Year:  2005        PMID: 15754417      PMCID: PMC1389728          DOI: 10.1631/jzus.2005.B0227

Source DB:  PubMed          Journal:  J Zhejiang Univ Sci B        ISSN: 1673-1581            Impact factor:   3.066


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4.  Use of proteomic patterns in serum to identify ovarian cancer.

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Authors:  Emanuel F Petricoin; Lance A Liotta
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Review 10.  Detection of tumor markers with ProteinChip technology.

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