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CLC number: R737.31

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Received: 2004-08-20

Revision Accepted: 2004-10-15

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Cited: 20

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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.4 P.227-231


An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer

Author(s):  YU Jie-kai, ZHENG Shu, TANG Yong, LI Li

Affiliation(s):  Cancer Institute, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China; more

Corresponding email(s):   zhengshu@mail.hz.zj.cn

Key Words:  Ovarian cancer, SVM, Diagnosis, SELDI-TOF, Proteomics

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YU Jie-kai, ZHENG Shu, TANG Yong, LI Li. An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer[J]. Journal of Zhejiang University Science B, 2005, 6(4): 227-231.

@article{title="An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer",
author="YU Jie-kai, ZHENG Shu, TANG Yong, LI Li",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer
%A YU Jie-kai
%A TANG Yong
%A LI Li
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 4
%P 227-231
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B0227

T1 - An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer
A1 - YU Jie-kai
A1 - ZHENG Shu
A1 - TANG Yong
A1 - LI Li
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 4
SP - 227
EP - 231
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B0227

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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