An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer.
- Author:
Jie-kai YU
1
;
Shu ZHENG
;
Yong TANG
;
Li LI
Author Information
- Publication Type:Journal Article
- MeSH: Adolescent; Adult; Aged; Biomarkers, Tumor; blood; Computational Biology; Female; Humans; Lasers; Mass Spectrometry; Middle Aged; Ovarian Neoplasms; blood; diagnosis; Peptide Mapping; Predictive Value of Tests; Proteomics; Reproducibility of Results; Sensitivity and Specificity
- From:Journal of Zhejiang University. Science. B 2005;6(4):227-231
- CountryChina
- Language:English
-
Abstract:
OBJECTIVETo find new potential biomarkers and establish the patterns for the detection of ovarian cancer.
METHODSSixty 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.
RESULTSFive 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%.
CONCLUSIONSThe combination of SELDI with bioinformatics tools could find new biomarkers and establish patterns with high sensitivity and specificity for the detection of ovarian cancer.
