An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer.
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.
Yu
Jie-kai
JK
Cancer Institute, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.
Zheng
Shu
S
Tang
Yong
Y
Li
Li
L
D000293:Adolescent; D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D019295:Computational Biology; D005260:Female; D006801:Humans; D007834:Lasers; D013058:Mass Spectrometry; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis*; D010449:Peptide Mapping; D011237:Predictive Value of Tests; D040901:Proteomics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor