An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer / 浙江大学学报(英文版)(B辑:生物医学和生物技术)
Journal of Zhejiang University. Science. B
;
(12): 227-231, 2005.
Article
in English
| WPRIM
| ID: wpr-316347
ABSTRACT
<p><b>OBJECTIVE</b>To find new potential biomarkers and establish the patterns for the detection of ovarian cancer.</p><p><b>METHODS</b>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.</p><p><b>RESULTS</b>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%.</p><p><b>CONCLUSIONS</b>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.</p>
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Ovarian Neoplasms
/
Mass Spectrometry
/
Blood
/
Peptide Mapping
/
Biomarkers, Tumor
/
Predictive Value of Tests
/
Reproducibility of Results
/
Sensitivity and Specificity
/
Computational Biology
/
Proteomics
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Adolescent
/
Adult
/
Aged
/
Female
/
Humans
Language:
English
Journal:
Journal of Zhejiang University. Science. B
Year:
2005
Type:
Article
Similar
MEDLINE
...
LILACS
LIS