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
Dans Anglais
| 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>
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Tumeurs de l'ovaire
/
Spectrométrie de masse
/
Sang
/
Cartographie peptidique
/
Marqueurs biologiques tumoraux
/
Valeur prédictive des tests
/
Reproductibilité des résultats
/
Sensibilité et spécificité
/
Biologie informatique
/
Protéomique
Type d'étude:
Etude diagnostique
/
Étude pronostique
Limites du sujet:
Adolescent
/
Adulte
/
Adulte très âgé
/
Femelle
/
Humains
langue:
Anglais
Texte intégral:
Journal of Zhejiang University. Science. B
Année:
2005
Type:
Article
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