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Updated: Aug 19, 2026
Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer
Jie-kai Yu1, Shu Zheng, Yong Tang
1Cancer Institute, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.
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.
