Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks.
OBJECTIVE
To use surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein chip array technology to detect proteomic patterns in the serum of women with endometriosis; build diagnostic models; and evaluate their clinical significance.


METHODS
Serum samples from women with endometriosis and healthy women were studied using SELDI-TOF-MS protein chip technology. For every matched pair, two-thirds of the samples were used to look for different patterns and one-third was used for cross-validation.


RESULTS
Five potential biomarkers were found and the diagnostic system distinguished endometriosis from validation samples with a sensitivity of 91.7% and a specificity of 90.0%.


CONCLUSIONS
This method shows great potential in identifying biomarkers to be used for endometriosis screening.
Wang
Liang
L
The 2nd Affiliated Hospital, Department of Gynecology, Zhejiang University School of Medicine, Hangzhou, China.
Zheng
Wei
W
Mu
Lin
L
Zhang
Su-Zhan
SZ
D000293:Adolescent; D000328:Adult; D015415:Biomarkers / Q000032:analysis; D014408:Biomarkers, Tumor / Q000032:analysis; D004715:Endometriosis / Q000097:blood / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D010449:Peptide Mapping; D040081:Protein Array Analysis / Q000379:methods; D040901:Proteomics; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D014591:Uterine Diseases / Q000097:blood / Q000175:diagnosis*
D015415:Biomarkers; D014408:Biomarkers, Tumor