Identification biomarkers of eutopic endometrium in endometriosis using artificial neural networks and protein fingerprinting.
Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein chip array technology was used to detect biomarkers of eutopic endometrium in endometriosis patients. Five potential biomarkers (6,898 m/z, 5,891 m/z, 5,385 m/z, 6,448 m/z, and 5,425 m/z) were found.
Wang
Liang
L
The 2nd Affiliated Hospital, Department of Gynecology, and Ministry of Education Key Laboratory of Cancer Prevention and Intervention, Zhejiang University School of Medicine, Hangzhou, People's Republic of China.
Zheng
Wei
W
Ding
Xiao-yan
XY
Yu
Jie-kai
JK
Jiang
Wen-zhi
WZ
Zhang
Su-zhan
SZ
D000328:Adult; D015415:Biomarkers / Q000032:analysis* / Q000378:metabolism; D016022:Case-Control Studies; D042241:Early Diagnosis; D004715:Endometriosis / Q000175:diagnosis / Q000378:metabolism* / Q000473:pathology; D004717:Endometrium / Q000737:chemistry / Q000378:metabolism* / Q000473:pathology; D005260:Female; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D010449:Peptide Mapping / Q000379:methods*; D040081:Protein Array Analysis; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D014591:Uterine Diseases / Q000175:diagnosis / Q000378:metabolism* / Q000473:pathology; D055815:Young Adult
D015415:Biomarkers