Identification of serum biomarkers for rheumatoid arthritis using protein fingerprint
- VernacularTitle:应用血清蛋白质指纹图谱筛选类风湿关节炎的血清标志物
- Author:
Wen-Bo LIU
;
Xing-Fu LI
;
Feng DING
;
Hua-Xiang LIU
;
- Publication Type:Journal Article
- Keywords:
Arthritis, rheumatoid;
Biological markers;
Proteomics;
Artificial neural network
- From:
Chinese Journal of Rheumatology
2000;0(06):-
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify serum biomarkers for rheumatoid arthritis(RA)by protein finger- print pattern. Methods One hundred and forty-one serum samples of 90 RA patients, 20 systemic lupus ery- thematosus(SLE)patients, and 31 healthy individuals were randomly divided into training set(n=93, 60 RA patients, 13 SLE patients and 20 healthy individuals)and test set(n=48, 30 RA patients, 7 SLE patients and 11 healthy individuals). They were detected by surface enhanced laser desorption/ionization-time of flight- mass spectrometry(SELDI-TOF-MS). The protein fingerprint pattern obtained from SELDI-TOF was trained by a multi-layer artificial neural network(ANN)to establish a diagnostic model. Results The detective mod- el obtained by ANN was used to detect the 48 unknown serum samples. The sensitivity and specificity for RA detection was 90% and 90.9% respectively. Conclusion In comparison with traditional methods, SELDI- TOF-MS could identify new serum biomarkers in RA. Combined with ANN, it provides high sensitivity and specificity for RA diagnosis.
