Search of serum protein biomarkers for systematic lupus eryt.hematosus using protein chip tech- nology
10.3760/cma.j.issn.0412-4030.2009.08.013
- VernacularTitle:蛋白质芯片技术筛选SLE患者血清蛋白标记物的研究
- Author:
Yan LAN
;
Xiusheng TANG
;
Jie WU
;
Jun QIN
;
Jimin CHEN
- Publication Type:Journal Article
- Keywords:
Lupus erythematosus,systemic;
Protein array analysis;
Proteomics;
Spectrometry,mass,matrix-assisted laser desorption-ionization
- From:
Chinese Journal of Dermatology
2009;42(8):560-562
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the changes of serum protein spectrum in patients with systematic lupus erythematosus (SLE) in order to screen specific protein markers. Methods Serum samples from 72 patients with SLE and 85 age- and sex-matched controls were assessed using surface-enhanced laser desorp-tion/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with weak cation exchange (CM10) pro-rein chip. Forty samples from the patients and 50 control samples were randomly selected to serve as a pre-liminary training set; significantly different protein peaks were automatically chosen for the system training and development of a decision classification tree model. The validity of the model was then challenged with a blind test set (including another 32 samples from patients and 35 from human controls). Results A total of 73 effective protein peaks were detected within the mass/charge ratio (m/z) interval 2000 - 50000, among which, 15 protein peaks significantly differed between patients with SLE and controls (P < 0.01). Three pro-tein peaks with an m/z value of 4001, 6305 and 7356 were automatically chosen as a biomarker pattern in the training set that discriminated patients with SLE from controls with a sensitivity of 90.0% (36/40), speci-ficity of 92.0% (46/50) and accuracy rate of 91.1% (82/90). When the SELDI marker pattern was tested with the blinded test set, it yielded a sensitivity of 87.5% (28/32), specificity of 91.4% (32/35) and accuracy rate of 89.6% (60/67). Conclusions SELDI-TOF-MS protein chip could be used to screen serum protein for SLE, and the decision classification tree model with these biomarkers may favor the diagnosis of SLE.
