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A serum protein signature with high diagnostic value in bacterial endocarditis:: Results from a study based on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry
被引:22
|作者:
Fenollar, Florence
Goncalves, Anthony
Esterni, Benjamin
Azza, Said
Habib, Gilbert
Borg, Jean-Paul
Raoult, Didier
机构:
[1] Univ Mediterranee, Fac Med, IFR 48, CNRS 6020,Unite Rickettsies, F-13385 Marseille 05, France
[2] Hop Enfants La Timone, Unite Fonct Rech Med, Marseille, France
[3] Hop Enfants La Timone, Unite Fonct Rech Med, Marseille, France
[4] Hop Enfants La Timone, INSERM UMR 599, Inst Paoli Calmettes, Marseille Canc Inst,Mol Pharmacol Dept, Marseille, France
[5] Hop Enfants La Timone, INSERM UMR 599, Inst Paoli Calmettes, Marseille Canc Inst,Biostat Dept, Marseille, France
[6] Hop Enfants La Timone, Serv Cardiol, Marseille, France
关键词:
D O I:
10.1086/508429
中图分类号:
R392 [医学免疫学];
Q939.91 [免疫学];
学科分类号:
100102 ;
摘要:
Background. Bacterial endocarditis is a serious disease. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) based on serum protein profiling is a powerful approach that can generate biomarkers with diagnostic value. Methods. To identify a protein signature associated with bacterial endocarditis, we retrospectively performed SELDI-TOF MS profiling of serum samples from 88 patients hospitalized because of clinical suspicion of endocarditis. The diagnosis was confirmed by conventional criteria for 34 patients (endocarditis positive) and was excluded for 54 patients (endocarditis negative). Serum samples were incubated with cation-exchange ProteinChip arrays. The protein profiles generated were subjected to biostatistical processing. Results. Fifty-nine samples (23 endocarditis positive and 36 endocarditis negative) were randomly selected for a learning set, with the 29 remaining samples (11 endocarditis positive and 18 endocarditis negative) serving as an independent testing (validation) set. Sixty-six protein peaks were differentially expressed between the endocarditis-positive and the endocarditis-negative patients. By combining partial least squares and logistic regression methods, we built a serum protein model that perfectly discriminated between endocarditis-positive and endocarditis-negative patients. Importantly, when this model was tested on the independent testing set, a correct prediction rate of nearly 90% was demonstrated. Overall, sensitivity, specificity, positive predictive value, and negative predictive value were 94%, 98%, 96%, and 96%, respectively. Conclusions. SELDI-TOF MS profiling revealed a serum signature with high diagnostic potential for endocarditis.
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页码:1356 / 1366
页数:11
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