Urinary proteome of steroid-sensitive and steroid-resistant idiopathic nephrotic syndrome of childhood

被引:44
作者
Woroniecki, Robert P.
Orlova, Tatyana N.
Mendelev, Natasha
Shatat, Ibrahim F.
Hailpern, Susan M.
Kaskel, Frederick J.
机构
[1] Albert Einstein Coll Med, Dept Pediat, Sect Pediat Nephrol, Bronx, NY 10467 USA
[2] Albert Einstein Coll Med, Dept Epidemiol & Populat Hlth, Bronx, NY 10467 USA
[3] New York Coll Med, Renal Inst, Valhalla, NY USA
关键词
nephrotic syndrome; childhood nephrosis; urinary biomarkers; SELDI; proteome; steroid sensitivity;
D O I
10.1159/000093814
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
The response to steroid therapy is used to characterize the idiopathic nephrotic syndrome (INS) of childhood as either steroid-sensitive (SSNS) or steroid-resistant (SRNS), a classification with a better prognostic capability than renal biopsy. The majority (similar to 80%) of INS is due to minimal change disease but the percentage of focal and segmental glomerulosclerosis is increasing. We applied a new technological platform to examine the urine proteome to determine if different urinary protein excretion profiles could differentiate patients with SSNS from those with SRNS. Twenty-five patients with INS and 17 control patients were studied. Mid-stream urines were analyzed using surface enhanced laser desorption and ionization mass spectrometry (SELDI-MS). Data were analyzed using multiple bioinformatic techniques. Patient classification was performed using Biomarker Pattern Software (TM) and a generalized form of Adaboost and predictive models were generated using a supervised algorithm with cross-validation. Urinary proteomic data distinguished INS patients from control patients, irrespective of steroid response, with a sensitivity of 92.3%, specificity of 93.7%, positive predictive value of 96% and a negative predictive value of 88.2%. Classification of patients as SSNS or SRNS was 100%. A protein of mass 4,144 daltons was identified as the single most important classifier in distinguishing SSNS from SRNS. SELDI-MS combined with bioinformatics can identify different proteomic patterns in INS. Characterization of the proteins of interest identified by this proteomic approach with prospective clinical validation may yield a valuable clinical tool for the non-invasive prediction of treatment response and prognosis. Copyright (c) 2006 S. Karger AG, Basel.
引用
收藏
页码:258 / 267
页数:10
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