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Urinary Proteome of Steroid-Sensitive and Steroid-Resistant Idiopathic Nephrotic Syndrome of Childhood

Woroniecki R.P.a · Orlova T.N.c · Mendelev N.c · Shatat I.F.a · Hailpern S.M.b · Kaskel F.J.a · Goligorsky M.S.c · O’Riordan E.c
aSection of Pediatric Nephrology, Department of Pediatrics, and bDepartment of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, N.Y.; cRenal Institute, New York Medical College, Valhalla, N.Y., USA Am J Nephrol 2006;26:258–267 (DOI:10.1159/000093814)

Abstract

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 (∼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 SoftwareTM 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.

 

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