دورية أكاديمية
Urinary Proteome of Steroid-Sensitive and Steroid-Resistant Idiopathic Nephrotic Syndrome of Childhood
| العنوان: | Urinary Proteome of Steroid-Sensitive and Steroid-Resistant Idiopathic Nephrotic Syndrome of Childhood |
|---|---|
| المؤلفون: | Woroniecki, Robert P., Orlova, Tatyana N., Mendelev, Natasha, Shatat, Ibrahim F., Hailpern, Susan M., Kaskel, Frederick J., Goligorsky, Michael S., O’Riordan, Edmond |
| المصدر: | American Journal of Nephrology ; volume 26, issue 3, page 258-267 ; ISSN 0250-8095 1421-9670 |
| بيانات النشر: | S. Karger AG |
| سنة النشر: | 2006 |
| مصطلحات موضوعية: | Nephrology |
| الوصف: | 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 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1159/000093814 |
| الإتاحة: | https://doi.org/10.1159/000093814Test https://www.karger.com/Article/Pdf/93814Test |
| حقوق: | https://www.karger.com/Services/SiteLicensesTest ; https://www.karger.com/Services/SiteLicensesTest |
| رقم الانضمام: | edsbas.6ECF1012 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1159/000093814 |
|---|