Dataset Information

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics.

ABSTRACT:

Introduction

Nephrotic syndrome (NS) is a characterized by massive proteinuria, edema, hypoalbuminemia, and dyslipidemia. Glucocorticoids (GCs), the primary therapy for >60 years, are ineffective in approximately 50% of adults and approximately 20% of children. Unfortunately, there are no validated biomarkers able to predict steroid-resistant NS (SRNS) or to define the pathways regulating SRNS.

Methods

We performed proteomic analyses on paired pediatric NS patient plasma samples obtained both at disease presentation before glucocorticoid initiation and after approximately 7 weeks of GC therapy to identify candidate biomarkers able to either predict steroid resistance before treatment or define critical molecular pathways/targets regulating steroid resistance.

Results

Proteomic analyses of 15 paired NS patient samples identified 215 prevalent proteins, including 13 candidate biomarkers that predicted SRNS before GC treatment, and 66 candidate biomarkers that mechanistically differentiated steroid-sensitive NS (SSNS) from SRNS. Ingenuity Pathway Analyses and protein networking pathways approaches further identified proteins and pathways associated with SRNS. Validation using 37 NS patient samples (24 SSNS/13 SRNS) confirmed vitamin D binding protein (VDB) and APOL1 as strong predictive candidate biomarkers for SRNS, and VDB, hemopexin (HPX), adiponectin (ADIPOQ), sex hormone-binding globulin (SHBG), and APOL1 as strong candidate biomarkers to mechanistically distinguish SRNS from SSNS. Logistic regression analysis identified a candidate biomarker panel (VDB, ADIPOQ, and matrix metalloproteinase 2 [MMP-2]) with significant ability to predict SRNS at disease presentation (P = 0.003; area under the receiver operating characteristic curve = 0.78).

Conclusion

Plasma proteomic analyses and immunoblotting of serial samples in childhood NS identified a candidate biomarker panel able to predict SRNS at disease presentation, as well as candidate molecular targets/pathways associated with clinical steroid resistance.

SUBMITTER: Agrawal S

PROVIDER: S-EPMC6943770 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics.

Agrawal Shipra S Merchant Michael L ML Kino Jiro J Li Ming M Wilkey Daniel W DW Gaweda Adam E AE Brier Michael E ME Chanley Melinda A MA Gooding Jessica R JR Sumner Susan J SJ Klein Jon B JB Smoyer William E WE

Kidney international reports 20190919 1

<h4>Introduction</h4>Nephrotic syndrome (NS) is a characterized by massive proteinuria, edema, hypoalbuminemia, and dyslipidemia. Glucocorticoids (GCs), the primary therapy for >60 years, are ineffective in approximately 50% of adults and approximately 20% of children. Unfortunately, there are no validated biomarkers able to predict steroid-resistant NS (SRNS) or to define the pathways regulating SRNS.<h4>Methods</h4>We performed proteomic analyses on paired pediatric NS patient plasma samples o ...[more]

PMID: 31922062

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Project description:BackgroundIn the absence of effective measures to predict steroid responsiveness, patients with nonhereditary steroid-resistant nephrotic syndrome (SRNS) have a significantly increased risk of progression to end-stage renal disease. In view of the poor outcomes of SRNS, it is urgent to identify the steroid responsiveness of idiopathic nephrotic syndrome (INS) early.MethodsTo build a prediction model for SRNS, we collected 91 subjects; 57 of them had steroid-sensitive nephrotic syndrome, and the others had SRNS. For each subject, 87 clinical variables were measured. In general, only a small part of these variables is informative to SRNS. Thus, we proposed a new variable selection framework including a penalized regression approach (named MLR+TLP) to select variables having a linear effect on the SRNS and a nonparametric screening method (MAC) to select variables having a nonlinear marginal (joint) effect on the SRNS. Thereafter, considering the correlation between selected clinical variables, we used a stepwise method to build our final model for predicting SRNS. In addition, a statistical testing procedure is proposed to test the overfitting of the proposed model.ResultsTwenty-six clinical variables were selected to be informative to SRNS, and an SVM model was built to predict SRNS with a leave-one-out cross-validation (LOO-CV) accuracy of 95.2% (overfitting p value<0.005). To make the model more useful, we incorporate prior medical information into the model and consider the correlation between selected variables. Then, a reduced SVM model including only eight clinical variables (erythrocyte sedimentation rate, urine occult blood, percentage of neutrophils, immunoglobulin A, cholesterol, vinculin autoantibody, aspartate aminotransferase, and prolonged prothrombin time) was built to have a LOO-CV accuracy of 92.8% (overfitting p value<0.005). The validation cohort showed that the reduced model obtained an accuracy of 94.0% (overfitting p value<0.005), with a sensitivity of 90.0% and a specificity of 96.7%. Notably, vinculin autoantibody is the only podocyte autoantibody included in this model. It is linearly related to steroid responsiveness. Finally, our model is freely available as a user-friendly web tool at https://datalinkx.shinyapps.io/srns/.ConclusionThe SRNS prediction model constructed in this study comprehensively and objectively evaluates the internal conditions and disease status of INS patients and will provide scientific guidance for selecting treatment methods for children with nonhereditary SRNS.

Project description:ObjectivePrimary immune thrombocytopenia (ITP) is the most common acquired autoimmune bleeding disorder among children. While glucocorticoids are the primary first-line treatment for ITP treatment, they prove ineffective in certain patients. The challenge of identifying biomarkers capable of early prediction regarding the response to glucocorticoid therapy in ITP persists. This study aimed to identify ideal biomarkers for predicting glucocorticoid efficacy in patients with ITP using plasma proteomics.MethodsA four-dimensional data-independent acquisition approach was performed to determine the differentially expressed proteins in plasma samples collected from glucocorticoid-sensitive (GCS) (n=18) and glucocorticoid-resistant (GCR) (n=17) children with ITP treated with prednisone. The significantly differentially expressed proteins were selected for enzyme-linked immunosorbent assay validation in a cohort conprising 65 samples(30 healthy controls, 18 GCS and 17 GCR children with ITP). Receiver operating characteristics curves, calibration curves, and clinical decision curve analysis were used to determine the diagnostic efficacy of this method.Results47 differentially expressed proteins (36 up-regulated and 11 down-regulated) were identified in the GCR group compared with the GCS group. The significantly differentially expressed proteins myosin heavy chain 9 (MYH9) and fetuin B (FETUB) were selected for enzyme-linked immunosorbent assay validation. The validation results were consistent with the proteomics analyses. Compared with the GCS group, the GCR group exhibited a significantly reduced the plasma concentration of MYH9 and elevated the plasma concentration of FETUB. Furthermore, the receiver operating characteristics curves, calibration curves, and clinical decision curve analysis demonstrated good diagnostic efficacy of these validated biomarkers.ConclusionThis study contributes to the establishment of objective biological indicators for precision therapy in children with ITP. More importantly, the proteins MYH9 and FETUB hold potential as a foundation for making informed decisions regarding alternative treatments for drugresistant patients, thereby preventing treatment delays.

Dataset Information

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics.

Introduction

Methods

Results

Conclusion

Publications

Predicting and Defining Steroid Resistance in Pediatric Nephrotic Syndrome Using Plasma Proteomics.

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