Research ArticleKidney Disease

Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker

Science Translational Medicine  02 Dec 2015:
Vol. 7, Issue 316, pp. 316ra193
DOI: 10.1126/scitranslmed.aac7071

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Urine marker to the rescue

Chronic kidney disease is a common medical problem worldwide, but it is difficult to predict which patients are more likely to progress to end-stage disease and need aggressive management. Ju et al. have now drawn on four independent cohorts totaling hundreds of patients from around the world to identify the expression of epidermal growth factor (EGF) in the kidneys as a marker of kidney disease progression. Moreover, the authors demonstrated that the amount of EGF in the urine is just as useful, providing a biomarker that can be easily tracked over time without requiring invasive biopsies.

Abstract

Chronic kidney disease (CKD) affects 8 to 16% people worldwide, with an increasing incidence and prevalence of end-stage kidney disease (ESKD). The effective management of CKD is confounded by the inability to identify patients at high risk of progression while in early stages of CKD. To address this challenge, a renal biopsy transcriptome-driven approach was applied to develop noninvasive prognostic biomarkers for CKD progression. Expression of intrarenal transcripts was correlated with the baseline estimated glomerular filtration rate (eGFR) in 261 patients. Proteins encoded by eGFR-associated transcripts were tested in urine for association with renal tissue injury and baseline eGFR. The ability to predict CKD progression, defined as the composite of ESKD or 40% reduction of baseline eGFR, was then determined in three independent CKD cohorts. A panel of intrarenal transcripts, including epidermal growth factor (EGF), a tubule-specific protein critical for cell differentiation and regeneration, predicted eGFR. The amount of EGF protein in urine (uEGF) showed significant correlation (P < 0.001) with intrarenal EGF mRNA, interstitial fibrosis/tubular atrophy, eGFR, and rate of eGFR loss. Prediction of the composite renal end point by age, gender, eGFR, and albuminuria was significantly (P < 0.001) improved by addition of uEGF, with an increase of the C-statistic from 0.75 to 0.87. Outcome predictions were replicated in two independent CKD cohorts. Our approach identified uEGF as an independent risk predictor of CKD progression. Addition of uEGF to standard clinical parameters improved the prediction of disease events in diverse CKD populations with a wide spectrum of causes and stages.

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