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

Wenjun Ju, Viji Nair, Shahaan Smith, Li Zhu, Kerby Shedden, Peter X.K. Song, Laura H. Mariani, Felix H. Eichinger, Celine C. Berthier, Ann Randolph, Jennifer Yi Chun Lai, Yan Zhou, Jennifer J. Hawkins, Markus Bitzer, Matthew G. Sampson, Martina Their, Corinne Solier, Gonzalo C. Duran-Pacheco, Guillemette Duchateau-Nguyen, Laurent EssiouxBrigitte Schott, Ivan Formentini, Maria C. Magnone, Maria Bobadilla, Clemens D. Cohen, Serena M. Bagnasco, Laura Barisoni, Jicheng Lv, Hong Zhang, Hai Yan Wang, Frank C. Brosius, Crystal A. Gadegbeku, Matthias Kretzler

Research output: Contribution to journalArticle

132 Scopus citations

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 transcriptomedriven 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.

Original languageEnglish (US)
Article number7071
JournalScience Translational Medicine
Volume7
Issue number316
DOIs
StatePublished - Dec 2 2015

ASJC Scopus subject areas

  • Medicine(all)

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    Ju, W., Nair, V., Smith, S., Zhu, L., Shedden, K., Song, P. X. K., Mariani, L. H., Eichinger, F. H., Berthier, C. C., Randolph, A., Lai, J. Y. C., Zhou, Y., Hawkins, J. J., Bitzer, M., Sampson, M. G., Their, M., Solier, C., Duran-Pacheco, G. C., Duchateau-Nguyen, G., ... Kretzler, M. (2015). Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. Science Translational Medicine, 7(316), [7071]. https://doi.org/10.1126/scitranslmed.aac7071