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Tissue transcriptome-driven identification of epidermal growth factor as a
chronic kidney disease biomarker
#MMPMID26631632
Ju W
; Nair V
; Smith S
; Zhu L
; Shedden K
; Song PXK
; Mariani LH
; Eichinger FH
; Berthier CC
; Randolph A
; Lai JY
; Zhou Y
; Hawkins JJ
; Bitzer M
; Sampson MG
; Thier M
; Solier C
; Duran-Pacheco GC
; Duchateau-Nguyen G
; Essioux L
; Schott B
; Formentini I
; Magnone MC
; Bobadilla M
; Cohen CD
; Bagnasco SM
; Barisoni L
; Lv J
; Zhang H
; Wang HY
; Brosius FC
; Gadegbeku CA
; Kretzler M
Sci Transl Med
2015[Dec]; 7
(316
): 316ra193
PMID26631632
show ga
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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