Format

Send to

Choose Destination
Sci Transl Med. 2015 Dec 2;7(316):316ra193. doi: 10.1126/scitranslmed.aac7071.

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

Author information

1
Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
2
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
3
Renal Division, Department of Internal Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing 100034, China.
4
Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.
5
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
6
Arbor Research Collaborative for Health, Ann Arbor, MI 48104, USA.
7
Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA.
8
Roche Pharmaceutical Research and Early Development-Roche Innovation Center, 4070 Basel, Switzerland.
9
Division of Nephrology, Institute of Physiology, University of Zurich, CH-8006 Zürich, Switzerland.
10
Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
11
Department of Pathology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.
12
Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA.
13
Temple Clinical Research Institute, Temple University School of Medicine, Philadelphia, PA 19140, USA.
#
Contributed equally

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.

PMID:
26631632
PMCID:
PMC4861144
DOI:
10.1126/scitranslmed.aac7071
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center