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

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.

Epidermal growth factor as a prognostic biomarker in chronic kidney diseases

“…Interestingly, EGF expression was also reduced at the renal transcriptional level in this rat model. These observations confirmed the results of Ju et al (7). The clinical utility of a biomarker for CKD depends on its ability to predict the risk of the development of end stage kidney disease.…”

“…Thus, it is reasonable that tubular proteins such as EGF, reflecting tubular cell damage, may be better predictive biomarkers than albuminuria. It makes sense that uEGF/Cr correlated with the EGF expression in renal biopsy specimen and eGFR at the time of renal biopsy, and that predicted the eGFR decline in the study by Ju et al (7). Furthermore, these observations were validated in other cohorts consisting of a variety of kidney diseases.…”

“…Among six genes [nicotinamide N-methyltransferase (NNMT), EGF, thymosin β 10 (TMSB10), tissue inhibitor of metalloproteinases 1 (TIMP1), tubulin α 1a (TUBA1A), and annexin A1 (ANXA1)] showing the best predictive ability, they selected EGF, because (I) EGF mRNA expression was correlated with eGFR in other two validation cohorts; (II) EGF expression was kidney-specific, and restricted to the thick ascending limb of Henle and distal tubules (8); and (III) EGF enhances renal tubule cell regeneration and repair and accelerates the recovery of renal function after injury (9,10). Of interest, they demonstrated that urine EGF protein-tocreatinine ratio (uEGF/Cr) correlated significantly with the intrarenal EGF mRNA expression in 34 C-PROBE and 85 Nephrotic Syndrome Study Network (NEPTUNE) patients (7). As altered urinary EGF excretion is concordant with altered intrarenal EGF expression, uEGF/Cr may in part reflect transcript of EGF from the injured tubules per se, and be illustrating the intrinsic renal pathological changes.…”

“…And the population of the cohort may be small. (7) demonstrated that urine epidermal growth factor (EGF) is a noninvasive prognostic biomarker for CKD progression using a renal biopsy transcriptome-driven approach from the European Renal cDNA Bank (ERCB). They examined the ability of the candidate gene transcript to predict patients' eGFR at time of biopsy.…”

Defining Glomerular Disease in Mechanistic Terms: Implementing an Integrative Biology Approach in Nephrology

“…A data-driven, unbiased approach implemented across several large-scale data domains recently identified a urinary marker that predicts progression of kidney disease in three independent cohorts, including two glomerular disease cohorts (30). The pipeline began with identifying renal biopsy mRNA transcripts that correlated with eGFR and also were differentially regulated compared with live kidney donor tissue.…”