Background: We identified and validated novel urinary autophagy markers in diabetic kidney disease (DKD) based on bioinformatics analysis and clinical validation.
Patients & methods: We retrieved three novel autophagy genes related to DKD from public microarray databases, namely; microtubule-associated protein light chain (MAP1LC3A), WD Repeat Domain, Phosphoinositide Interacting 2 (WIPI2), and RB1-Inducible Coiled-Coil 1 (RB1CC1). Secondly we assessed the expression of the chosen autophagy transcript in urine sediment of 86 patients with DKD and 74 (age and sex matched) controls by reverse transcription quantitative real-time PCR.
Results: The urinary expression levels of MAP1LC3A, WIPI, RB1CC1 were significantly lower in DKD than control group (P<0.001).The receiver-operating characteristic curve (ROC) analyses that each urinary autophagy transcript showed high sensitivity and specificity for distinguishing DKD from control (MAP1LC3A, 81.4% and 81.1%; WIPI, 74.4% and 67.6%, and RB1CC1, 81.4%,70.3%, respectively). Notably, a negative correlation was found between these autophagy markers, serum creatinine and urinary albumin creatinine ratio. The sensitivity and specificity of this urinary autophagy based panel reached 90.6% and 60% in diagnosis of DKD.
Conclusion: We identified and validated a novel diagnostic urinary autophagy based panel with high sensitivity and moderate specificity representing a vital player in the pathogenesis of DKD.
Keywords: Autophagy; Bioinformatics; Diabetic kidney disease; RNA; Urinary biomarkers.
Copyright © 2017 Elsevier Inc. All rights reserved.