Evaluation of Urinary Autophagy Transcripts Expression in Diabetic Kidney Disease
Matboli, Marwa; Azazy, Ahmed E.M.; Adel, Seham; Bekhet, Miram M.; Eissa, Sanaa;
Abstract
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.
Other data
Title | Evaluation of Urinary Autophagy Transcripts Expression in Diabetic Kidney Disease | Authors | Matboli, Marwa ; Azazy, Ahmed E.M.; Adel, Seham; Bekhet, Miram M.; Eissa, Sanaa | Keywords | Autophagy | Bioinformatics | Diabetic kidney disease | RNA | Urinary biomarkers | Issue Date | Oct-2017 | Publisher | ELSEVIER SCIENCE INC | Journal | Journal of Diabetes and its Complications | ISSN | 10568727 | DOI | 10.1016/j.jdiacomp.2017.06.009 | PubMed ID | 28760651 | Scopus ID | 2-s2.0-85026300683 | Web of science ID | WOS:000411917200003 |
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