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Gene expression-based analysis identified NTNG1 and HGF as biomarkers for diabetic kidney disease.

ABSTRACT: Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease. Because the molecular mechanisms of DKD are not fully understood, exploration of hub genes and the mechanisms underlying this disease are essential for elucidating the pathogenesis and progression of DKD. Accordingly, in this study, we performed an analysis of gene expression in DKD. The differentially expressed genes (DEGs) included 39 upregulated genes and 113 downregulated genes in the GSE30528 dataset and 127 upregulated genes and 18 downregulated genes in the GSE30529 dataset. Additionally, functional analyses were performed to determine the roles of DEGs using glomeruli samples from patients with DKD and healthy controls from the GSE30528 dataset and using tubule samples from patients with DKD and healthy controls from the GSE30529 dataset. These DEGs were enriched in pathways such as the Wnt signaling pathway, metabolic pathways, and the mammalian target of rapamycin signaling pathway in the GSE30528 dataset and the longevity regulating pathway and Ras signaling pathway in the GSE30529 dataset. Moreover, a protein-protein interaction network was constructed using the identified DEGs, and hub gene analysis was performed. Furthermore, correlation analyses between key genes and pathological characteristics of DKD indicated that CCR4, NTNG1, HGF and ISL1 are related to DKD, and NTNG1 and HGF may server as diagnostic biomarkers in DKD using the receiver-operator characteristic (ROC) curve. Collectively, our findings established 2 reliable biomarkers for DKD.

SUBMITTER: Tang YL

PROVIDER: S-EPMC6946191 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Gene expression-based analysis identified NTNG1 and HGF as biomarkers for diabetic kidney disease.

Tang Yun-Liang YL Dong Xiao-Yang XY Zeng Zhen-Guo ZG Feng Zhen Z

Medicine 20200101 1

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease. Because the molecular mechanisms of DKD are not fully understood, exploration of hub genes and the mechanisms underlying this disease are essential for elucidating the pathogenesis and progression of DKD. Accordingly, in this study, we performed an analysis of gene expression in DKD. The differentially expressed genes (DEGs) included 39 upregulated genes and 113 downregulated genes in the GSE30528 dataset and 127 upregu ...[more]

PMID: 31895808

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Project description:Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0291-6) contains supplementary material, which is available to authorized users.

Project description:BackgroundDiabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim) and whether they have potential as prognostic biomarkers in DKD (secondary aim).MethodsiBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes.DiscussioniBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D.Trial registrationClinicaltrials.gov ( NCT03716401 ).

Dataset Information

Gene expression-based analysis identified NTNG1 and HGF as biomarkers for diabetic kidney disease.

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Gene expression-based analysis identified NTNG1 and HGF as biomarkers for diabetic kidney disease.

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