Dataset Information

Expression Profiling and Clinical Significance of Plasma MicroRNAs in Diabetic Nephropathy.

ABSTRACT:

Aims

MicroRNAs (miRNAs) stably and abundantly exist in body fluids and have been considered as novel and noninvasive biomarkers for several diseases. The present study is aimed at investigating the expression profiling and clinical significance of plasma miRNAs in the pathogenesis and progression of diabetic nephropathy (DN).

Methods

Plasma samples were obtained from 66 DN patients (36 had microalbuminuria and 30 had macroalbuminuria), 36 diabetic patients with normoalbuminuria, and 40 healthy controls. The plasma miRNA profiles were obtained by miRNA low-density array chip and validated by quantitative real-time polymerase chain reaction. The correlations between the differential expression of plasma miRNAs and clinicopathological parameters were explored.

Results

miR-150-5p, miR-155-5p, miR-30e, miR-320e, and miR-3196 were found to be differentially expressed in plasma samples among these three groups: diabetic patients with microalbuminuria, diabetic patients with normoalbuminuria, and healthy controls (P < 0.05). The expression levels of miR-150-5p and miR-155-5p in patients with macroalbuminuria were 2.3-fold (P = 0.001) and 1.5-fold (P = 0.033) higher than patients with microalbuminuria, respectively. However, the expression levels of miR-30e, miR-3196, miR-320, and let-7a-5p were not significantly different between these two groups (P > 0.05). Furthermore, plasma miR-150-5p (P = 0.016, r = -0.460) and miR-155-5p (P = 0.014, r = -0.467) were negatively correlated with the albuminuria excretion rate, while plasma miR-150-5p (P = 0.01, r = 0.318) and miR-155-5p (P = 0.030, r = 0.271) were positively correlated with the estimated glomerular filtration rate.

Conclusion

miR-150-5p, miR-155-5p, miR-30e, miR-320e, and miR-3196 are potentially new diagnostic biomarkers for early DN. miR-150-5p and miR-155-5p may be involved in the pathogenesis and progression of DN. Further research is required to verify these findings and clarify the specific molecular mechanisms.

SUBMITTER: Wang J

PROVIDER: S-EPMC6536987 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDespite huge efforts, the underlying molecular mechanisms of diabetic nephropathy (DN) are yet elusive, and holistic views have rarely been generated. Considering the complexity of DN pathogenesis, the integration of datasets from different molecular types to construct a multilayer map of DN can provide a comprehensive insight toward the disease mechanisms and also can generate new knowledge. Here, we have re-analyzed two mRNA microarray datasets related to glomerular and tubulointerstitial compartments of human diabetic kidneys.Materials and methodsThe quality of the datasets was confirmed by unsupervised hierarchical clustering and principal component analysis. For each dataset, differentially expressed (DE) genes were identified, and transcription factors (TFs) regulating these genes and kinases phosphorylating the TFs were enriched. Furthermore, microRNAs (miRNAs) targeting the DE genes, TFs, and kinases were detected. Based on the harvested genes for glomeruli and tubulointerstitium, key signaling pathways and biological processes involved in diseases pathogenesis were recognized. In addition, the interaction of different elements in each kidney compartment was depicted in multilayer networks, and topology analysis was performed to identify key nodes. Central miRNAs whose target genes were most likely to be related to DN were selected, and their expressions were quantitatively measured in a streptozotocin-induced DN mouse model.ResultsAmong the examined miRNAs, miR-208a-3p and miR-496a-3p are, for the first time, found to be significantly overexpressed in the cortex of diabetic kidneys compared to controls.ConclusionWe predict that miR-208 is involved in oxygen metabolism and regulation of cellular energy balance. Furthermore, miR-496 potentially regulates protein metabolism and ion transport. However, their exact functions remain to be investigated in future studies. Taken together, starting from transcriptomics data, we have generated multilayer interaction networks and introduced novel players in DN.