BACKGROUND: Urinary sediment messenger RNAs (mRNAs) have been shown as novel biomarkers of kidney disease. We aimed to identify targeted urinary mRNAs in diabetic nephropathy (DN) based on bioinformatics analysis and clinical validation. METHODS: Microarray studies of DN were searched in the GEO database and Nephroseq platform. Gene modules negatively correlated with estimated glomerular filtration rate (eGFR) were identified by informatics methods. Hub genes were screened within the selected modules. In validation cohorts, a quantitative polymerase chain reaction assay was used to compare the expression levels of candidate mRNAs. Patients with renal biopsy-confirmed DN were then followed up for a median time of 21 months. End-stage renal disease (ESRD) was defined as the primary endpoint. Multivariate Cox proportional hazards regression was developed to evaluate the prognostic values of candidate mRNAs. RESULTS: Bioinformatics analysis revealed four chemokines (CCL5, CXCL1, CXLC6 and CXCL12) as candidate mRNAs negatively correlated with eGFR, of which CCL5 and CXCL1 mRNA levels were upregulated in the urinary sediment of patients with DN. In addition, urinary sediment mRNA of CXCL1 was negatively correlated with eGFR (r = -0.2275, P = 0.0301) and CCL5 level was negatively correlated with eGFR (r = -0.4388, P < 0.0001) and positively correlated with urinary albumin:creatinine ratio (r = 0.2693, P = 0.0098); also, CCL5 and CXCL1 were upregulated in patients with severe renal interstitial fibrosis. Urinary sediment CCL5 mRNA was an independent predictor of ESRD [hazard ratio 1.350 (95% confidence interval 1.045-1.745)]. CONCLUSIONS: Urinary sediment CCL5 and CXCL1 mRNAs were upregulated in DN patients and associated with a decline in renal function and degree of renal interstitial fibrosis. Urinary sediment CCL5 mRNA could be used as a potential prognostic biomarker of DN.
BACKGROUND: Urinary sediment messenger RNAs (mRNAs) have been shown as novel biomarkers of kidney disease. We aimed to identify targeted urinary mRNAs in diabetic nephropathy (DN) based on bioinformatics analysis and clinical validation. METHODS: Microarray studies of DN were searched in the GEO database and Nephroseq platform. Gene modules negatively correlated with estimated glomerular filtration rate (eGFR) were identified by informatics methods. Hub genes were screened within the selected modules. In validation cohorts, a quantitative polymerase chain reaction assay was used to compare the expression levels of candidate mRNAs. Patients with renal biopsy-confirmed DN were then followed up for a median time of 21 months. End-stage renal disease (ESRD) was defined as the primary endpoint. Multivariate Cox proportional hazards regression was developed to evaluate the prognostic values of candidate mRNAs. RESULTS: Bioinformatics analysis revealed four chemokines (CCL5, CXCL1, CXLC6 and CXCL12) as candidate mRNAs negatively correlated with eGFR, of which CCL5 and CXCL1 mRNA levels were upregulated in the urinary sediment of patients with DN. In addition, urinary sediment mRNA of CXCL1 was negatively correlated with eGFR (r = -0.2275, P = 0.0301) and CCL5 level was negatively correlated with eGFR (r = -0.4388, P < 0.0001) and positively correlated with urinary albumin:creatinine ratio (r = 0.2693, P = 0.0098); also, CCL5 and CXCL1 were upregulated in patients with severe renal interstitial fibrosis. Urinary sediment CCL5 mRNA was an independent predictor of ESRD [hazard ratio 1.350 (95% confidence interval 1.045-1.745)]. CONCLUSIONS: Urinary sediment CCL5 and CXCL1 mRNAs were upregulated in DN patients and associated with a decline in renal function and degree of renal interstitial fibrosis. Urinary sediment CCL5 mRNA could be used as a potential prognostic biomarker of DN.
Authors: Karolina I Woroniecka; Ae Seo Deok Park; Davoud Mohtat; David B Thomas; James M Pullman; Katalin Susztak Journal: Diabetes Date: 2011-07-13 Impact factor: 9.461
Authors: Stefania Di Mauro; Alessandra Scamporrino; Agnese Filippello; Maurizio Di Marco; Maria Teresa Di Martino; Francesca Scionti; Antonino Di Pino; Roberto Scicali; Roberta Malaguarnera; Francesco Purrello; Salvatore Piro Journal: Int J Mol Sci Date: 2022-07-25 Impact factor: 6.208