Study Of Biomarkers For Renal Fibrosis Based On Urinary MRNA Array Analysis

Objective:Renal fibrosis is a histological outcome of chronic kidney disease (CKD) progression. Sustained injury, non-resolving inflammation would set up and trigger the activation and expansion of matrix-producing fibroblasts (myofibroblasts) from multiple sources which promote scar formation and ensure a vicious progression to end-stage kidney failure, which is a complex histopathological processes characterized by over production of extracellular matrix (ECM). At present, the golden standard for renal fibrosis diagnosis is renal biopsy and histological analysis. However, the non-invasive detection of renal fibrosis remains a challenge. Recently, real-time quantitative polymerase chain reaction (PCR)-based urinary RNA detection has emerged as a novel strategy for the identification of biomarkers for kidney disease. The aim of this study was to construct renal fibrosis target real time-PCR array based on urinary mRNA detection and used it to detect urinary mRNAs of CKD patients and IgA nephropathy (IgAN) patients for investigating potential non-invasive biomarkers of renal fibrosis.Method:1. Based on the non-invasive approach to isolate and quantify RNA of specific genes in urinary excretion cells,86 renal fibrosis related gene was chosen to construct specific real-time-PCR array. Fifty biopsy-proved CKD patients were involved to analyze the sensitivity, specificity, and reproducibility of the array.2. About 135 CKD patients proved by renal biopsy and 31 health controls were involved in this study and were divided into training sets and validation sets. Urinary mRNAs were detected by fibrosis target real time-PCR array. The differences of mRNA expression between two groups were calculated. And the relation between mRNA expression levels and serum creatinine (Scr), blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), glomerular sclerosis (GS) score, tubular-interstitial fibrosis (TIF) score were analyzed to identify biomarker of renal fibrosis and evaluate the diagnosis value.3. A total of 74 renal biopsy proved IgAN patients and 31 normal controls from Zhongda Hospotal were involved. IgAN target real time-PCR array was applied to profile the expression of a panel of 90 genes relevant to pathogenesis and progression of IgAN. The relationship between gene expression levels and Scr, eGFR, proteinuria, the "Oxford MEST score", and renal fibrosis scoring in patients and controls were identified to dig out biomarkers of IgAN. Fisher’s linear discriminant analysis was used to weight variables and derive the optimal composite biomarker. Leave-one-out cross-validation (LOOCV) was applied to validate the performance of the composite classifier in practice.Results:1. Our results indicated that the PCR array system achieved a positive call rate greater than 85% with all samples and that 44 out of 50 samples achieved a positive call rate over 95%. A single product of the predicted size from each reaction without secondary products, such as primer dimers, indicates the PCR array’s high specificity. The Ct values of PPC in our experiments were 20.63±0.35 and the variation coefficient was only 1.69%.2. Both training sets and validation sets have proved that VIM mRNA expression was significantly higher in CKD patients compared with normal controls. Spearman correlation showed that the mRNA levels of VIM was significantly correlated with Scr, BUN, eGFR, GS and TIF scores. Multiple logistic regression and ROC analysis further indicated that urine VIM mRNA was a strong predictive parameter of renal fibrosis and could differentiate patients with moderate-to-severe fibrosis from none-to-mild fibrosis group.3. In this study,50 out of 90 mRNAs were differentially expressed between patients and controls. mRNA levels of BCL2, PAI1, TGF-β1, and VIM correlated with RF severity and could effectively discriminated moderate-to-severe fibrosis from none-to-mild (P<0.05). A three-gene signature composed by urinary mRNA levels of BCL2, PAI1, TGF-β1 were developed by Fisher’s linear discriminant analysis. This composite signature showed 91.3% in sensitivity,85.7% in specificity, and the largest AUC was 0.92, with the best Youden’s index was 0.77 (P<0.05).Conclusion:1. We firstly constructed a renal fibrosis target real time-PCR array with well sensitivity, specificity, and reproducibility, which may have potential in clinical application.2. Our study demonstrated that detection of VIM mRNA in urinary sediments could well predict renal fibrosis severity, which suggested this will serve as a novel independent non-invasive biomarker to monitor the progression of kidney fibrosis.3. A combined detection of urinary mRNAs (BCL2, PAI1, TGF-β1) is superior to single molecule marker in reflecting fibrosis status, suggesting this might serve as a novel method to evaluate renal fibrosis in IgAN.