Correlation Of Expression Profiles Between MicroRNAs And Graft Function After Kidney Transplantation

In recent years,the short-term survival rate of renal grafts has been significantly improved,but the long-term,stable and functional survival of renal allograft remains unsatisfactory.The status of renal grafts after transplantation is evaluated mainly by serum creatinine,biopsy and histopathology.Although the detection of serum creatinine is noninvasive,simple,but its specificity is poor and not early enough,thus it couldn't predict the progression of chronic renal graft chronic injury.Although renal biopsy is the gold standard for the evaluation of graft status,it has a large risk of injury to transplant recipients and couldn't be used frequently,and the results are greatly influenced by subjective factors,Thus the acceptance of the biopsy for recipients is poor.Thus,conventional methods couldn't meet the needs of early,dynamic and noninvasive detection of renal function after transplantation.A new type of noninvasive and accurate marker is urgently needed to achieve the purpose of dynamic monitoring of graft function.MicroRNA(miRNA)is a small noncoding small RNA molecule that regulates gene expression at the post transcriptional level by interacting with m RNA.MiRNA participates in the regulation of many biological functions and plays an important role in immune regulation.MiRNA is stable in serum and PBMC and could be potential used as a noninvasive biomarker.At present,a number of studies have shown that the expression of miRNA in the specific recipient PBMC is related to the physiological and pathological state after renal transplantation.It is suggested that the effective miRNA combination detection combined with the algorithm analysis may help to develop the new strategies for renal function assessment and prediction after transplantation.We reviewed the studies about miRNA from PBMC in renal transplantation and selected nine MicroRNAs as targets for renal function assessment: miR-142-5p,miR-142-3p,miR-223,miR-211,miR-486-5p,mi R-155,miR-10 b,miR-30a-3p,let-7c.We also selected three core clinical indicators associated with renal function after transplantation: creatinine,Cystatin C and urea.In the case of the grouping of three clinical indicators,appropriate statistical methods were used to screen the differential miRNAs among the 9 miRNAs,then we combined the specific miRNA expressionprofile with machine learning algorithm analysis,developing a new tools of renal function assessment and prediction after transplantation.The methods are as follows:We collected 5-10 ml of peripheral blood from renal transplant patients,PBMCs were separated,total miRNAs were extracted,and then quantitatively determined nine miRNAs by qRT-PCR.At the same time,the serum creatinine,Cystatin C and urea detection data of patients were collected 1-3 days before and 3-4 weeks after sampling.Data analysis for single miRNA: Mann Whitney test was used for statistical differences analysis of two groups: according to the patient's two physical examination data,the patients were divided into normal and abnormal groups according to the threshold of creatinine,Cystatin C and urea respectively.In the group of three clinical indicators,the miRNAs expression in the two groups of PBMC was analyzed.Data analysis for combined miRNA: a combination of several miRNAs with significant differences in statistical analysis using logical regression method or a machine learning model.The patients were divided into two groups by using the creatinine / cystatin C/ urea(normal / abnormal)after 3-4 weeks of sampling.In the overall dataset,80% is selected as training data set(training set)and the remaining 20%as a testing data set(testing set).The results are as follows:1.We found that the expression of miRNA-142-3p,miRNA-142-5p,miRNA-10 b and miRNA-223 was significantly correlated with the level of creatinine,and the expression of miRNA-142-3p and miRNA-142-5p was significantly correlated with cystatin-C level.In addition,there was no correlation between the 9 miRNA expression levels and urea level.2.The sensitivity and specificity of each single miRNA used as a biomarker for graft function monitoring after renal transplantation was not strong.The sensitivity and specificity of combined miRNAs panel were improved.(1)The AUC values of the creatinine levels measured by miRNA-142-3p,miRNA-142-5p and miRNA-10 b expressions were 0.7018,0,6930,0,6892 respectively.The AUC values of the creatinine levels of second tests with miRNA-142-3p,miRNA-142-5p,miRNA-10 b and miRNA-223 expressions were 0.6942,0.7030,0.6867,0.7155,respectively.(2)The AUC values of the levels of cystatin C in the first test were 0.6944 and0.7251,respectively,and the AUC values of the second tests of miRNA-142-3p and miRNA-142-5p were 0.6980 and 0.6942,respectively,by miRNA-142-3p and miRNA-142-5p.(3)The AUC value of creatinine prediction model(combined miRNA-142-3p,miRNA-142-5p,miRNA-223 and miRNA-10b)established by two logistic regression analysis was 0.817.The accuracy of the model established by random forest method in the testing set is 62.5%.(4)The AUC value of cystatin C model(combined mi RNA-142-3p and miRNA-142-5)established by two logistic regression analysis is 0.7769.The accuracy of the model established by random forest method in the testing set is 50%.In this study,the specific miRNA expression profile in PBMCs of renal transplant recipients was used as a parameter for the first time,and our study is a preliminary attempt to use machine learning algorithms to achieve the prediction and assessment of renal graft function,which provided ideas and basis for new diagnostic and assessment methods after renal transplantation.