Constructing a diagnostic prediction model for antibody-mediated rejection after kidney transplantation based upon bioinformatics and machine learning algorithms
10.3760/cma.j.cn421203-20230928-00109
- VernacularTitle:基于生物信息学和机器学习算法构建肾移植术后抗体介导排斥反应的诊断预测模型
- Author:
Jiyue WU
1
;
Zejia SUN
;
Qing BI
;
Xuemeng QIU
;
Wei WANG
Author Information
1. 首都医科大学附属北京朝阳医院泌尿外科，北京 100020
- Keywords:
Kidney transplantation;
Antibody-mediated rejection;
Bioinformatics;
Machine-learning;
Diagnostic model
- From:
Chinese Journal of Organ Transplantation
2024;45(10):718-727
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a diagnostic prediction nomogram for antibody-mediated rejection (AMR) after kidney transplantation (KT) based upon peripheral blood gene expression profiling and preliminarily screening potential drugs for AMR.Methods:Seven large kidney transplant cohort datasets related to AMR were retrieved from the database of GEO. Differential expression analysis was utilized for identifying differentially expressed genes between AMR and normal recipients. Multiple machine learning algorithms of random forest (RF), extreme gradient boosting (XGB), support vector machine (SVM) and generalized linear model (GLM) were employed for constructing diagnostic models for AMR after kidney transplantation. Receiver operating characteristic (ROC) curve was plotted for comparing the accuracy of each model. The key genes of optimal model were integrated for creating a diagnostic prediction nomogram for AMR. Calibration curve and decision curve analyses were employed for evaluating the accuracy of nomogram. The differentially expressed genes from biopsy tissues of AMR recipients were uploaded to the database of CMap for identifying potential therapeutic drugs through screening Top 5 compounds with opposite expression patterns to AMR.Results:Seven genes of CXCL10, FCGR1B, GBP5, CD69, LY96, BCL2A1 and EVI2A were over-expressed in both peripheral blood and biopsy tissues of AMR recipients. There were statistically significant differences with recipients without AMR (FDR<0.05). The AMR diagnostic model based upon RF algorithm demonstrated the highest AUC value (0.904) among various machine learning algorithms. Its AUC values were 0.876 and 0.824 in external datasets of GSE50084 and GSE175718. As for the diagnostic prediction nomogram constructed through integrating five key genes of BCL2A1, CXCL10, FCGR1BP, CD69 & EVI2A from RF model, calibration curve indicated that the predicted outcomes of nomogram approximated actual outcomes. Decision curve indicated that net benefit rate of nomogram was higher than that of extreme curves over a wide range of horizontal axis. The predicted results of CMap suggested that Top 5 compounds were raltegravir, rilmenidine, hydrastine, metyrapone and valproic acid.Conclusions:The nomogram constructed based upon peripheral blood gene expression profiling demonstrates high accuracy and generalizability in the diagnosis of AMR. As predicted by CMap, raltegravir, rilmenidine, hydrastine, metyrapone and valproic acid may be potential therapeutic drugs for AMR.
