Han Qianguang, Zhang Xiang, Ren Xiaohan, Hang Zhou, Yin Yu, Wang Zijie, Chen Hao, Sun Li, Tao Jun, Han Zhijian, Tan Ruoyun, Gu Min, Ju Xiaobing
Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Urology, Affiliated Hospital of Nantong University, Nantong, China.
Front Genet. 2022 Mar 21;13:844709. doi: 10.3389/fgene.2022.844709. eCollection 2022.
Early diagnosis and detection of acute rejection following kidney transplantation are of great significance for guiding the treatment and improving the prognosis of renal transplant recipients. In this study, we are aimed to explore the biological characteristics of biopsy-proven acute rejection (BPAR) and establish a predictive model. Gene expression matrix of the renal allograft samples in the GEO database were screened and included, using Limma R package to identify differentially expressed transcripts between BPAR and No-BPAR groups. Then a predictive model of BPAR was established based on logistic regression of which key transcripts involved in the predictive model were further explored using functional enrichment analyses including Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). A total of four studies (GSE129166, GSE48581, GSE36059, and GSE98320) were included for extensive analysis of differential expression. 32 differential expressed transcripts were observed to be significant between two groups after the pooled analysis. Afterward, a predictive model containing the five most significant transcripts (IDO1, CXCL10, IFNG, GBP1, PMAIP1) showed good predictive efficacy for BPAR after kidney transplantation (AUC = 0.919, 95%CI = 0.902-0.939). Results of functional enrichment analysis showed that The functions of differential genes are mainly manifested in chemokine receptor binding, chemokine activity, G protein-coupled receptor binding, etc. while the immune infiltration analysis indicated that immune cells mainly related to acute rejection include Macrophages. M1, T cells gamma delta, T cells CD4 memory activated, eosinophils, etc. We have identified a total of 32 differential expressed transcripts and based on that, a predictive model with five significant transcripts was established, which was suggested as a highly recommended tool for the prediction of BPAR after kidney transplantation. However, an extensive study should be performed for the evaluation of the predictive model and mechanism involved.
肾移植后急性排斥反应的早期诊断和检测对于指导治疗及改善肾移植受者的预后具有重要意义。在本研究中，我们旨在探索经活检证实的急性排斥反应（BPAR）的生物学特征并建立一个预测模型。筛选并纳入了基因表达综合数据库（GEO数据库）中肾移植样本的基因表达矩阵，使用Limma R包来识别BPAR组和非BPAR组之间差异表达的转录本。然后基于逻辑回归建立BPAR的预测模型，并使用包括基因本体分析（GO）、京都基因与基因组百科全书（KEGG）通路分析以及基因集富集分析（GSEA）在内的功能富集分析进一步探索预测模型中涉及的关键转录本。总共纳入了四项研究（GSE129166、GSE48581、GSE36059和GSE98320）进行差异表达的广泛分析。汇总分析后，观察到两组之间有32个差异表达的转录本具有显著性。随后，一个包含五个最显著转录本（IDO1、CXCL10、IFNG、GBP1、PMAIP1）的预测模型对肾移植后的BPAR显示出良好的预测效能（曲线下面积[AUC]=0.919，95%置信区间[CI]=0.902 - 0.939）。功能富集分析结果表明，差异基因的功能主要体现在趋化因子受体结合、趋化因子活性、G蛋白偶联受体结合等方面，而免疫浸润分析表明与急性排斥反应主要相关的免疫细胞包括M1巨噬细胞、γδT细胞、CD4记忆激活T细胞、嗜酸性粒细胞等。我们总共鉴定出32个差异表达的转录本，并在此基础上建立了一个包含五个显著转录本的预测模型，该模型被认为是肾移植后BPAR预测的高度推荐工具。然而，应进行广泛研究以评估该预测模型及其涉及的机制。
