Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China; Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China.
Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China; Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China; Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China.
Transpl Immunol. 2024 Oct;86:102101. doi: 10.1016/j.trim.2024.102101. Epub 2024 Aug 2.
Oxidative stress is an unavoidable process in kidney transplantation and is closely related to the development of acute rejection after kidney transplantation. This study aimed to investigate the biomarkers associated with oxidative stress and their potential biological functions during acute rejection of kidney transplants.
We identified Hub genes using five machine learning algorithms based on differentially expressed genes (DEGs) in the kidney transplant acute rejection dataset GSE50058 and oxidative stress-related genes (OS) obtained from the MSigDB database, and validated them with the datasets GSE1563 and GSE9493, as well as with animal experiments; Subsequently, we explored the potential biological functions of Hub genes using single-gene GSEA enrichment analysis; The Cibersort algorithm was used to explore the altered levels of infiltration of 22 immune cells during acute rejection of renal transplantation, and a correlation analysis between Hub genes and immune cells was performed; Finally, we also explored transcription factors (TFs), miRNAs, and potential drugs that regulate Hub genes.
We obtained a total of 57 genes, which we defined as oxidative stress-associated differential genes (DEOSGs), after intersecting DEGs during acute rejection of kidney transplants with OSs obtained from the MSigDB database; The results of enrichment analysis revealed that DEOSGs were mainly enriched in response to oxidative stress, response to reactive oxygen species, and regulation of oxidative stress and reactive oxygen species; Subsequently, we identified one Hub gene as APOD using five machine learning algorithms, which were validated by validation sets and animal experiments; The results of single-gene GSEA enrichment analysis revealed that APOD was closely associated with the regulation of immune signaling pathways during acute rejection of kidney transplants; The Cibersort algorithm found that the infiltration levels of a total of 10 immune cells were altered in acute rejection, while APOD was found to correlate with the expression of multiple immune cells; Finally, we also identified 154 TFs, 12 miRNAs, and 12 drugs or compounds associated with APOD regulation.
In this study, APOD was identified as a biomarker associated with oxidative stress during acute rejection of kidney transplants using multiple machine learning algorithms, which provides a potential therapeutic target for mitigating oxidative stress injury and reducing the incidence of acute rejection in kidney transplantation.
氧化应激是肾移植过程中不可避免的过程，与肾移植后急性排斥反应的发生密切相关。本研究旨在探讨与氧化应激相关的生物标志物及其在肾移植急性排斥反应中的潜在生物学功能。
我们使用基于差异表达基因（DEGs）的五个机器学习算法和从 MSigDB 数据库获得的与氧化应激相关的基因（OS）来识别 Hub 基因，并使用数据集 GSE1563 和 GSE9493 以及动物实验进行验证；随后，我们使用单基因 GSEA 富集分析来探索 Hub 基因的潜在生物学功能；使用 Cibersort 算法来探索 22 种免疫细胞在肾移植急性排斥反应中的浸润水平的改变，并对 Hub 基因与免疫细胞进行相关性分析；最后，我们还探索了调节 Hub 基因的转录因子（TFs）、miRNAs 和潜在药物。
我们通过将肾移植急性排斥反应时的差异表达基因（DEGs）与 MSigDB 数据库中的 OS 进行交集，获得了总共 57 个基因，我们将其定义为与氧化应激相关的差异基因（DEOSGs）；富集分析的结果表明，DEOSGs 主要富集在对氧化应激、活性氧的反应以及对氧化应激和活性氧的调节；随后，我们使用五种机器学习算法识别出一个 Hub 基因作为 APOD，并通过验证集和动物实验进行验证；单基因 GSEA 富集分析的结果表明，APOD 与肾移植急性排斥反应中免疫信号通路的调节密切相关；Cibersort 算法发现，在急性排斥反应中，共有 10 种免疫细胞的浸润水平发生了改变，而 APOD 与多种免疫细胞的表达相关；最后，我们还鉴定了 154 个 TFs、12 个 miRNAs 和 12 个与 APOD 调节相关的药物或化合物。
本研究使用多种机器学习算法鉴定出 APOD 是肾移植急性排斥反应中与氧化应激相关的生物标志物，为减轻氧化应激损伤和降低肾移植急性排斥反应的发生率提供了潜在的治疗靶点。
