Department of Urology Children's Hospital of Chongqing Medical University National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
Chongqing Key Laboratory of Structural Birth Defect and Reconstruction, Chongqing, China.
J Immunol Res. 2024 Jun 24;2024:6908968. doi: 10.1155/2024/6908968. eCollection 2024.
BACKGROUND: Kidney transplantation (KT) is the best treatment for end-stage renal disease. Although long and short-term survival rates for the graft have improved significantly with the development of immunosuppressants, acute rejection (AR) remains a major risk factor attacking the graft and patients. The innate immune response plays an important role in rejection. Therefore, our objective is to determine the biomarkers of congenital immunity associated with AR after KT and provide support for future research. MATERIALS AND METHODS: A differential expression genes (DEGs) analysis was performed based on the dataset GSE174020 from the NCBI gene Expression Synthesis Database (GEO) and then combined with the GSE5099 M1 macrophage-related gene identified in the Molecular Signatures Database. We then identified genes in DEGs associated with M1 macrophages defined as DEM1Gs and performed gene ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. Cibersort was used to analyze the immune cell infiltration during AR. At the same time, we used the protein-protein interaction (PPI) network and Cytoscape software to determine the key genes. Dataset, GSE14328 derived from pediatric patients, GSE138043 and GSE9493 derived from adult patients, were used to verify Hub genes. Additional verification was the rat KT model, which was used to perform HE staining, immunohistochemical staining, and Western Blot. Hub genes were searched in the HPA database to confirm their expression. Finally, we construct the interaction network of transcription factor (TF)-Hub genes and miRNA-Hub genes. RESULTS: Compared to the normal group, 366 genes were upregulated, and 423 genes were downregulated in the AR group. Then, 106 genes related to M1 macrophages were found among these genes. GO and KEGG enrichment analysis showed that these genes are mainly involved in cytokine binding, antigen binding, NK cell-mediated cytotoxicity, activation of immune receptors and immune response, and activation of the inflammatory NF-B signaling pathway. Two Hub genes, namely CCR7 and CD48, were identified by PPI and Cytoscape analysis. They have been verified in external validation sets, originated from both pediatric patients and adult patients, and animal experiments. In the HPA database, CCR7 and CD48 are mainly expressed in T cells, B cells, macrophages, and tissues where these immune cells are distributed. In addition to immunoinfiltration, CD4+T, CD8+T, NK cells, NKT cells, and monocytes increased significantly in the AR group, which was highly consistent with the results of Hub gene screening. Finally, we predicted that 19 TFs and 32 miRNAs might interact with the Hub gene. CONCLUSIONS: Through a comprehensive bioinformatic analysis, our findings may provide predictive and therapeutic targets for AR after KT.
背景:肾移植(KT)是治疗终末期肾病的最佳方法。尽管随着免疫抑制剂的发展,移植物的长期和短期存活率有了显著提高,但急性排斥(AR)仍然是攻击移植物和患者的主要危险因素。先天免疫反应在排斥反应中起着重要作用。因此,我们的目标是确定与 KT 后 AR 相关的先天性免疫标志物,为未来的研究提供支持。
材料和方法:基于 NCBI 基因表达综合数据库(GEO)中的数据集 GSE174020 进行差异表达基因(DEGs)分析,然后结合分子特征数据库中确定的 GSE5099 M1 巨噬细胞相关基因。我们随后确定了与 M1 巨噬细胞相关的 DEGs 中的基因,这些基因被定义为 DEM1Gs,并进行了基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析。Cibersort 用于分析 AR 期间的免疫细胞浸润。同时,我们使用蛋白质-蛋白质相互作用(PPI)网络和 Cytoscape 软件来确定关键基因。使用来自儿科患者的数据集 GSE14328、来自成人患者的数据集 GSE138043 和 GSE9493 来验证 Hub 基因。另外的验证是使用大鼠 KT 模型进行 HE 染色、免疫组织化学染色和 Western Blot。在 HPA 数据库中搜索 Hub 基因以确认其表达。最后,我们构建了转录因子(TF)-Hub 基因和 miRNA-Hub 基因的互作网络。
结果:与正常组相比,AR 组中有 366 个基因上调,423 个基因下调。然后,在这些基因中发现了 106 个与 M1 巨噬细胞相关的基因。GO 和 KEGG 富集分析表明,这些基因主要参与细胞因子结合、抗原结合、NK 细胞介导的细胞毒性、免疫受体和免疫反应的激活以及炎症 NF-B 信号通路的激活。通过 PPI 和 Cytoscape 分析鉴定出两个 Hub 基因,即 CCR7 和 CD48。它们已经在外部验证集(来自儿科患者和成人患者)和动物实验中得到了验证。在 HPA 数据库中,CCR7 和 CD48 主要在 T 细胞、B 细胞、巨噬细胞和这些免疫细胞分布的组织中表达。除免疫浸润外,AR 组中的 CD4+T、CD8+T、NK 细胞、NKT 细胞和单核细胞显著增加,这与 Hub 基因筛选的结果高度一致。最后,我们预测了 19 个 TF 和 32 个 miRNA 可能与 Hub 基因相互作用。
结论:通过全面的生物信息学分析,我们的发现可能为 KT 后 AR 提供预测和治疗靶点。
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