构建预测 ceRNA 网络并鉴定格雷夫斯眼病中免疫细胞浸润的模式。
Construction of predictive ceRNA network and identification of the patterns of immune cells infiltrated in Graves ophthalmopathy.
机构信息
Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
出版信息
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2023 Aug 28;48(8):1185-1196. doi: 10.11817/j.issn.1672-7347.2023.230118.
OBJECTIVES
Graves' ophthalmopathy (GO) is a multifactorial disease, and the mechanism of non coding RNA interactions and inflammatory cell infiltration patterns are not fully understood. This study aims to construct a competing endogenous RNA (ceRNA) network for this disease and clarify the infiltration patterns of inflammatory cells in orbital tissue to further explore the pathogenesis of GO.
METHODS
The differentially expressed genes were identified using the GEO2R analysis tool. The Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology analysis were used to analyze differential genes. RNA interaction relationships were extracted from the RNA interactome database. Protein-protein interactions were identified using the STRING database and were visualized using Cytoscape. StarBase, miRcode, and DIANA-LncBase Experimental v.2 were used to construct ceRNA networks together with their interacted non-coding RNA. The CIBERSORT algorithm was used to detect the patterns of infiltrating immune cells in GO using R software.
RESULTS
A total of 114 differentially expressed genes for GO and 121 pathways were detected using both the KEGG and gene ontology enrichment analysis. Four hub genes (,and ) were extracted from protein-protein interaction using cytoHubba in Cytoscape, 104 nodes and 142 edges were extracted, and a ceRNA network was identified (). The results of immune cell analysis showed that in GO, the proportions of CD8 T cells and CD4 memory resting T cells were upregulated and downregulated, respectively. The proportion of CD4 memory resting T cells was positively correlated with the expression of .
CONCLUSIONS
This study has constructed a ceRNA regulatory network (MALAT1-MIR21-DDX5) in GO orbital tissue, clarifying the downregulation of the proportion of CD4 stationary memory T cells and their positive regulatory relationship with ceRNA components, further revealing the pathogenesis of GO.
目的
格雷夫斯眼病（GO）是一种多因素疾病，非编码 RNA 相互作用和炎症细胞浸润模式的机制尚不完全清楚。本研究旨在构建该疾病的竞争内源性 RNA（ceRNA）网络，并阐明眼眶组织中炎症细胞的浸润模式，以进一步探讨 GO 的发病机制。
方法
使用 GEO2R 分析工具鉴定差异表达基因。使用京都基因与基因组百科全书（KEGG）和基因本体论分析对差异基因进行分析。从 RNA 相互作用组数据库中提取 RNA 相互作用关系。使用 STRING 数据库识别蛋白质-蛋白质相互作用，并使用 Cytoscape 可视化。使用 StarBase、miRcode 和 DIANA-LncBase Experimental v.2 共同构建 ceRNA 网络及其相互作用的非编码 RNA。使用 R 软件中的 CIBERSORT 算法检测 GO 中浸润免疫细胞的模式。
结果
KEGG 和基因本体论富集分析共检测到 114 个 GO 的差异表达基因和 121 条通路。使用 Cytoscape 中的 cytoHubba 从蛋白质-蛋白质相互作用中提取了 4 个枢纽基因（、和），提取了 104 个节点和 142 个边，并确定了 ceRNA 网络（）。免疫细胞分析的结果表明，在 GO 中，CD8 T 细胞和 CD4 静止记忆 T 细胞的比例分别上调和下调。CD4 静止记忆 T 细胞的比例与 ceRNA 成分的表达呈正相关。
结论
本研究构建了 GO 眼眶组织中的 ceRNA 调控网络（MALAT1-MIR21-DDX5），阐明了 CD4 静止记忆 T 细胞比例的下调及其与 ceRNA 成分的正调节关系，进一步揭示了 GO 的发病机制。
Zotero 插件