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1.中南大学湘雅三医院眼科,长沙 410013
曹家敏,Email: cjm1069839003@163.com, ORCID: 0000-0003-2498-3454
陈海燕,Email: chy0211@139.com, ORCID: 0009-0009-4536-8590
李明渊,Email: 600703@csu.edu.cn, ORCID: 0000-0003-1655-1360
曹家敏, 陈海燕, 谢冰雨, 等. Graves眼病中预测性ceRNA网络的构建及免疫细胞浸润模式的鉴定[J]. 中南大学学报(医学版), 2023,48(8):1185-1196.
CAO Jiamin, CHEN Haiyan, XIE Bingyu, et al. Construction of predictive ceRNA network and identification of the patterns of immune cells infiltrated in Graves’ ophthalmopathy[J]. Journal of Central South University. Medical Science, 2023,48(8):1185-1196.
曹家敏, 陈海燕, 谢冰雨, 等. Graves眼病中预测性ceRNA网络的构建及免疫细胞浸润模式的鉴定[J]. 中南大学学报(医学版), 2023,48(8):1185-1196. DOI: 10.11817/j.issn.1672-7347.2023.230118.
CAO Jiamin, CHEN Haiyan, XIE Bingyu, et al. Construction of predictive ceRNA network and identification of the patterns of immune cells infiltrated in Graves’ ophthalmopathy[J]. Journal of Central South University. Medical Science, 2023,48(8):1185-1196. DOI: 10.11817/j.issn.1672-7347.2023.230118.
目的,2,Graves眼病(Graves’ ophthalmopathy,GO)是一种多因素疾病,其非编码RNA相互作用的机制及炎症细胞浸润模式尚不完全清楚。本研究旨在构建GO的竞争性内源RNA(competing endogenous RNA,ceRNA)网络并明确眼眶组织中炎症细胞浸润模式以进一步探究GO的发病机制。,方法,2,使用GEO2R工具鉴定差异表达基因(differentially expressed genes,DEGs),对DEGs进行京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)和基因本体分析,并从RNA相互作用数据库中提取RNA的相互作用关系;使用STRING数据库鉴定蛋白质之间的相互作用,并通过Cytoscape进行可视化;基于StarBase、miRcode和DIANA LncBase Experimental v.2数据库中相互作用的非编码RNA来构建ceRNA网络;采用CIBERSORT算法和R语言检测并描述GO中免疫细胞的浸润模式。,结果,2,共获得114个DEGs,通过KEGG和基因本体分析明确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,Hub,基因(,SRSF6、DDX5,、,HNRNPC,和,HNRNPM,),共提取104个节点和142条连接,构建出1个ceRNA网络(,MALAT1-MIR21-DDX5,)。免疫细胞分析结果显示:GO中CD8,+,T细胞和CD4,+,静止型记忆T细胞的比例分别上调和下调。CD4,+,静止型记忆T细胞的比例与MALAT1、MIR21和DDX5的表达呈正相关(均,P,<,0.05)。,结论,2,成功构建出GO眼眶组织内的ceRNA调节网络(,MALAT1-MIR21-DDX5,),明确在GO中CD4,+,静止型记忆T细胞的比例下调且与ceRNA组成成分存在正向调节关系,进一步揭示了GO的发病机制。
Objective,2,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,2,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,2,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 (,SRSF6, DDX5, HNRNPC,and, HNRNPM,) were extracted from protein-protein interaction using cytoHubba in Cytoscape, 104 nodes and 142 edges were extracted, and a ceRNA network was identified (,MALAT1-MIR21-DDX5,). 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 ,MALAT1, MIR21, and DDX5,.,Conclusion,2,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.
Graves眼病生物信息学分析竞争性内源RNA免疫细胞
Graves’ ophthalmopathybioinformatics analysiscompeting endogenous RNAimmune cells
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