Construction of predictive ceRNA network and identification of the patterns of immune cells infiltrated in Graves ' ophthalmopathy.
10.11817/j.issn.1672-7347.2023.230118
- Author:
Jiamin CAO
1
;
Haiyan CHEN
2
;
Bingyu XIE
3
;
Yizhi CHEN
3
;
Wei XIONG
3
;
Mingyuan LI
4
Author Information
1. Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha 410013, China. cjm1069839003@163.com.
2. Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha 410013, China. chy0211@139.com.
3. Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
4. Department of Ophthalmology, Third Xiangya Hospital, Central South University, Changsha 410013, China. 600703@csu.edu.cn.
- Publication Type:Journal Article
- Keywords:
Graves’ ophthalmopathy;
bioinformatics analysis;
competing endogenous RNA;
immune cells
- MeSH:
Humans;
CD8-Positive T-Lymphocytes;
RNA, Long Noncoding/genetics*;
Algorithms;
CD4-Positive T-Lymphocytes;
Down-Regulation;
Graves Ophthalmopathy/genetics*;
Gene Regulatory Networks;
MicroRNAs/genetics*;
Serine-Arginine Splicing Factors;
Phosphoproteins
- From:
Journal of Central South University(Medical Sciences)
2023;48(8):1185-1196
- CountryChina
- Language:English
-
Abstract:
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 (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.
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.
