Screening of primary open-angle glaucoma diagnostic markers based on immune-related genes and immune infiltration

被引:0
|
作者
Suo, Lingge [1 ,2 ]
Dai, Wanwei [1 ,2 ]
Qin, Xuejiao [3 ]
Li, Guanlin [4 ]
Zhang, Di [1 ,2 ]
Cheng, Tian [5 ]
Yao, Taikang [5 ]
Zhang, Chun [1 ,2 ]
机构
[1] Peking Univ Third Hosp, Dept Ophthalmol, 49 North Garden Rd, Beijing 100191, Peoples R China
[2] Peking Univ Third Hosp, Beijing Key Lab Restorat Damaged Ocular Nerve, 49 North Garden Rd, Beijing 100191, Peoples R China
[3] Shandong Univ, Dept Ophthalmol, Hosp 2, Jinan, Shandong, Peoples R China
[4] Peking Univ Third Hosp, Clin Stem Cell Res Ctr, Beijing, Peoples R China
[5] Peking Univ, Sch Basic Med Sci, Beijing, Peoples R China
来源
BMC GENOMIC DATA | 2022年 / 23卷 / 01期
关键词
Bioinformatics analysis; Primary open-angle glaucoma; Optic nerve; Trabecular meshwork; Immune infiltration; TRABECULAR MESHWORK; EXPRESSION; MECHANISMS; SEQUENCE; KERATIN;
D O I
10.1186/s12863-022-01072-8
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Purpose Primary open-angle glaucoma (POAG) continues to be a poorly understood disease. Although there were multiple researches on the identification of POAG biomarkers, few studies systematically revealed the immune-related cells and immune infiltration of POAG. Bioinformatics analyses of optic nerve (ON) and trabecular meshwork (TM) gene expression data were performed to further elucidate the immune-related genes of POAG and identify candidate target genes for treatment. Methods We performed a gene analysis of publicly available microarray data, namely, the GSE27276-GPL2507, GSE2378-GPL8300, GSE9944-GPL8300, and GSE9944-GPL571 datasets from the Gene Expression Omnibus database. The obtained datasets were used as input for parallel pathway analyses. Based on random forest and support vector machine (SVM) analysis to screen the key genes, significantly changed pathways were clustered into functional categories, and the results were further investigated. CIBERSORT was used to evaluate the infiltration of immune cells in POAG tissues. A network visualizing the differences between the data in the POAG and normal groups was created. GO and KEGG enrichment analyses were performed using the Metascape database. We divided the differentially expressed mRNAs into upregulated and downregulated groups and predicted the drug targets of the differentially expressed genes through the Connectivity Map (CMap) database. Results A total of 49 differentially expressed genes, including 19 downregulated genes and 30 upregulated genes, were detected. Five genes ((Keratin 14) KRT14, (Hemoglobin subunit beta) HBB, (Acyl-CoA Oxidase 2) ACOX2, (Hephaestin) HEPH and Keratin 13 (KRT13)) were significantly changed. The results showed that the expression profiles of drug disturbances, including those for avrainvillamide-analysis-3, cytochalasin-D, NPI-2358, oxymethylone and vinorelbine, were negatively correlated with the expression profiles of disease disturbances. This finding indicated that these drugs may reduce or even reverse the POAG disease state. Conclusion This study provides an overview of the processes involved in the molecular pathogenesis of POAG in the ON and TM. The findings provide a new understanding of the molecular mechanism of POAG from the perspective of immunology.
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页数:16
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