小桶
S100A8型
细胞因子
炎症
肿瘤坏死因子α
医学
接收机工作特性
基因表达
分子生物学
折叠变化
基因
免疫学
生物
内科学
基因本体论
生物化学
作者
Guanghan Sun,Jian Liu,Lei Wan,Wei Liu,Yan Long,Bingxi Bao,Ying Zhang
标识
DOI:10.3785/j.issn.1008-9292.2020.12.09
摘要
OBJECTIVE To detect the differentially expressed inflammatory proteins in acute gouty arthritis (AGA) with protein chip. METHODS The Raybiotech cytokine antibody chip was used to screen the proteomic expression in serum samples of 10 AGA patients and 10 healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were applied to determine the biological function annotation of differentially expressed proteins and the enrichment of signal pathways. ELISA method was used to verify the differential protein expression in 60 AGA patients and 60 healthy subjects. The ROC curve was employed to evaluate the diagnostic value of differential proteins in AGA patients. RESULTS According to|log2FC|>log2 1.2 and corrected P<0.01, 4 most differentially expressed proteins in AGA patients were identified, including tumor necrosis factor receptor super family members Ⅱ (TNF RⅡ), macrophage inflammatory protein 1β (MIP-1β), interleukin-8 (IL-8), and granulocyte-macrophage colony stimulating factor (GM-CSF). GO and KEGG enrichment analysis showed that the differentially expressed proteins were related to inflammation, metabolism and cytokine pathways. The ELISA results showed that serum levels of differentially expressed proteins were significantly different between AGA patients and healthy subjects(all P<0.01). ROC curve analysis showed that the areas under the curve (AUCs) of GM-CSF, IL-8, MIP-1β and TNF RⅡ for predicting AGA were 0.657 (95% CI: 0.560-0.760, sensitivity: 68.33%, specificity: 50.00%), 0.994 (95% CI: 0.980-1.000, sensitivity: 100.00%, specificity: 61.67%), 0.980 (95% CI: 0.712-0.985, sensitivity: 95.00%, specificity: 98.33%) and 0.965 (95% CI: 0.928-1.000, sensitivity: 100.00%, specificity: 10.00%), respectively. CONCLUSIONS Proteomics can be applied to identify the biomarkers of AGA, which may be used for risk prediction and diagnosis of AGA patients.
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