Differentially expressed inflammatory proteins in acute gouty arthritis based on protein chip

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
出处
期刊:Journal of Zhejiang University (Medical Sciences) [Science Press]
卷期号:49 (6): 743-749
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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