Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2020
[Differentially expressed inflammatory proteins in acute gouty arthritis based on protein chip].
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Sun Guanghan, Liu Jian, Wan Lei, Liu Wei, Long Yan, Bao Bingxi, Zhang Ying
Аннотация
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.
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.