急性痛风性关节炎致炎蛋白的蛋白质组学研究

目的: 基于血清的蛋白质组学识别有炎症功能的蛋白质信号，为临床诊断急性痛风性关节炎（AGA）寻找生物标志物。方法: 运用RayBiotech细胞因子抗体芯片检测并筛选10例AGA患者和10名健康志愿者血清标本的差异表达蛋白质。进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析，确定差异表达蛋白质的生物学功能注释及信号通路的富集。ELISA法检测差异蛋白质在60例AGA患者和60名健康志愿者中的表达。构建ROC曲线以评估差异表达蛋白质对AGA的诊断价值。结果: 确定了AGA患者4种表达差异最显著的蛋白质，包括肿瘤坏死因子受体Ⅱ（TNF RⅡ）、巨噬细胞炎性蛋白1β（MIP-1β）、IL-8、粒细胞巨噬细胞集落刺激因子（GM-CSF）。富集分析结果表明，差异表达蛋白质与炎症、代谢及细胞因子通路等相关。AGA患者和健康者血清中差异表达蛋白质的表达水平差异均有统计学意义（均P < 0.01）。ROC曲线分析结果显示，GM-CSF预测AGA的AUC为0.657（95% CI：0.560~0.760），敏感度为68.33%，特异度为50.00%；IL-8预测AGA的AUC为0.994（95% CI：0.980~1.000），敏感度为100.00%，特异度为61.67%；MIP-1β预测AGA的AUC为0.980（95% CI：0.712~0.985），敏感度为95.00%，特异度为98.33%；TNF RⅡ预测AGA的AUC为0.965（95% CI：0.928~1.000），敏感度为100.00%，特异度为10.00%。结论: 采用蛋白质组学的方法可以识别AGA的生物标志物，有助于AGA的风险预测和诊断。

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

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. Conclusion: Proteomics can be applied to identify the biomarkers of AGA, which may be used for risk prediction and diagnosis of AGA patients.