基于Au蛋白芯片技术的狼疮肾炎患者尿蛋白标志物研究

目的 寻找狼疮肾炎(LN)患者尿蛋白标志物,探讨基于Au蛋白芯片技术预测LN的应用价值.方法 采用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术及Au蛋白芯片检测166例LN患者和对照者尿蛋白指纹图,通过t检验评价蛋白表达差异性,筛选LN患者尿标志蛋白并结合人工神经网络(ANN)技术建立诊断模型,评价其预测LN的价值.对部分筛选的差异蛋白通过比对标准蛋白质谱数据进行初步鉴定.结果 LN患者与对照者尿中差异蛋白质峰有24个(t值为-6.44～10.14,P<0.05),通过BPS软件筛选4863、9744、8762、33 832、67 403和80 806质荷比(m/z)蛋白质建立的ANN模型预测LN的灵敏度为100%(26/26),特异度为95%(38/40).其中5个11 700、22 509、33 832、67 403和80 806 m/z蛋向与标准蛋白质谱比对显示可能为β_2-微球蛋白、视黄醇结合蛋白、α_1-微球蛋白、白蛋白和转铁蛋白.结论 基于Au蛋白芯片技术的尿蛋白指纹图谱检测在LN的早期警示、判断蛋白尿类型及指导免疫抑制剂的使用中具有潜在应用价值.

Screening of protein markers in urine from lupus nephritis patients using Au protein chip technology

Objective To explore protein markers in urine from patients with lupus nephritis (LN) by proteomic method and discuss its clinical significance in laboratory diagnosis of LN. Methods Surface-enhanced laser desorption-ionization time of flight mass spectrometry (SELDI-TOF-MS) and Au Chip (ProteinChip Gold Array) were used to detect the urinary protein profilings of 166 cases, including patients with LN and the control samples. The differences between the two groups were evaluated by t-test. The differential peaks were screened and the decision tree pattern was developed by artificial neural network (ANN). The model was blindly tested to validate its diagnostic efficiency. Some differential proteins were identified by molecular mass compared with the standard proteins. Results Twenty-four distinguished protein peaks (t value: -6.44～10.14, P<0.05) were obtained between LN and the control groups. A diagnostic model constructed by BPS with proteins of m/z 4863, 9744, 8762, 33 832, 67 403 and 80 806 could be used to successfully diagnose LN with the sensitivity of 100% (26/26) and specificity of 95% (38/40). Five specific protein peaks (m/z at 11 700, 22 509, 33 832, 67 403 and 80 806) were compared with molecular mass of the standard proteins and considered as β_2-microglobulin, retinol binding protein, al-microglobulin, albumin and transferrin, respectively. Conclusion These results suggest that the specific peaks detected by Au chip have the potentials in early warning of LN, identification of albuminuria and direction of proper use of immuno-suppressants.