Purohit Sharad, Podolsky Robert, Schatz Desmond, Muir Andy, Hopkins Diane, Huang Yi-Hua, She Jin-Xiong
Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, GA 30912, USA.
Proteomics. 2006 Dec;6(24):6405-15. doi: 10.1002/pmic.200600420.
The SELDI-TOF technique was used to profile serum proteins from Type 1 diabetes (T1D) patients and healthy autoantibody-negative (AbN) controls. Univariate and multivariate analyses were performed to identify putative biomarkers for T1D and to assess the reproducibility of the SELDI technique. We found 146 protein/peptide peaks (581 total peaks discovered) in human serum showing statistical differences in expression levels between T1D patients and controls, with 84% of these peaks showing technical replication. Because individual proteins did not offer great power for disease prediction, we used our model averaging approach that combines the information from multiple multivariate models to accurately classify T1D and control subjects (88.9% specificity and 90.0% sensitivity). Analyses of a test subset of the data showed less accuracy (82.8% specificity and 76.2% sensitivity), although the results are still positive. Unfortunately, no multivariate model could be replicated using the same samples. This first attempt of high throughput analyses of the human serum proteome in T1D patients suggests that model averaging is a viable method for developing biomarkers; however, the reproducibility of SELDI-TOF is currently not sufficient to be used for classification of complex diseases like T1D.
表面增强激光解吸电离飞行时间（SELDI-TOF）技术用于分析1型糖尿病（T1D）患者和自身抗体阴性（AbN）健康对照者的血清蛋白。进行单变量和多变量分析以确定T1D的潜在生物标志物，并评估SELDI技术的可重复性。我们在人血清中发现了146个蛋白质/肽峰（共发现581个峰），这些峰在T1D患者和对照者之间的表达水平存在统计学差异，其中84%的峰显示出技术重复性。由于单个蛋白质对疾病预测的能力有限，我们使用了模型平均方法，该方法结合了多个多变量模型的信息，以准确区分T1D患者和对照者（特异性为88.9%，敏感性为90.0%）。对数据测试子集的分析显示准确性较低（特异性为82.8%，敏感性为76.2%），尽管结果仍然是阳性的。不幸的是，使用相同样本无法复制多变量模型。对T1D患者血清蛋白质组进行高通量分析的首次尝试表明，模型平均是开发生物标志物的一种可行方法；然而，目前SELDI-TOF的可重复性不足以用于像T1D这样的复杂疾病的分类。
