National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
BMC Med. 2011 May 30;9:65. doi: 10.1186/1741-7015-9-65.
A number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE.
In this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set.
Differentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignature to a fifth independent data set.
The novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature.
许多出版物已经报道了使用微阵列技术来识别基因表达特征，以推断与人类外周血单核细胞系统性红斑狼疮（SLE）相关的机制和途径。然而，微阵列数据的荟萃分析方法在 SLE 中尚未得到很好的探索。
在这项研究中，应用基于途径的荟萃分析方法对四个独立的基因表达寡核苷酸微阵列数据集进行分析，以识别 SLE 的基因表达特征，并通过第五个独立数据集进行验证。
通过比较对照样本和 SLE 样本的表达微阵列数据，在每个数据集中都鉴定出了差异表达基因（DEGs）。使用 Ingenuity Pathway Analysis 软件，在四个数据集中确定了与 DEGs 相关的途径。使用基于留一数据集的途径荟萃分析方法，鉴定出了一个 37 基因的荟萃特征。该 SLE 荟萃特征通过无监督学习方法清楚地区分了 SLE 患者和对照者。通过将荟萃特征应用于第五个独立数据集，最终确认了荟萃特征。
新颖的基于途径的荟萃分析方法被证明是一种有用的技术，可以对不同的微阵列数据集进行分组。该技术允许在四个不同的数据集中得出经过验证的结论，并通过第五个独立数据集进行验证。使用这种方法鉴定出的荟萃特征和途径可以作为识别 SLE 治疗靶点的来源，并可能用于诊断和监测目的。此外，该荟萃分析方法为组合不同的微阵列数据集以识别强大的荟萃特征提供了一种简单直观的解决方案。
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