Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells.
Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells.
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使用基于途径的方法对微阵列数据进行荟萃分析确定了人类外周血单核细胞中全身性红斑狼疮的37基因表达签名。
DOI：
10.1186/1741-7015-9-65
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发表时间：
2011-05-30
期刊：
影响因子：
9.3
通讯作者：
Amur S
中科院分区：
文献类型：
--
作者：
Arasappan D;Tong W;Mummaneni P;Fang H;Amur S
许多出版物已经报道了使用微阵列技术来鉴定基因表达特征以推断与人外周血单核细胞中的系统性红斑狼疮（SLE）相关的机制和途径。然而，微阵列数据的荟萃分析方法尚未在SLE中得到很好的探索。在这项研究中，基于通路的荟萃分析应用于四个独立的基因表达寡核苷酸微阵列数据集，以确定SLE的基因表达特征，这些数据集由第五个独立的数据集证实。通过比较来自对照样品和SLE样品的表达微阵列数据，在每个数据集中鉴定差异表达基因（DEG）。使用Incidity Pathway Analysis软件，在四个数据集中的每一个中鉴定与DEG相关的通路。使用离开一个数据设置路径为基础的荟萃分析方法，37个基因的元签名被确定。通过无监督学习方法观察到，这种SLE元特征清楚地将SLE患者与对照区分开来。元签名的最终确认是通过将元签名应用于第五个独立数据集来实现的。新的路径为基础的荟萃分析方法被证明是一个有用的技术分组不同的微阵列数据集。该技术允许在四个不同的数据集上得出经验证的结论，并由独立的第五个数据集证实。通过使用这种方法确定的元签名和途径可以作为确定SLE治疗靶点的来源，并可能用于诊断和监测目的。此外，元分析方法提供了一个简单，直观的解决方案，结合不同的微阵列数据集，以确定一个强大的元签名。请参阅研究摘要：http://genomemedicine.com/content/3/5/30
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. Please see Research Highlight: http://genomemedicine.com/content/3/5/30
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影响因子：
3.5
作者：
Teramoto, Kae;Negoro, Nobuo;Miura, Katsuyuki
通讯作者：
Miura, Katsuyuki
DOI：
10.1084/jem.20021553
发表时间：
2003-03-17
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Bennett L;Palucka AK;Arce E;Cantrell V;Borvak J;Banchereau J;Pascual V
通讯作者：
Pascual V
影响因子：
--
作者：
Bauer, Jason W.;Petri, Michelle;Batliwalla, Franak M.;Koeuth, Thearith;Wilson, Joseph;Slattery, Catherine;Panoskaltsis-Mortari, Angela;Gregersen, Peter K.;Behrens, Timothy W.;Baechler, Emily C.
通讯作者：
Baechler, Emily C.
来自多个微阵列实验的基因表达数据的荟萃分析的潜在变量方法。
影响因子：
3
作者：
Choi, Hyungwon;Shen, Ronglai;Chinnaiyan, Arul M;Ghosh, Debashis
通讯作者：
Ghosh, Debashis
影响因子：
4.9
作者：
Thibault DL;Graham KL;Lee LY;Balboni I;Hertzog PJ;Utz PJ
通讯作者：
Utz PJ