The Reconstruction of Transcriptional Networks Reveals Critical Genes with Implications for Clinical Outcome of Multiple Myeloma
The Reconstruction of Transcriptional Networks Reveals Critical Genes with Implications for Clinical Outcome of Multiple Myeloma
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DOI：
10.1158/1078-0432.ccr-11-0596
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发表时间：
2011-12-01
影响因子：
11.5
通讯作者：
Neri, Antonino
中科院分区：
文献类型：
--
作者：
Agnelli, Luca;Forcato, Mattia;Neri, Antonino
目的：微阵列技术和生物信息学分析的结合使用提高了我们对多发性骨髓瘤（MM）生物学复杂性的理解。相比之下，在尝试预测临床结果时应用相同的技术来识别异质分子特征则不太成功。在此，我们在来自公开基因表达集的 MM 患者的 1,883 个样本中重建了基因调控网络，以便能够在独立的数据集中鉴定与不良预后相关的稳健且可重复的特征。实验设计：通过使用精确细胞网络重建算法 (ARACNe) 和来自七个 MM 数据集的微阵列数据来重建基因调控网络。应用网络组件的批判性分析来识别在转录网络中发挥重要作用的基因，这些基因在数据集之间是保守的。结果：网络批判性分析表明（i）CCND1和CCND2是最关键的基因； (ii) CCND2、AIF1 和 BLNK 在数据集中共享的连接数量最多； (iii)具有预后能力的稳健基因特征源自最关键的转录本和最连接节点的共享主要邻居。具体来说，一个关键基因模型（包括 FAM53B、KIF21B、WHSC1 和 TMPO）和一个邻居基因模型（包括 BLNK 共享邻居 CSGALNACT1 和 SLC7A7）预测了所有数据集中的生存情况以及后续信息。结论：在大量 MM 肿瘤中重建基因调控网络定义了具有预后重要性的稳健且可重复的特征，并可能导致识别对 MM 生物学至关重要的新分子机制。临床癌症研究； 17（23）； 7402-12。 (C)2011 AACR。
Purpose: The combined use of microarray technologies and bioinformatics analysis has improved our understanding of biological complexity of multiple myeloma (MM). In contrast, the application of the same technology in the attempt to predict clinical outcome has been less successful with the identification of heterogeneous molecular signatures. Herein, we have reconstructed gene regulatory networks in a panel of 1,883 samples from MM patients derived from publicly available gene expression sets, to allow the identification of robust and reproducible signatures associated with poor prognosis across independent data sets.Experimental Design: Gene regulatory networks were reconstructed by using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) and microarray data from seven MM data sets. Critical analysis of network components was applied to identify genes playing an essential role in transcriptional networks, which are conserved between data sets.Results: Network critical analysis revealed that (i) CCND1 and CCND2 were the most critical genes; (ii) CCND2, AIF1, and BLNK had the largest number of connections shared among the data sets; (iii) robust gene signatures with prognostic power were derived from the most critical transcripts and from shared primary neighbors of the most connected nodes. Specifically, a critical-gene model, comprising FAM53B, KIF21B, WHSC1, and TMPO, and a neighbor-gene model, comprising BLNK shared neighbors CSGALNACT1 and SLC7A7, predicted survival in all data sets with follow-up information.Conclusions: The reconstruction of gene regulatory networks in a large panel of MM tumors defined robust and reproducible signatures with prognostic importance, and may lead to identify novel molecular mechanisms central to MM biology. Clin Cancer Res; 17(23); 7402-12. (C)2011 AACR.