Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression
Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression
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DOI：
10.1073/pnas.0401994101
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发表时间：
2004-06-22
影响因子：
11.1
通讯作者：
Chinnaiyan, AM
中科院分区：
文献类型：
--
作者：
Rhodes, DR;Yu, JJ;Chinnaiyan, AM
许多研究已经使用DNA微阵列来识别人类癌症的基因表达特征，然而这些通常无法管理的大特征的关键特征仍然难以捉摸。为了解决这个问题，我们开发了一种统计方法，比较元分析，它识别和评估多个基因表达签名的交叉点，从不同的收集微阵列数据集。我们收集并分析了40个已发表的癌症微阵列数据集，包括来自> 3，700个癌症样本的3800万个基因表达测量值。由此，我们表征了在大多数癌症类型中相对于它们产生的正常组织普遍激活的共同转录谱，这可能反映了肿瘤转化的基本转录特征。此外，我们表征了在各种类型的未分化癌症中通常被激活的转录谱，表明癌细胞进展和避免分化的共同分子机制。最后，我们在独立的数据集上验证了这些转录谱。
Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.