Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA.
Department of Systems Biology, Columbia University, New York, NY, 10032, USA.
Sci Rep. 2020 Oct 14;10(1):17199. doi: 10.1038/s41598-020-74276-6.
Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.
对癌症患者活检的大型基因表达数据集进行分析,可以识别代表癌症中特定生物分子事件的共表达特征。这些特征中的一些涉及由于存在拷贝数改变 (CNAs) 而基因组共定位的基因,对这些基因表达的分析提供了有关它们作为癌基因或肿瘤抑制基因的综合作用的有价值的信息。在这里,我们专注于在对来自癌症基因组图谱的基因表达和 CNA 数据进行全面分析后,由于基因组特定区域的驱动扩增和缺失而在多种癌症类型中存在的此类特征的发现和解释。