Wang Hong-Jiu, Zhou Meng, Jia Li, Sun Jie, Shi Hong-Bo, Liu Shu-Lin, Wang Zhen-Zhen
College of Science, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China (mainland).
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China (mainland).
Med Sci Monit. 2015 Aug 29;21:2557-66. doi: 10.12659/MSM.894887.
Chromosomal instability is a hallmark of cancer. Chromosomal imbalances, like amplifications and deletions, influence the transcriptional activity of genes. These imbalances affect not only the expression of genes in the aberrant chromosomal regions, but also that of related genes, and may be relevant to the cancer status.
Here, we used the 7 publicly available microarray studies in breast cancer tissues and propose a general and unsupervised method by using the gene expression data and related gene information to systematically identify aberrant chromosomal regions. This method aimed to identify the chromosomal regions where the genes and their related genes both show consistent changes in the expression levels. Such patterns have been reported to be associated with the chromosomal aberrations and may be used in cancer diagnosis.
We compared 488 tumor and 222 normal samples from 7 microarray-based human breast cancer studies and detected the amplifications of 8q11.21, 14q32.11, 4q21.23, 18q11.2, Xq28, and the deletions of 3p24.1, 10q23.2 (BSCG1), 20p11.21, 9q21.13, and 1q41, which may be involved in the novel mechanisms of tumorigenesis. In addition, several known pathogenic genes, transcription factors (TFs), and microRNAs (miRNAs) associated with breast cancer were found.
This approach can be applied to other microarray studies, which provide a new and useful method for exploring chromosome structural variations in different types of diseases.
染色体不稳定是癌症的一个标志。染色体失衡,如扩增和缺失,会影响基因的转录活性。这些失衡不仅影响异常染色体区域中基因的表达,还会影响相关基因的表达,并且可能与癌症状态相关。
在此,我们使用了7项公开的乳腺癌组织微阵列研究,并提出了一种通用的无监督方法,通过使用基因表达数据和相关基因信息来系统地识别异常染色体区域。该方法旨在识别基因及其相关基因在表达水平上均表现出一致变化的染色体区域。据报道,这种模式与染色体畸变有关,可用于癌症诊断。
我们比较了来自7项基于微阵列的人类乳腺癌研究的488个肿瘤样本和222个正常样本,检测到8q11.21、14q32.11、4q21.23、18q11.2、Xq28的扩增,以及3p24.1、10q23.2(BSCG1)、20p11.21、9q21.13和1q41的缺失,这些可能参与肿瘤发生的新机制。此外,还发现了一些与乳腺癌相关的已知致病基因、转录因子(TFs)和微小RNA(miRNAs)。
这种方法可应用于其他微阵列研究,为探索不同类型疾病中的染色体结构变异提供了一种新的有用方法。