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Identification of Aberrant Chromosomal Regions in Human Breast Cancer Using Gene Expression Data and Related Gene Information

Hong-Jiu Wang, Meng Zhou, Li Jia, Jie Sun, Hong-Bo Shi, Shu-Lin Liu, Zhen-Zhen Wang

College of Science, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China (mainland)

Med Sci Monit 2015; 21:2557-2566

DOI: 10.12659/MSM.894887

Available online:

Published: 2015-08-29


BACKGROUND: 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.
MATERIAL AND METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.

Keywords: Breast Neoplasms - genetics, Algorithms, Chromosome Aberrations, Chromosome Mapping, Gene Expression



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