Integration of gene expression data and genetic variations involved in breast cancer.

PURPOSE The studies of transcriptome and genome involved in breast cancer are effectively promote the understanding of biological processes and the development of novel targeted therapies. Here we performed an integrated analysis of gene expression and genetic variation to disclose the molecular pathogenesis in breast cancer. METHODS Gene expression profiles were applied to identify differential expression levels of genes between breast cancer and normal subjects. DNA sequencing data were extracted to analyze gene mutational information including number of mutations, number of mutated genes and their chromosomal distributions. Correlation analysis of gene mutations and differential expression was performed. Network-based approach was applied to compare the topological properties between the differentially expressed (DE) genes prone to mutation and those that(were not. Two-tailed p<0.05 was considered as statistically significant. RESULTS Statistical analysis showed that DE genes presented significantly positive correlation with the number of mutations (p=1.267E(-05)), mutated genes (p=0.00001) and total genes in the genome (p=2.489E(-06)). There were 81 genes, both DE and mutant, and they were distributed on chromosome 4 (N=51), chromosome 15 (N=29), and chromosome 18 (N=1). These 81 genes showed an increase in the number of genes interacting with in the protein-protein network. CONCLUSION Analysis of the integration of transcriptome and genome in breast cancer disclosed distinctive topology between the DE genes prone to mutation and those that were not.

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