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Promoter composition predicts gene classes in microarray expression analyses of breast cancer

The human genome contains a large amount of cis-regulatory DNA responsible for directing both spatial and temporal gene-expression patterns. Previous studies have shown that, based on their mRNA expression patterns, breast tumors could be divided into five subgroups (Luminal A, Luminal B, Normal-like, ErbB2+-like, and Basal-like), each with a distinct molecular portrait [1]. Whole genome gene-expression analyses of independent sets of breast tumors have revealed repeatedly the robustness of this classification [2]. These patterns have clinical implications in terms of disease-free survival time and are always determined by the same set of genes in all datasets [3]. A list of 552 genes, whose expression in terms of mRNA varied considerably among the different tumors but little between two samples of the same tumor, has been nominated to be sufficient to separate these tumor subgroups. Why exactly these genes? What is the mechanism of their abnormal regulation? Genes are regulated by multiple transcription binding sites that interact with a specific combination of transcription factors. Here we report the promoter composition of the genes that strongly predict the patient subgroups. Using a random expectation value (re-value) to generate a background model, we analyzed a total of 277 cis-elements (Genomatix software). The gene classes showed a clear separation when based solely on their promoter composition. This finding suggests that studying those transcription factors associated with the observed expression pattern in breast cancers could identify novel and important biological pathways, including the NF-κ B and Ets transcription factor families.


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Nordgard, S., Sørlie, T., Chanock, S. et al. Promoter composition predicts gene classes in microarray expression analyses of breast cancer. Breast Cancer Res 7, P4.34 (2005).

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  • Breast Cancer
  • Transcription Factor Family
  • mRNA Expression Pattern
  • Transcription Binding
  • Microarray Expression Analysis