Affiliations: Institute of Systems Biology, Novosibirsk,
Russia | Design Technological Institute of Digital Techniques,
SB RAS, Novosibirsk, Russia | Institute of Cytology and Genetics, SB RAS,
Novosibirsk, Russia | BIOBASE GmbH, Wolfenbüttel, Germany
Abstract: Albeit the great number of microarray data available on breast
cancer, reliable identification of genes associated with breast cancer
development remains a challenge. The aim of this work was to develop a novel
method of meta-analysis for the identification of differentially expressed
genes integrating results of several independent microarray experiments. We developed a statistical method for identification of up- and
down-regulated genes to perform meta-analysis. The method takes advantage of
hypergeometric and binomial distributions. Using our method we performed
meta-analysis of five data sets from independent cDNA-microarray experiments on
breast cancer. The meta-analysis revealed that 3.2% and 2.8% of the
24,726 analyzed genes are significantly (P-value
< 0.01) down- and up-regulated, respectively. We also show
that properly applied meta-analysis is a good tool for comparison of different
breast cancer subtypes. Our meta-analysis showed that the expression of the
majority of genes does not show significant differences in different subtypes
of breast cancer. Here, we report the rationale, development and application of
meta-analysis that enable us to identify biologically meaningful features of
breast cancer. The algorithm we propose for the meta-analysis can reveal the
features specific to the breast cancer subtypes and those common to breast
cancer. The results allow us to revise the previously generated lists of genes
associated with breast cancer and also identify most promising anticancer
drug-target genes.
Keywords: Breast cancer, cDNA microarray data, meta-analysis, differentially expressed genes, drug targets, Cyclonet database, hypergeometric distribution
