Senol Dogan, Amina Kurtovic-Kozaric and Gunay Karli
Invasive breast cancer is a heterogeneous disease. The analysis of one or a group of specific gene expression profiles may not be enough to understand molecular activities in cancer cells. Therefore, a method which gives us the opportunity to compare similar up and down regulated gene expression profiles, is needed. The main purpose of our work is to sort the extreme high and low expressed genes and extract, compare and cluster them. Expression profiles of 598 samples of invasive breast cancer and 48 samples of normal tissue have been analysed to create a new algorithm called Extreme Gene Expression Family (EGEF). The EGEF algorithm sorted, grouped and compared the highest and the lowest expressed genes (n = 100). According to the hierarchical clustering result, dense and light memberships of gene families are detected. The resulting analysis allows us to predict which genes would show similar expression signatures in invasive breast cancer and to us to recognize specific biological activities and processes. EGEF algorithm can be used to detect expression signatures in other cancers and biological processes.
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