alexa The Detection of Extremely High and Low Expressed Genes
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

The Detection of Extremely High and Low Expressed Genes by EGEF Algorithm in Invasive Breast Cancer

Senol Dogan1*, Amina Kurtovic-Kozaric1,2 and Gunay Karli3

1Genetics and Bioengineering Department, International Burch University, Sarajevo, Bosnia and Herzegovina

2Clinical Pathology and University Clinical Center, Sarajevo University, Sarajevo, Bosnia and Herzegovina

3Information Technologies Department, International Burch University, Sarajevo, Bosnia and Herzegovina

*Corresponding Author:
Senol Dogan
Genetics and Bioengineering Department
International Burch University, Sarajevo
Bosnia and Herzegovina
Tel: +387 33 944 400
E-mail: [email protected]

Received Date: February 01, 2016; Accepted Date: February 27, 2016; Published Date: March 07, 2016

Citation: Dogan S, Kurtovic-Kozaric A, Karli G (2016) The Detection of Extremely High and Low Expressed Genes by EGEF Algorithm in Invasive Breast Cancer. J Biom Biostat 7: 286. doi:10.4172/2155-6180.1000286

Copyright: © 2016 Dogan S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

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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