Title: Identification of candidate biomarkers and pathways in breast cancer by differential network analysis

Authors: Onur Mendi; Adem Karahoca

Addresses: Department of Bioinformatics, Faculty of Medicine, Demiroglu Bilim University, Istanbul, Turkey ' Department of Computer Engineering, Faculty of Engineering, MEF University, Istanbul, Turkey

Abstract: Breast cancer is one of the most malignant cancers in women worldwide. The aim of the present study was to explore the underlying biological mechanisms of breast cancer. For this purpose, we propose a novel framework to reveal mechanisms that drive disease progression in breast cancer by combining prior knowledge in the literature with differential networking methodology. Our integration framework has resulted in the most important genes and interactions by allowing ranking the breast cancer-specific gene network. YY1, SMARCA5, FOXM1, STAT4 and PTTG1 were found to be the most important genes in breast cancer. Functional and pathway enrichment analyses identified numerous pathways that may play a critical role in disease progression. Considering the success of the comparison of the results with the literature, the systemic lupus erythematosus pathway may be a potential target of breast cancer.

Keywords: breast cancer; differential network analysis; bioinformatics; microarray.

DOI: 10.1504/IJDMB.2020.113697

International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.4, pp.344 - 367

Accepted: 18 Jan 2021
Published online: 18 Mar 2021 *

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