Identification of candidate biomarkers and pathways in breast cancer by differential network analysis. (12th March 2021)

• Record Type: Journal Article
• Authors: Mendi, Onur; Karahoca, Adem
• Is Part Of: International journal of data mining and bioinformatics. Volume 24:Number 4(2020)
• Page Start: 344
• Page End: 367
• Publication Date: 2021-03-12
• Subjects: breast cancer -- differential network analysis -- bioinformatics -- microarray; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.