“School of Biological”
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| Paper IPM / Biological / 16598 |
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| Abstract: | |||||
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Cancer is a complex disease with a high rate of mortality. The characteristics of tumor masses are very heterogeneous; thus, the appropriate classiï¬cation of tumors is a critical point in the correct treatment. A high level of heterogeneity has also been observed in breast cancer. Therefore, detecting the molecular subtypes of this disease is a worthwhile issue for medicine that could be facilitated using bioinformatics. Method: Numerous methods have already classiï¬ed breast cancer based on gene expression data; however, they are not reliable due to the dynamic nature of these data. In contrast, gene mutation data are relatively stable and may lead to better classiï¬cation. The aim of this study is to introduce a novel method for detecting the molecular subtypes of breast cancer. In this study, somatic mutation proï¬les of tumors are used; nonetheless, the somatic mutation proï¬les are very sparse. To address this issue, we made use of the network propagation method on gene interaction network and made the mutation proï¬les dense. Afterward, we used deep embedded clustering (DEC) method to classify breast tumors into four subtypes. In the next step, gene signatures of each subtype obtained by Fisher exact test and Benjamini-Hochberg procedure.
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