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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Information theoretic sub-network mining characterizes breast cancer subtypes in terms of cancer core mechanisms
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Information theoretic sub-network mining characterizes breast cancer subtypes in terms of cancer core mechanisms

机译：信息理论亚网络挖掘在癌症核心机制方面表征乳腺癌亚型

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A breast cancer subtype classification scheme, PAM50, based on genetic information is widely accepted for clinical applications. On the other hands, experimental cancer biology studies have been successful in revealing the mechanisms of breast cancer and now the hallmarks of cancer have been determined to explain the core mechanisms of tumorigenesis. Thus, it is important to understand how the breast cancer subtypes are related to the cancer core mechanisms, but multiple studies are yet to address the hallmarks of breast cancer subtypes. Therefore, a new approach that can explain the differences among breast cancer subtypes in terms of cancer hallmarks is needed.We developed an information theoretic sub-network mining algorithm, differentially expressed sub-network and pathway analysis (DeSPA), that retrieves tumor-related genes by mining a gene regulatory network (GRN) of transcription factors and miRNAs. With extensive experiments of the cancer genome atlas (TCGA) breast cancer sequencing data, we showed that our approach was able to select genes that belong to cancer core pathways such as DNA replication, cell cycle, p53 pathways while keeping the accuracy of breast cancer subtype classification comparable to that of PAM50. In addition, our method produces a regulatory network of TF, miRNA, and their target genes that distinguish breast cancer subtypes, which is confirmed by experimental studies in the literature.
机译：基于遗传信息的乳腺癌亚型分类方案PAM50在临床应用中被广泛接受。另一方面，实验性癌症生物学研究已经成功地揭示了乳腺癌的发病机制，现在癌症的特征已经被确定来解释肿瘤发生的核心机制。因此，了解乳腺癌亚型与癌症核心机制之间的关系很重要，但多项研究尚未解决乳腺癌亚型的特征。因此，需要一种新的方法来解释乳腺癌亚型在癌症特征方面的差异。我们开发了一种信息论子网络挖掘算法，即差异表达子网络和通路分析（Dispa），该算法通过挖掘转录因子和miRNA的基因调控网络（GRN）来检索肿瘤相关基因。通过对癌症基因组图谱（TCGA）乳腺癌测序数据的广泛实验，我们表明，我们的方法能够选择属于癌症核心途径的基因，如DNA复制、细胞周期、p53途径，同时保持乳腺癌亚型分类的准确性与PAM50相当。此外，我们的方法产生了一个TF、miRNA及其靶基因的调节网络，用于区分乳腺癌亚型，这已被文献中的实验研究所证实。

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