Lu Jennifer, Wilfred Premila, Korbie Darren, Trau Matt
Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia.
Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, QLD 4072, Australia.
Cancers (Basel). 2020 Oct 30;12(11):3199. doi: 10.3390/cancers12113199.
Disruption of signaling pathways that plays a role in the normal development and cellular homeostasis may lead to the dysregulation of cellular signaling and bring about the onset of different diseases, including cancer. In addition to genetic aberrations, DNA methylation also acts as an epigenetic modifier to drive the onset and progression of cancer by mediating the reversible transcription of related genes. Although the role of DNA methylation as an alternative driver of carcinogenesis has been well-established, the global effects of DNA methylation on oncogenic signaling pathways and the presentation of cancer is only emerging. In this article, we introduced a differential methylation parsing pipeline (MethylMine) which mined for epigenetic biomarkers based on feature selection. This pipeline was used to mine for biomarkers, which presented a substantial difference in methylation between the tumor and the matching normal tissue samples. Combined with the Data Integration Analysis for Biomarker discovery (DIABLO) framework for machine learning and multi-omic analysis, we revisited the TCGA DNA methylation and RNA-Seq datasets for breast, colorectal, lung, and prostate cancer, and identified differentially methylated genes within the NRF2-KEAP1/PI3K oncogenic pathway, which regulates the expression of cytoprotective genes, that serve as potential therapeutic targets to treat different cancers.
在正常发育和细胞稳态中发挥作用的信号通路的破坏可能导致细胞信号失调,并引发包括癌症在内的各种疾病。除了基因畸变外,DNA甲基化还作为一种表观遗传修饰因子,通过介导相关基因的可逆转录来驱动癌症的发生和发展。尽管DNA甲基化作为癌症发生的另一种驱动因素的作用已得到充分证实,但DNA甲基化对致癌信号通路和癌症表现的整体影响才刚刚显现。在本文中,我们介绍了一种基于特征选择挖掘表观遗传生物标志物的差异甲基化解析流程(MethylMine)。该流程用于挖掘在肿瘤与匹配的正常组织样本之间甲基化存在显著差异的生物标志物。结合用于机器学习和多组学分析的生物标志物发现数据整合分析(DIABLO)框架,我们重新审视了TCGA中乳腺癌、结直肠癌、肺癌和前列腺癌的DNA甲基化和RNA测序数据集,并在NRF2-KEAP1/PI3K致癌通路中鉴定出差异甲基化基因,该通路调节细胞保护基因的表达,这些基因可作为治疗不同癌症的潜在治疗靶点。