Integrating multiple omics data for the discovery of potential Beclin-1 interactions in breast cancer
Integrating multiple omics data for the discovery of potential Beclin-1 interactions in breast cancer
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整合多个组学数据以发现乳腺癌中潜在的 Beclin-1 相互作用
DOI：
10.1039/c6mb00653a
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发表时间：
2017
影响因子：
--
通讯作者：
He Jun
中科院分区：
文献类型：
--
作者：
Chen Yi;Wang Xuan;Wang Guan;Li Zhaozhi;Wang Jinjin;Huang Lingyu;Qin Ziyi;Yuan Xiang;Cheng Zhong;Zhang Shu;Yin Yiqiong;He Jun
据报道，乳腺癌是最常见的恶性疾病之一，也是世界各地妇女癌症死亡的主要原因。此外，这种复杂的癌症分为多个亚型，这些亚型表现出不同的临床症状，需要相应的定向治疗。我们以BECN 1作为起点，BECN 1是自噬中发挥肿瘤抑制作用的核心基因。本文的研究旨在通过使用最小绝对收缩和选择算子（LASSO）整合多个组学数据来识别与乳腺癌及其多种亚型相关的基因，LASSO是一种可以整合两种以上组学数据的统计方法。所有数据均来自癌症基因组图谱（TCGA）平台，该平台存储了临床和分子肿瘤数据。该模型是基于三种数据，包括mRNA-基因表达的因变量水平，DNA甲基化和拷贝数的变化作为自变量。最后，我们提出了乳腺癌的四种亚型，并认为作为微阵列分析的结果，AFF 3与乳腺癌中的BECN 1相关，并且可能是潜在的治疗靶点。这一发现可能在基因水平上为乳腺癌的四种不同亚型提供一些潜在的靶向治疗方法。总之，找出Beclin-1在乳腺癌亚型中的主要作用具有重要价值。所获得的结果对进一步的研究具有指导意义，并可能在临床应用中提供优异的结果，以及在动物实验中的测试，也可能表明一种新的方法来进行生物信息学分析。
Breast cancer has been reported as one of the most frequently diagnosed malignant diseases and the leading cause of cancer death in women all around the world. Furthermore, this complicated cancer is divided into multiple subtypes which present different clinical symptoms and need correspondingly directed therapy. We took BECN1, a core gene in autophagy performing a tumor inhibitory effect, as a starting point. The study in this paper aims to identify genes related to breast cancer and its multiple subtypes by integrating multiple omics data using the least absolute shrinkage and selection operator (LASSO), which is a statistical method that can integrate more than two types of omics data. All the data is obtained from The Cancer Genome Atlas (TCGA) platform which stores clinical and molecular tumor data. The model constructed is based on three kinds of data including mRNA-gene expression with a dependent variable level, DNA methylation and copy number alterations as independent variables. Finally, we propose four subnets of four subtypes of breast cancer, and consider as a result of microarray analysis that AFF3 is associated with BECN1 in breast cancer, and may be a potential therapeutic target. This finding may provide some potential targeted therapeutics for the four different subtypes of breast cancer at the genetic level. In conclusion, finding out the major role Beclin-1 plays in breast cancer subtypes is of great value. The results obtained are instructive for further research and may provide excellent results in clinical applications, as well as testing in animal experiments, and may also indicate a new method to perform bioinformatics analysis.