Identification and comparison of aberrant key regulatory networks in breast, colon, liver, lung, and stomach cancers through methylome database analysis.
Identification and comparison of aberrant key regulatory networks in breast, colon, liver, lung, and stomach cancers through methylome database analysis.
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DOI：
10.1371/journal.pone.0097818
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Kim SJ
中科院分区：
文献类型：
--
作者：
Kim B;Kang S;Jeong G;Park SB;Kim SJ
在启动子的特定CpG位点的异常甲基化是广泛负责各种癌症类型的发生和发展。尽管基于微阵列的甲基化组分析技术有助于在全基因组水平上阐明甲基化变化，但通过比较分析识别在高度发病的癌症中常见的关键甲基化标记或顶部调控网络仍然有限。在这项研究中，我们在计算机上对五种组织的每10组正常和癌症对进行了全基因组甲基化分析：乳腺，结肠，肝脏，肺和胃。甲基化阵列覆盖27，578个CpG位点，对应于14，495个基因，并且收集了癌症中显著高甲基化或低甲基化的基因（FDR调整的p值<0.05;甲基化差异>0.3）。对数据集的分析证实了先前已知的甲基化标志物的甲基化，并进一步鉴定了新的甲基化标志物，如GPX 2、CLDN 15和KL。使用甲基化组数据集的聚类分析产生具有区分癌细胞与正常细胞的二分模式的图，而不管组织类型。分析进一步显示，乳腺癌与肺癌最接近，而与结肠癌最远。通路分析表明，在所有五种癌症中，“癌症”相关网络或“癌症”相关生物功能表现为最高置信度，而每种癌症类型代表其组织特异性基因集。我们的研究结果有助于了解参与癌变的重要异常表观遗传途径。此外，新的甲基化标记物可用于建立癌症预后的标记物。
Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the “cancer” related network or the “cancer” related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.
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影响因子：
3.8
作者：
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