通过整合DNA甲基化、拷贝数变异和基因表达数据鉴定癌症功能异常子通路
子途径定义为具有特定生物学功能的生物途径的局部区域。随着大规模测序数据的产生,有更多的机会来研究癌症发展的分子机制。有必要研究DNA甲基化,拷贝数变异(CNV)和基因表达变化对致癌功能障碍亚通路分子状态的潜在影响。我们提出了一种新方法,即通过整合多组学数据和途径拓扑信息来识别功能障碍性亚途径,来鉴定癌症功能障碍性亚途径(ICDS)。我们首先通过整合以下三种数据类型来计算基因风险得分:DNA甲基化,CNV和基因表达。其次,我们执行了贪婪搜索算法,以识别判别分数局部最高的路径内的关键功能障碍子路径。最后,使用置换检验来计算这些关键功能障碍子途径的统计学显着性水平。我们使用来自肝细胞肝癌(LIHC),头颈鳞状细胞癌(HNSC),子宫颈鳞状细胞癌和宫颈内腺癌的数据集验证了ICDS在识别失调的子路径中的有效性。我们进一步将ICDS与执行相同子路径识别算法但仅考虑DNA甲基化,CNV或基因表达(分别定义为ICDS_M,ICDS_CNV或ICDS_G)的方法进行了比较。通过这些分析,我们证实了ICDS比其他仅考虑一种数据类型的其他三种方法能更好地识别与癌症相关的子途径。我们的ICDS方法已实现为可免费使用的基于R的工具( https://cran.r-project.org / web / packages / ICDS )。**
Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data
A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS).
pmid: 31156704 Front Genet 影响因子: 3.517 发表日期: 20190101 官网 免费下载
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