目的:寻找对胃癌的发生发展有驱动作用的基因和信号通路,为胃癌的靶向治疗提供可能的靶点。方法:从TCGA下载胃癌的RNA-Seq和DNA拷贝数变异数据,利用Perl脚本和R语言中的edgR包筛选出既差异表达且拷贝数发生变异的基因。将筛选出的基因进行基因功能富集分析和通路分析。再将富集过后的基因上传String数据库,生成蛋白质相互作用网络(PPI),进而从PPI网络中分离出结合度高的稠密子网络,最后结合MalaCards疾病数据库构建胃癌的核心网络,进而寻找因差异表达与基因拷贝数呈正相关的基因。结果:本文一共筛选出26个胃癌驱动基,以及PI3K-Akt信号通路、ECM-受体相互作用、Wnt信号通路和胃酸分泌等可能与胃癌的发生发展相关的信号通路,为胃癌的诊断和治疗提供潜在的靶点。 Aims: To find driving genes and signal pathways that cause the occurrence and development of gastric cancer, in order to provide possible targets for targeted therapy of gastric cancer. Methods: This paper downloads RNA-Seq and DNA copy number variation data of gastric cancer from TCGA, uses Perl script and the edgR package in R language to screen out genes that are differentially expressed and have copy number variation; then performs gene function enrichment analysis and pathway analysis of the selected genes; after then uploads the enriched genes to the String data-base to generate a protein interaction network (PPI), and then separates dense sub-networks with high binding degree from the PPI network. Finally, it combines with MalaCards disease database to construct the core network of gastric cancer, and then finds genes whose differential expression is positively correlated with gene copy number. Results: This paper screened a total of 26 gastric cancer driving groups, as well as PI3K-Akt signaling pathway, ECM-receptor interaction, Wnt signaling pathway, gastric acid secretion and other signaling pathways that may be related to the occurrence and development of gastric cancer to provide potential targets for the diagnosis and treatment of gastric cancer.
生物信息学,胃癌,靶向治疗,DNA拷贝数变异,PPI网络, Bioinformatical Gastric Cancer Targeted Therapy DNA Copy Number Variation Protein Protein Interaction Network摘要
Aims: To find driving genes and signal pathways that cause the occurrence and development of gastric cancer, in order to provide possible targets for targeted therapy of gastric cancer. Methods: This paper downloads RNA-Seq and DNA copy number variation data of gastric cancer from TCGA, uses Perl script and the edgR package in R language to screen out genes that are differentially expressed and have copy number variation; then performs gene function enrichment analysis and pathway analysis of the selected genes; after then uploads the enriched genes to the String database to generate a protein interaction network (PPI), and then separates dense sub-networks with high binding degree from the PPI network. Finally, it combines with MalaCards disease database to construct the core network of gastric cancer, and then finds genes whose differential expression is positively correlated with gene copy number. Results: This paper screened a total of 26 gastric cancer driving groups, as well as PI3K-Akt signaling pathway, ECM-receptor interaction, Wnt signaling pathway, gastric acid secretion and other signaling pathways that may be related to the occurrence and development of gastric cancer to provide potential targets for the diagnosis and treatment of gastric cancer.
Keywords:Bioinformatical, Gastric Cancer, Targeted Therapy, DNA Copy Number Variation, Protein Protein Interaction Network
癌症和肿瘤基因图谱(TCGA)计划是在2005年由美国国家癌症研究所和国家人类基因组研究所联合发起的一项旨在对人类全部肿瘤进行基因组测序,绘制基因变异图谱的研究计划,现已收录了34种肿瘤的11,315份组织标本多组学数据。本文的数据来源于TCGA官网(https://portal.gdc.cancer.gov),从中获取了32例正常组织样本和375例胃癌组织样本的基因表达数据(RNA-Seq),以及所有胃癌的DNA拷贝数变异数据(Copy Number Variation),其中包含464例正常样本,442例患癌样本。
杨正伟,张 娟. 整合多组学数据搜寻胃癌的驱动基因Integrating Multiple Omics Data to Search for Driver Genes for Gastric Cancer[J]. 计算机科学与应用, 2020, 10(10): 1820-1833. https://doi.org/10.12677/CSA.2020.1010192
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