Identification of potential key genes in esophageal adenocarcinoma using bioinformatics
Identification of potential key genes in esophageal adenocarcinoma using bioinformatics
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利用生物信息学鉴定食管腺癌的潜在关键基因
DOI：
10.3892/etm.2019.7973
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发表时间：
2019-11-01
影响因子：
2.7
通讯作者：
Xu, Shuchang
中科院分区：
文献类型：
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作者：
Dong, Zhiyu;Wang, Junwen;Xu, Shuchang
食管腺癌（EAC）是食管癌在欧洲和美国的主要病理亚型。本生物信息学研究分析了一个高通量测序数据集GSE94869，以确定差异表达基因（DEGs），以确定与EAC相关的关键基因、生物过程和途径。功能富集分析使用数据库进行标注可视化和集成发现。采用加权基因共表达网络分析法建立deg共表达网络，并用Cytoscape进行可视化。使用基于癌症基因组图谱（TCGA）数据库的Kaplan-Meier分析来鉴定预后相关基因。使用单因素和多因素Cox比例风险模型来鉴定与无复发生存（RFS）有预后价值的基因，同时使用基于TCGA和另一个微阵列数据集GSE26886的数据的箱形图来验证预后相关基因的差异表达。共鉴定出130个基因，包括82个上调基因和48个下调基因。上调的DEGs与细胞外基质组织、分解、磷酸肌醇-3激酶/AKT、Rap1和Ras信号通路显著相关，而下调的基因与Wnt信号通路相关。随后，建立了两个共表达模块，鉴定了20个枢纽基因。蓝色模块与Rap1信号通路相关，而绿松石模块与Ras和Rap1信号通路相关。其中，甲基转移酶如7B （METTL7B）与RFS相关。此外，利用TCGA和GSE26886的数据成功验证了METTL7B在EAC中的过表达。本研究确定了EAC的关键基因，并为EAC的诊断和治疗提供了潜在的生物标志物。
Esophageal adenocarcinoma (EAC) is the predominant pathological subtype of esophageal cancer in Europe and the USA. The present bioinformatics study analyzed a high-throughput sequencing dataset, GSE94869, to determine differentially expressed genes (DEGs) in order to identify key genes, biological processes and pathways associated with EAC. Functional enrichment analysis was performed using the Database for Annotation Visualization and Integrated Discovery. The co-expression network of the DEGs was established using Weighted Gene Co-Expression Network Analysis and visualized using Cytoscape. A Kaplan-Meier analysis based on The Cancer Genome Atlas (TCGA) database was used to identify prognosis-associated genes. Univariate and multivariate Cox proportional hazard models were used to identify genes with a prognostic value regarding relapse-free survival (RFS), while validation of the differential expression of prognosis-associated genes was performed using a box plot based on data from TCGA and another microarray dataset, GSE26886. A total of 130 DEGs, comprising 82 upregulated and 48 downregulated genes, were identified. The upregulated DEGs were significantly associated with extracellular matrix organization, disassembly, and the phosphoinositide-3 kinase/AKT, Rap1 and Ras signaling pathways, while the downregulated genes were associated with the Wnt signalling pathway. Subsequently, two co-expression modules were established and 20 hub genes were identified. The blue module was associated with the Rap1 signaling pathway, while the turquoise module was associated with the Ras and Rap1 signaling pathways. Among them, methyltransferase like 7B (METTL7B) was associated with RFS. Furthermore, the overexpression of METTL7B in EAC was successfully validated using data from TCGA and GSE26886. The present study identified key genes and provides potential biomarkers for the diagnosis and treatment of EAC.