Nangraj Asma Sindhoo, Selvaraj Gurudeeban, Kaliamurthi Satyavani, Kaushik Aman Chandra, Cho William C, Wei Dong Qing
The State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Center of Interdisciplinary Sciences-Computational Life Sciences, Henan University of Technology, Zhengzhou, China.
Front Pharmacol. 2020 Jul 31;11:881. doi: 10.3389/fphar.2020.00881. eCollection 2020.
Esophageal adenocarcinoma (EAC) is a deadly cancer with high mortality rate, especially in economically advanced countries, while Barrett's esophagus (BE) is reported to be a precursor that strongly increases the risk of EAC. Due to the complexity of these diseases, their molecular mechanisms have not been revealed clearly. This study aims to explore the gene signatures shared between BE and EAC based on integrated network analysis. We obtained EAC- and BE-associated microarray datasets GSE26886, GSE1420, GSE37200, and GSE37203 from the Gene Expression Omnibus and ArrayExpress using systematic meta-analysis. These data were accompanied by clinical data and RNAseq data from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to explore the relationship between gene sets and clinical traits as well as to discover the key relationships behind the co-expression modules. A differentially expressed gene-based protein-protein interaction (PPI) complex was used to extract hub genes through Cytoscape plugins. As a result, 403 DEGs were excavated, comprising 236 upregulated and 167 downregulated genes, which are involved in the cell cycle and replication pathways. Forty key genes were identified using modules of MCODE, CytoHubba, and CytoNCA with different algorithms. A dark-gray module with 207 genes was identified which having a high correlation with phenotype (gender) in the WGCNA. Furthermore, five shared hub gene signatures (SHGS), namely, pre-mRNA processing factor 4 (PRPF4), serine and arginine-rich splicing factor 1 (SRSF1), heterogeneous nuclear ribonucleoprotein M (HNRNPM), DExH-Box Helicase 9 (DHX9), and origin recognition complex subunit 2 (ORC2), were identified between BE and EAC. SHGS enrichment denotes that RNA metabolism and splicosomes play a key role in esophageal cancer development and progress. We conclude that the PPI complex and WGCNA co-expression network highlight the importance of phenotypic identifying hub gene signatures for BE and EAC.
食管腺癌（EAC）是一种死亡率很高的致命癌症，在经济发达国家尤为如此，而据报道巴雷特食管（BE）是一种会显著增加EAC风险的癌前病变。由于这些疾病的复杂性，它们的分子机制尚未完全明确。本研究旨在基于整合网络分析探索BE和EAC之间共有的基因特征。我们通过系统的荟萃分析从基因表达综合数据库（Gene Expression Omnibus）和ArrayExpress获取了与EAC和BE相关的微阵列数据集GSE26886、GSE1420、GSE37200和GSE37203。这些数据还伴有来自癌症基因组图谱（TCGA）的临床数据和RNA测序数据。进行了加权基因共表达网络分析（WGCNA）和差异表达基因（DEG）分析，以探索基因集与临床特征之间的关系，并发现共表达模块背后的关键关系。使用基于差异表达基因的蛋白质-蛋白质相互作用（PPI）复合体，通过Cytoscape插件提取枢纽基因。结果，挖掘出403个差异表达基因，包括236个上调基因和167个下调基因，它们参与细胞周期和复制途径。使用具有不同算法的MCODE、CytoHubba和CytoNCA模块鉴定出40个关键基因。在WGCNA中鉴定出一个包含207个基因的深灰色模块，该模块与表型（性别）具有高度相关性。此外，在BE和EAC之间鉴定出五个共享枢纽基因特征（SHGS），即前体mRNA加工因子4（PRPF4）、富含丝氨酸和精氨酸的剪接因子1（SRSF1）、不均一核核糖核蛋白M（HNRNPM）、解旋酶DHX9（DExH-Box Helicase 9）和复制起点识别复合物亚基2（ORC2）。SHGS富集表明RNA代谢和剪接体在食管癌的发生和发展中起关键作用。我们得出结论，PPI复合体和WGCNA共表达网络突出了表型识别BE和EAC枢纽基因特征的重要性。
