Identification of genes and pathways in esophageal adenocarcinoma using bioinformatics analysis

He,Feng; Ai,Bo; Tian,Lei

doi:10.3892/br.2018.1134

Identification of genes and pathways in esophageal adenocarcinoma using bioinformatics analysis

Authors:
- Feng He
- Bo Ai
- Lei Tian
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Affiliations: Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China, Department of Clinical Laboratory, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P.R. China
Published online on: July 25, 2018 https://doi.org/10.3892/br.2018.1134
Pages: 305-312
Copyright: © He et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Esophageal adenocarcinoma (EAC) is one of the most common subtypes of esophageal cancer, and is associated with a low 5-year survival rate. The present study aimed to identify key genes and pathways associated with EAC using bioinformatics analysis. The gene expression profiles of GSE92396, which includes 12 EAC samples and 9 normal esophageal samples, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the EAC and normal samples were identified using the limma package in R language. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the identified DEGs were conducted using the online analysis tool, the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Finally, module analysis was conducted for the PPI network using the MCODE plug-in in Cytoscape. Of the 386 DEGs identified, the 150 upregulated genes were mainly enriched in the KEGG pathways of complement and coagulation cascades, maturity onset diabetes of the young and protein digestion and absorption; and the 236 downregulated genes were mainly enriched in amoebiasis, retinol metabolism and drug metabolism-cytochrome P450. Based on information from the STRING database, a PPI network comprising of 369 nodes and 534 edges was constructed in Cytoscape. The top 10 hub nodes with the highest degrees were determined as interleukin-8, involucrin, tissue inhibitor of metalloproteinase 1, fibronectin 1, serpin family E member 1, serpin family A member 1, cystic fibrosis transmembrane conductance regulator, secreted phosphoprotein 1, collagen type I alpha 1 chain and angiotensinogen. A total of 6 modules were detected from the PPI network that satisfied the criteria of MCODE score >4 and number of nodes >4. KEGG pathways enriched for the module DEGs were mainly within arachidonic acid metabolism, complement and coagulation cascades and rheumatoid arthritis. In conclusion, identification of these key genes and pathways may improve understanding of the mechanisms underlying the development of EAC, and may be used as diagnostic and therapeutic targets in EAC.

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