Open Access

Identification of potential key genes in esophageal adenocarcinoma using bioinformatics

  • Authors:
    • Zhiyu Dong
    • Junwen Wang
    • Haiqin Zhang
    • Tingting Zhan
    • Ying Chen
    • Shuchang Xu
  • View Affiliations

  • Published online on: September 5, 2019     https://doi.org/10.3892/etm.2019.7973
  • Pages: 3291-3298
  • Copyright: © Dong 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 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.
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November-2019
Volume 18 Issue 5

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Spandidos Publications style
Dong Z, Wang J, Zhang H, Zhan T, Chen Y and Xu S: Identification of potential key genes in esophageal adenocarcinoma using bioinformatics. Exp Ther Med 18: 3291-3298, 2019
APA
Dong, Z., Wang, J., Zhang, H., Zhan, T., Chen, Y., & Xu, S. (2019). Identification of potential key genes in esophageal adenocarcinoma using bioinformatics. Experimental and Therapeutic Medicine, 18, 3291-3298. https://doi.org/10.3892/etm.2019.7973
MLA
Dong, Z., Wang, J., Zhang, H., Zhan, T., Chen, Y., Xu, S."Identification of potential key genes in esophageal adenocarcinoma using bioinformatics". Experimental and Therapeutic Medicine 18.5 (2019): 3291-3298.
Chicago
Dong, Z., Wang, J., Zhang, H., Zhan, T., Chen, Y., Xu, S."Identification of potential key genes in esophageal adenocarcinoma using bioinformatics". Experimental and Therapeutic Medicine 18, no. 5 (2019): 3291-3298. https://doi.org/10.3892/etm.2019.7973