Identification of Crucial Genes Correlated with Esophageal Cancer by Integrated High-throughput Data Analysis

Overview

Journal Medicine (Baltimore)

Specialty General Medicine

Date 2020 May 24

PMID 32443386

Citations 10

Authors

Wei Zhou

Jiarui Wu

Xinkui Liu

Mengwei Ni

Ziqi Meng

Shuyu Liu

Shanshan Jia

Jingyuan Zhang

Siyu Guo

Xiaomeng Zhang

Affiliations

Soon will be listed here.

Abstract

Background: Esophageal cancer (ESCA) is one of the most deadly malignancies in the world. Although the management and treatment of patients with ESCA have improved, the overall 5-year survival rate is still very poor.

Methods: The study aimed to identify potential key genes associated with the pathogenesis and prognosis of ESCA. In the study, integrated bioinformatics methods were used to screen differentially expressed genes (DEGs) between ESCA and normal tissue in the data set of gene expression profiles. The hub gene in DEGs was further analyzed by protein-protein interaction (PPI) network and survival analysis to explore its relationship with the pathogenesis and poor prognosis of ESCA.

Results: 134 up-regulated genes and 183 down-regulated genes were obtained in ESCA compared with normal tissues. Moreover, the PPI network was established with 176 nodes and 800 interactions. Ten hub genes (AURKA, CDC20, BUB1, TOP2A, ASPM, DLGAP5, TPX2, CENPF, UBE2C, and NEK2) were filtered out based on the degree value. Functional enrichment analysis indicated that a variety of extracellular related items and ECM-receptor interaction pathway were all correlated with the ESCA.

Conclusions: The results of this study would provide some guidance for further study of diagnostic and prognostic biomarkers to promote ESCA treatment.

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