Identification of potential key genes in esophageal adenocarcinoma using bioinformatics

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.

METTL7B is a novel prognostic biomarker of lower-grade glioma based on pan-cancer analysis

“…Compared with normal tissues, METTL7B was reported to be expressed at high levels in ESCA [ 7 ], LUAD [ 5 ], and THCA [ 4 ]. By analyzing 27 cancer datasets, we found that METTL7B expression was upregulated in 22 tumors, including ESCA, LUAD and THCA, consistent with previous reports.…”

“…METTL7B is involved in infections, hepatitis and obstetric diseases [1][2][3]. METTL7B has also been proven to be overexpressed in multiple malignancies [e.g., papillary thyroid cancer (PTC), lung cancer, and esophageal adenocarcinoma], while it is downregulated in breast cancer [4][5][6][7]. Through different mechanisms, METTL7B plays a vital regulatory role in multiple tumors.…”

Human METTL7B is an alkyl thiol methyltransferase that metabolizes hydrogen sulfide and captopril

“…Despite an unknown function, METTL7B has surfaced in several genetic studies linking its upregulation to multiple disease states 24 , 25 . Specifically, METTL7B expression is significantly altered in kidney disease, acute respiratory distress syndrome, and numerous cancers, including breast, non-small cell lung, thyroid, and ovarian 24 – 30 .…”

Study on the Selection of the Targets of Esophageal Carcinoma and Interventions of Ginsenosides Based on Network Pharmacology and Bioinformatics

“…In this study, we also integrate molecular docking technology, which can predict the binding degree of small molecules to targets and facilitate the screening of active components in Chinese medicines. The screening of prognostic genes for ESCA has been addressed in previous studies [ 28 ]. On the contrary, this study combines network pharmacology with bioinformatics to screen four ESCA targets.…”