Identification of key genes associated with esophageal adenocarcinoma based on bioinformatics analysis.

ABSTRACT:

Background

Esophageal adenocarcinoma (EAC) is an aggressive malignancy and accounts for the majority of cancer-related death worldwide. It is often diagnosed at an advanced stage and entails a poor prognosis for those afflicted. The mechanisms of its pathogenesis and progress remain unclear and require urgent elucidation. This study aimed to identify specific genes and potential pathways associated with the progression and prognosis of EAC using bioinformatics analyses.

Methods

EAC microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs) using bioinformatics analysis. The DEGs in TCGA were then analyzed to construct a co-expression network by weighted correlation ne

SUBMITTER: Qi W 

PROVIDER: S-EPMC8743722 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature