Identification of genes and pathways in esophageal adenocarcinoma using bioinformatics analysis

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.

Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli

“…Here we investigated RNA-Seq data, consisting of 4,240 non-zero expressions, of anaerobic E. Coli in a bioreactor where readings were taken at 0, 0.5, 1, 2, 5 and 10 min after air supply was initiated. Unlike other approaches that typically investigated arbitrarily selected differential fold changing genes, and performed gene ontology enrichment analysis [41][42][43] , here we undertook multi-dimensional statistical approaches, in view of a dynamical system approach, where we queried the portion of transcriptome that are able to track the global transcriptome-wide attractor response. In other words, we investigated the spectrum of genes that are responsible for shaping the AAT.…”

Esophageal Cancer Development: Crucial Clues Arising from the Extracellular Matrix

“…In fact, these observations sound very interesting, and the association between inflammatory process and EC development has emerged as a key event along disease development [39,135]. Furthermore, in silico analysis identified genes and signaling pathways potentially involved with EAC genesis and development, with it being detected that fibronectin 1 may represent a major target associated with metastatic capacity [136]. Indeed, fibronectin is upregulated in EAC [137], but nevertheless its contribution to cell invasion and migration is still under investigation.…”

Integrated Analysis of Hub Genes and Pathways In Esophageal Carcinoma Based on NCBI’s Gene Expression Omnibus (GEO) Database: A Bioinformatics Analysis

“…More crucially, it can provide novel insight leading to early diagnosis, definitive treatment, and survival prediction [ 12 ]. Previous research in the bioinformatics field has yielded some achievements in ESCA knowledge, such as identification of associated genes and pathways and altered methylation associated with ESCA pathology [ 13 , 14 ]. However, analytical ability for ESCA has been limited due to insufficient sample size and a lack of in-depth evaluation of key genes which play a dominant role in the malignant development of ESCA.…”