Hub genes and pathways in gastric cancer: A comparison between studies that used normal tissues adjacent to the tumour and studies that used healthy tissues as calibrator

Several bioinformatics studies have been performed on high-throughput expression data to determine the cellular pathways and hub genes affected by Gastric cancer (GC). However, these studies differ in using a healthy tissue or normal tissue adjacent to the tumour (NAT) as calibrator tissues. This study was designed to find how using healthy or NAT tissues as calibrator tissues could affect pathway enrichment data and hub genes in GC. Two gene expression datasets with NAT tissues (GSE79973 and GSE118916) and one dataset with healthy tissues (GSE54129) were downloaded and processed by the limma package to screen the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were performed by the Enrichr online tool. Protein-protein interaction network construction, module analysis, and hub genes selection were performed by Cytoscape software, Molecular Complex Detection plugin, and cytoHubba plugin, respectively. The gene expression profiling interactive analysis web server was used to analyse RNA sequencing expression data from The Cancer Genome Atlas Program. The Kaplan-Meier plotter was used to perform survival analysis. Our results showed that some KEGG and GO pathways were shared between studies with NAT and the study with healthy tissues. However, some terms, especially inflammation-related terms, were missed when NAT tissues were used as calibrator tissues. Also, only FN1 and COL1A1 are common hub genes between DEGs of the studies with NAT and healthy tissues. Since hub genes are usually extracted and suggested as candidate targets for GC diagnosis, prognosis, or treatment, selecting healthy or NAT tissues may affect the hub genes selection.

Automated summary

The use of healthy or normal adjacent tissues as calibrator tissues can have an impact on pathway enrichment data and hub gene selection in Gastric cancer (GC). In this study, gene expression datasets containing normal adjacent tissues and healthy tissues were analyzed using various bioinformatics methods. The results showed that there were some shared pathways between the two types of tissues, but selecting normal adjacent tissues as calibrator tissues could miss some inflammation-related pathways.