Identification of candidate biomarkers for gastric cancer by bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus.

467 Background: Gastric cancer (GC) remains one of the most prevalent malignant diseases worldwide. The high mortality rate is largely due to the limited treatment options and the challenges in diagnosing the cancer at an early stage. Understanding the genetic differences driving the proliferation of GC is crucial for advancing diagnostic methods and developing targeted treatments. This study aims to identify biomarkers involved in the pathogenesis of GC through integrated bioinformatics analysis. Methods: Extracted from the Geo Expression Omnibus, three DataSets (GSE118916, GSE79973, and GSE13861) were analyzed for differentially expressed genes (DEGs) using the R statistical language. The three data sets were then compared for common DEGs, which would serve as the focus of this study. These DEGs were functionally analyzed for pathways by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein–protein interaction (PPI) network was constructed to screen out candidate genes. Results: The results revealed that 123 DEGs of the three datasets containing 26 upregulated genes and 97 downregulated genes were ascertained in our study. The GO and KEGG enrichment analysis results showed that the functions of DEGs were mainly involved in the retinol metabolic process, xenobiotic metabolic process, collagen type I trimer, potassium: proton exchanging ATPase complex, benzaldehyde dehydrogenase (NAD+) activity, alcohol dehydrogenase (NADP+) activity, Collecting duct acid secretion, and Tyrosine metabolism. Through the PPI analysis network, the core genes with the highest degree of 7 nodes were selected: COL1A1, COL1A2, COL11A1, THBS2, ATP4A, COL10A1, and CXCL8. Conclusions: The 7 genes identified in this study may play a vital regulatory role in the occurrence and development of GC and may also be potential therapeutic targets. Further mechanistic studies of the pathogenesis and treatment of GC may be able to identify new targets using these shared pathways. These findings could contribute to a better understanding of the origins of gastric cancer and facilitate the development of early interventions. The bioinformatics analysis conducted in this study offers new insights and directions for further research on gastric cancer.