Identification of Hub Genes and Potential Pathogenesis in Gastric Cancer Based on Integrated Gene Expression Profile Analysis

Objective: Gastric cancer (GC) is one of the most common malignancies and ranks third in terms of cancer-related mortality. This study aims to identify the hub genes and potential mechanisms in GC using a bioinformatics approach. Methods: Microarray data GSE54129, GSE79973, GSE55696 were extracted from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) was identified using Benjamini-Hochberg method in the limma package. GO and KEGG pathway enrichment analyses of the DEGs were conducted. Furthermore, protein–protein interaction network was constructed the STRING platform, and the hub genes were discovered using Maximal Clique Centrality method via cytoHubba. The predictive significance of hub genes was evaluated through GSE15459 dataset. Results: A total of 73 genes was identified as DEGs in GC. Volcano plots and heatmaps of DEGs were visualized. Functional enrichment analysis revealed that the genes were mostly enriched in response to xenobiotic stimulus, digestion, cellular hormone metabolic process, extracellular matrix structural constituent, calcium-dependent cysteine-type endopeptidase activity, aromatase activity, apical part of cell, basal part of cell, and apical plasma membrane. Regarding KEGG pathway-enrichment, the genes were mainly involved in Drug metabolism-cytochrome P450, Retinol metabolism, Chemical carcinogenesis-DNA adducts, Gastric acid secretion, and Metabolism of xenobiotics by cytochrome P450. By combining the results of Cytohubba, the top five intersecting genes identified were SPP1, INHBA, MMP7, THBS2 and FAP. Kapplan-Meier analysis results showed that these 5 hub genes were highly related to the overall survival of patients. Conclusion: SPP1, INHBA, MMP7, THBS2, and FAP were identified as prospective biomarkers and therapeutic targets for GC that might be utilized for prognostic evaluation and scheme selection.