Identification of potential key genes in gastric cancer using bioinformatics analysis

Abstract

Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: 'extracellular matrix organization', 'collagen catabolic process' and 'cell adhesion'. These three KEGG pathways were significantly enriched in the categories: 'ECM-receptor interaction', 'protein digestion and absorption', and 'focal adhesion'. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

Keywords: bioinformatics analysis; diagnosis; differentially expressed genes; gastric cancer; key genes.

Figure 1.

Venn diagram of shared differentially expressed genes. (A) Upregulated and (B) downregulated genes from four gene expression profiles.

Figure 2.

Gene Ontology terms and KEGG pathway enrichment analyses of 271 differentially expressed genes. Top 10 terms of enrichment for (A) BP, (B) CC and (C) MF. (D) Top 10 enriched KEGG pathways. KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function.

Figure 3.

Protein-protein interaction network of differentially expressed genes. Red indicates upregulated genes, and green represents downregulated genes.

Figure 4.

mRNA expression of the six key genes in 20 different types of cancer. Cell color is determined by the best gene rank percentile for the analyses within the cell.

Figure 5.

Expression of six key genes in different gastric cancer gene chips in Oncomine. P<0.0001 and a |fold change|>2 were used as the threshold. Comparison of mRNA expression in cancerous vs. normal gastric tissue. (A) FN1, (B) COL1A1, (C) INHBA, (D) CST1, (E) ATP4A and (F) ATP4B.

Figure 6.

Meta-analyses of the six key genes in gastric cancer in Oncomine. (A) FN1, (B) COL1A1, (C) INHBA, (D) CST1, (E) ATP4A and (F) ATP4B.

Figure 7.

Kaplan-Meier overall survival analyses of patients with gastric cancer based on expression of the six key genes. (A) FN1, (B) COL1A1, (C) INHBA, (D) CST1, (E) ATP4A, (F) ATP4B. HR, hazard ratio.