The present study aimed to identify genes with a differential pattern of expression in gastric cancer (GC), and to find novel molecular biomarkers for GC diagnosis and therapeutic treatment. The gene expression profile of GSE19826, including 12 GC samples and 15 normal controls, was downloaded from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) were screened in the GC samples compared with the normal controls. Two-way hierarchical clustering of DEGs was performed to distinguish the normal controls from the GC samples. The co-expression coefficient was analyzed among the DEGs using the data from COXPRESdb. The gene co-expression network was constructed based on the DEGs using Cytoscape software, and modules in the network were analyzed by ClusterOne and Bingo. Furthermore, enrichment analysis of the DEGs in the modules was performed using the Database for Annotation, Visualization and Integrated Discovery. In total, 596 DEGs in the GC samples and 57 co-expression gene pairs were identified. A total of 7 genes were enriched in the same module, for which the function was phosphate transport and which was annotated to participate in the extracellular matrix-receptor interaction pathway. These genes were collagen, type VI, α3 (COL6A3), COL1A2, COL1A1, COL5A2, thrombospondin 2, COL11A1 and COL5A1. Overall, the present study identified several biomarkers for GC using the gene expression profiling of human GC samples. The COL family is a promising prognostic marker for GC. Gene expression products represent candidate biomarkers endowed with great potential for the early screening and therapy of GC patients.
Keywords: co-expression network; differentially-expressed genes; function analysis; gastric cancer; pathway analysis.