Introduction: Hub genes related to the development of gastric cancer (GC) were identified based on bioinformatics methods. This study aimed to identify GC hub genes, explore the expression of genes in GC and their correlation with prognosis, so as to provide strategies for GC diagnosis and targeted therapy.
Methods: Two messenger RNA (mRNA) microarray datasets were downloaded from GEO database. These data were combined with TCGA database to obtain common DEGs between GC tissues and normal tissues. GO and KEGG pathway enrichment analysis was performed. Visualized PPI network analysis was performed by Cytoscape to further identify hub genes. GEPIA database was used to evaluate the prognostic value of hub genes. The online software Ualcan was applied to analyze the expression of the prognosis-related genes in cancer tissues and normal tissues from different perspectives of primary GC, TNM stage, nodal metastasis status and tumor grade. Immunohistochemical staining of GC tissues and normal tissues was performed to evaluate the expression of signature genes in GC.
Results: Eighty-four common differentially expressed genes (DEGs) in GC were identified. These genes were closely related to the P13K-Akt signal pathway and other signaling pathways. Ten hub genes were identified. Collagen type I alpha 1 (COL1A1) and collagen type IV alpha 1 (COL4A1) were significantly associated with poor prognosis of GC and were all positively correlated with T stage, distant metastasis, and TNM stage of GC. Immunohistochemistry revealed that the expression of these 2 genes was upregulated in GC tissues. These 2 genes expression was negatively related with 5-year survival rate of GC patients.
Conclusion: Ten highly expressed hub genes in GC tissue were mined by bioinformatics method. COL1A1 and COL4A1 were significantly associated with the prognosis of GC. This study provided a theoretical basis for the pathogenesis, clinical diagnosis and therapeutic targets of GC.
Keywords: gastric cancer, bioinformatics analysis, prognosis, differentially expressed genes, immunohistochemical