Identification of biomarkers for the diagnosis and treatment of primary colorectal cancer based on microarray technology

BACKGROUND: Primary colorectal cancer (PCRC) is one of the most common malignant tumors in clinic, and is characterized by high heterogeneity occurring between tumors and intracellularly. Therefore, this study aimed to explore potential gene targets for the diagnosis and treatment of PCRC via bioinformatic technology. METHODS: Gene Expression Omnibus (GEO) was used to download the data used in this study. Differently expressed genes (DEGs) were identified with GEO2R, and the gene set enrichment analysis (GSEA) was implemented for enrichment analysis. Then, the researchers constructed a protein-protein interaction (PPI) network, a significant module, and a hub genes network. RESULTS: The GSE81558 dataset was downloaded, and a total of 97 DEGs were found. There were 23 up-regulated DEGs and 74 down-regulated DEGs in the PCRC samples, compared with the control group. The PPI network included a total of 42 nodes and 63 edges. One module network consisted of 11 nodes and 25 edges. Another module network consisted of 4 nodes and 6 edges. The hub genes network was created by cytoHubba using GCG, GUCA2B, CLCA4, ZG16, TMIGD1, GUCA2A, CHGA, PYY, SST, and MS4A12. CONCLUSIONS: Ten hub genes were found from the genomic samples of patients with PCRC and normal controls by bioinformatics analysis. The hub genes might provide novel ideas and evidence for the diagnosis and targeted therapy of PCRC.