Screening of hub genes associated with prognosis in non-small cell lung cancer by integrated bioinformatics analysis

BACKGROUND: Lung cancer is an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. This study conducted a bioinformatics analysis to identify critical genes associated with poor prognosis in non-small cell lung cancer (NSCLC). METHODS: We downloaded three datasets (GSE33532, GSE27262, and GSE18842) from the gene expression omnibus (GEO), and used the GEO2R online tools to identify the differentially expressed genes (DEGs). We then used the Search Tool for Retrieval of Interacting Genes (STRING) database to establish a protein-protein interaction (PPI) network and used the Cytoscape software to perform a module analysis of the PPI network. A Kaplan-Meier plotter was used to perform the overall survival (OS) analysis, and the Gene Expression Profiling Interactive Analysis (GEPIA) database was used for expression level analysis of hub genes. Further, the UALCAN database was used to validate the relationship between the gene expression level of each hub gene and clinical characteristics. RESULTS: We identified 254 DEGs, which were composed of 66 up-regulated genes and 188 down-regulated genes. Out of these, five DEGs were identified as hub genes (CDC20, BUB1, CCNB2, CCNB1, UBE2C) by constructing a PPI network. The use of a Kaplan-Meier plotter to generate patient survival curves suggested a strong relationship between the five hub genes with worse OS. Validation of the above results using the GEPIA database showed that all the hub genes were highly expressed in NSCLC tissues. Using the UALACN data mining platform, we found that the five hub genes are correlated with tumor stage and the status of node metastasis in NSCLC patients. CONCLUSIONS: We identified five hub DEGs that might provide perspectives in the explorations of pathogenesis and treatments for NSCLC.