Target gene screening and evaluation of prognostic values in non-small cell lung cancers by bioinformatics analysis

Background: Non-small cell lung cancer (NSCLC) is the major leading cause of cancer-related deaths worldwide. This study aims to explore molecular mechanism of NSCLC. Methods: Microarray dataset was obtained from the Gene Expression Omnibus (GEO) database, and analyzed by using GEO2R. Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Then, STRING, Cytoscape and MCODE were applied to construct the Protein-protein interaction (PPI) network and screen hub genes. Following, overall survival (OS) analysis of hub genes was performed by using the Kaplan-Meier plotter online tool. Moreover, miRecords was also applied to predict the targets of the differentially expressed microRNAs (DEMs). Results: A total of 228 DEGs were identified, and they were mainly enriched in the terms of cell adhesion molecules, leukocyte transendothelial migration and ECM-receptor interaction. A PPI network was constructed, and 16 hub genes were identified, including TEK, ANGPTI, MMP9, VWF, CDH5, EDN1, ESAM, CCNE1, CDC45, PRC1, CCNB2, AURKA, MELK, CDC20, TOP2A and PTTG1. Among the genes, expressions of 14 hub genes were associated with prognosis of NSCLC patients. Additionally, a total of 11 DEMs were also identified. Conclusion: Our results provide some potential underlying biomarkers for NSCLC. Further studies are required to elucidate the pathogenesis of NSCLC.