Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis

Lung adenocarcinoma (LUAD) accounts for the majority of cancer-related deaths worldwide. Our study identified key LUAD genes and their potential mechanism via bioinformatics analysis of public datasets. GSE10799, GSE40791, and GSE27262 microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database. The RobustRankAggreg package was used to perform a meta-analysis, and 50 upregulated genes and 87 downregulated genes overlapped in three datasets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Furthermore, protein-protein interaction (PPI) networks of the differentially expressed genes (DEGs) were built by the Search Tool for the Retrieval of Interacting Genes (STRING) and 22 core genes were identified by Molecular Complex Detection (MCODE) and visualized with Cytoscape. Subsequently, these core genes were analyzed by the Kaplan-Meier Plotter and Gene Expression Profiling Interactive Analysis (GEPIA). The results showed that all 22 genes were significantly associated with reduced survival rates. For GEPIA, the expression of only one gene was not significantly different between LUAD tissues and normal tissues. A KEGG pathway enrichment reanalysis of the 21 genes identified five key genes (CCNB1, BUB1B, CDC20, TTK, and MAD2L1) in the cell cycle pathway. Finally, the Comparative Toxicogenomics Database (CTD) website was used to explore the relationship between these key genes and certain drugs. Based on the bioinformatics analysis, five key genes were identified in LUAD, and drugs closely associated these genes can provide clues for the treatment and prognosis of LUAD.