ABSTRACT
@# To indentify the candidate genes and signaling pathways in lung adenocarcinoma by analyzing gene profiles with bioinformatics. Methods: The expression profiles of GSE40791, GSE68571, GSE43458, and GSE18842 were down-loaded from the Gene Expression Omnibus (GEO) database. The four microarray datasets were integrated to obtain the differentially expressed genes related to lung adenocarcinoma. STRING database was used to construct the protein-protein interaction (PPI) network of differentially expressed genes, and to further explore the gene modules and the key genes. DAVID was used to perform the gene enrichment analysis of each gene module, and to explore the regulatory function of each gene module in adenocarcinoma cells, as well as the relationship between the key genes in the module and the prognosis of the patients. Results: Thirty-seven up-regulated genes and 120 down-regulated genes were obtained from the primary screen, and the protein-protein interaction(PPI) network was successfully constructed. According to MCODE algorithm, we constructed gene modules and calculated the core genes (KIF14, SEPP1, SPP1, RBP4) in the PPI network. Finally, four modules were proved to be involved in regulation of cell cycle, blood coagulation, cell adhesion and cell metabolism, and four key genes were proved to be differentially expressed between lung adenocarcinoma tissues and normal tissues (all P<0.05). Survival analysis showed that expressions of KIF14, SEPP1 and SPP1 had significant effect on the prognosis of lung adenocarcinoma (P<0.01 or P<0.05), while RBP4 exerted insignificant difference in the survival rate of lung adenocarcinoma patients (P>0.05). Conclusion: With bioinformatics, three differentially expressed genes between lung adenocarcinoma tissues and normal adjacent tissues were finally screened out and proved to be closely related to the prognosis of patients, which provided new thoughts in the diagnosis and prognosis prediction of lung adenocarcinoma and improved the study efficiency on the mechanism of lung adenocarcinoma.