Comparison of gene expression profiles in healthy individuals and people with lung cancer using the meta-analysis method.

Abstract

Advances in the early detection of cancer are a way for treatment to be effective. However, due to the inability of early diagnose and treatment for lung cancer, the death rate of this type of cancer is high. Usually, a high percentage of patients are diagnosed at a stage where they are not able to receive treatment. The purpose of this study was the comparison of gene expression profiles in healthy individuals and people with lung cancer. The raw data sets GSE10072 and GSE19804 were taken from the GEO online database. Differentially expressed genes (DEGs) were identified between the non-tumor and tumor tissue samples using a meta-analysis investigation. Then, gene ontology and biological pathway analysis were performed with the Enrichr online server. The protein-protein interaction network of genes obtained from the meta-analysis investigation was drawn and analyzed using the String Online database and Cytoscape Software. Meta-analysis results showed a total of 515 differentially expressed genes. The results of the functional processes and biological pathway revealed that differentially expressed genes were mainly enriched in positive regulation of cell differentiation, regulation of cell population proliferation, regulation of epithelial cell differentiation, positive regulation of epithelial cell proliferation, response to growth factor, defense response to the tumor cell, cellular response to UV, regulation of cell cycle process, cell adhesion molecules, PPAR signaling pathway, TNF signaling pathway, ECM-receptor interaction, p53 signaling pathway, PI3K-Akt signaling pathway, and Cell cycle. Finally, key genes related to lung cancer, including IL6, MMP9, VWF, PECAM1, FOS, and CAV1 were identified. In conclusion, comparisons of gene expression profiles in healthy individuals and people with lung cancer identified some key genes that can act as lung cancer markers and can be used to predict new findings on cancer. These genes can play an important role in diagnosis and early cancer treatment.