Identification of significant genes in non-small cell lung cancer by bioinformatics analyses

Lung cancer is the most malignant cancer featured with undesirable prognosis. It is urgent to identify novel biomarkers to improve both diagnosis and prognosis. The purpose of the study was to identify significant genes involved in lung cancer through bioinformatic methods and reveal potential underlying mechanisms.

Three datasets GSE19188, GSE27262, GSE118375, containing 122 lung cancer and 96 normal tissues, were available from GEO database. GEO2R and Venn diagram online software were applied to pick out differentially expressed genes (DEGs). Next, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO) enrichment, followed by protein-protein interaction (PPI) of these DEGs visualized by cytoscape. The MCODE plug-in was performed to construct a module complex of DEGs. In addition, Kaplan-Meier analysis was implemented for analysis of overall survival. To further validate the expression of these genes, Gene Expression Profiling Interactive Analysis (GEPIA) was used.

A total of 149 DEGs were identified, including 127 downregulated genes and 22 upregulated genes. KEGG analysis revealed that the DEGs were mainly enriched in ECM-receptor interaction, Vascular smooth muscle contraction, and PPAR signaling pathway. GO analysis of DEGs showed that significant functional enrichment of angiogenesis, cell adhesion, and vasculogenesis. 13 genes were selected as hub genes based on MCODE, and 11 of 13 genes had a significance. The results of GEPIA were consistent with survival analysis. Furthermore, reanalysis of these genes found they were significantly enriched in ECM-receptor interaction and PI3K-Akt signaling pathway.

We have identified several key genes, which could be potential diagnostic and prognostic biomarker as well as therapy targets.