Abstract

BACKGROUND: The aim of this study was to explore the differently expressed genes and pathways in non-small cell lung cancer (NSCLC) and their correlation with the prognosis.

MATERIAL AND METHODS: Gene expression data series of GSE19804, GSE101929, and GSE33532 were downloaded from the Gene Expression Ominibus (GEO) database. The overlaping differently expressed genes (DEGs) were identified form the above 3 data series. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) were used to analyze the biological functions and signal pathways of DEGs. The protein–protein interaction (PPI) was analyzed thorough Search Tool for the Retrieval of Interacting Gens (STRING). The relationship between the expression of hub genes and the prognosis of patients was analyzed by Kaplan-Meier Plotter online software.

RESULTS: Twenty-nine DEGs were identified, with 22 upregulated genes and 7 downregulated genes. The enriched biological processes were mainly related to diet-induced thermogenesis and actin filament binding. The KEGG pathways were enriched in calcium signaling, regulation of lipolysis in adipocytes, and PPAR signaling. Two downregulated genes (MMP1 and SPP1) were identified as hub genes by Cytohubba. Twenty-two dysregulated genes were correlated with patient prognosis.

CONCLUSIONS: Differentially expressed genes are common in NSCLC patients and can be used as biomarkers for patient prognosis.

Keywords: Microarray Analysis, Prognosis, Biomarkers, Tumor, Computational Biology, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, gene ontology, Gene Regulatory Networks, Genetic Predisposition to Disease, Protein Interaction Mapping, Protein Interaction Maps, Software, transcriptome