Identification of genes associated with lung cancer by bioinformatics analysis.

OBJECTIVE: This study was aimed to explore the underlying genes associated with lung cancer (LC) by bioinformatics analysis.

DATA AND METHODS: Gene expression profile GSE2514 was downloaded from the Gene Expression Omnibus database. Twenty lung and nineteen para-carcinoma tissue samples were used to identify the differentially expressed genes (DEGs) by paired t-test. Pathway enrichment analysis of DEGs was performed, followed by the construction of protein-protein interaction (PPI) network. Functional enrichment analysis of the module identified from PPI network was performed, and the enriched term with the highest enrichment scores was selected for pathway enrichment analysis.

RESULTS: Total 257 DEGs including 179 up-regulated DEGs such as monoamine oxidase A (MAOA) and intercellular adhesion molecule 2 (ICAM2), and 78 down-regulated DEGs such as thrombospondin-2 (THBS2) were identified. Up-regulated DEGs were enriched in 7 pathways, such as drug metabolism, tyrosine metabolism and cell adhesion molecules (CAMs). Down-regulated DEGs were enriched in extracellular cell matrix receptor interaction and focal adhesion pathways. In the PPI network, interleukin-6 (IL6) had the highest connectivity degree of 39. Module 1 with the highest functional enrichment scores of 5.457 containing 13 hub genes such as KIAA0101.

CONCLUSIONS: DEGs of LC were mainly enriched in the pathways related to metabolism and cell adhesion. The DEGs such as MAOA, ICAM2, IL6, THBS2 and KIAA0101 may be the potential targets for LC diagnosis and treatment.