Gene expression profiles on predicting protein interaction network and exploring of new treatments for lung cancer

Abstract

In the present study, we aimed to explore disease-associated genes and their functions in lung cancer. We downloaded the gene expression profile GSE4115 from Gene Expression Omnibus (GEO) database. Total 97 lung cancer and 90 adjacent non-tumor lung tissue (normal) samples were applied to identify the differentially expressed genes (DEGs) by paired t test and variance analysis in spectral angle mapper (SAM) package in R. Gene Ontology (GO) functional enrichment analysis of DEGs were performed with Database for Annotation Visualization and Integrated Discovery, followed by construction of protein-protein interaction (PPI) network from Human Protein Reference Database (HPRD). Finally, network modules were analyzed by the MCODE algorithm to detect protein complexes in the PPI network. Total 3,102 genes were identified as DEGs at FDR < 0.05, including 1,146 down-regulated and 1,956 up-regulated DEGs. GO functional enrichment analysis revealed that up-regulated DEGs mainly participated in cell cycle and intracellular related functions, and down-regulated DEGs might influence cell functions. There were 39,240 pairs of PPIs in human obtained from HPRD databases, 3,102 DEGs were mapped to this PPI network, in which 2,429 pairs of PPIs and 1,342 genes were identified. With MCODE algorithm, 48 modules were selected, including five corresponding modules and three modules with differences in gene expressing profiles. In addition, three DGEs, FXR2, ARFGAP1 and ELAVL1 were discovered as potential lung cancer related genes. The discovery of featured genes which were probably related to lung cancer, has a great significance on studying mechanism, distinguishing normal and cancer tissues, and exploring new treatments for lung cancer.