Screening of critical genes in lung adenocarcinoma via network analysis of gene expression profile

Biomarker discovery is of great importance in diagnosis and treatment of diseases. In present study, a number of differentially expressed genes (DEGs) were identified for lung adenocarcinoma via comparative analysis of gene expression data. A gene expression core signature was generated for four types of lung adenocarcinoma (EGFR-mutated, KRAS-mutated, ALK-mutated and triple-negative adenocarcinoma). Functional enrichment analysis with DAVID tools revealed that up-regulated genes were mainly associated with cell cycle while down-regulated genes were mainly involved in vasculature development and cell adhesion. Then it was used to retrieve relevant small molecule drugs with Connectivity map and trichostatin A was predicted to be the top candidate drug for treatment of lung cancer. Network clustering was performed with MCL in cytoscape to identify sub-networks and several hub genes were obtained: CDC25C, ICT1, TK1 and EZH2. These genes play important roles in the progression of lung cancer and some have been suggested as potential biomarkers. Therefore, our findings are beneficial in deepening the understandings about the pathogenesis and providing directions for future researches.