Identification of gene markers in the development of smoking-induced lung cancer

Lung cancer is a malignant tumor with high mortality in both women and men. To study the mechanisms of smoking-induced lung cancer, we analyzed microarray of GSE4115. GSE4115 was downloaded from Gene Expression Omnibus including 78 and 85 bronchial epithelium tissue samples separately from smokers with and without lung cancer. Limma package in R was used to screen differentially expressed genes (DEGs). Hierarchical cluster analysis for DEGs was conducted using orange software and visualized by distance map. Using DAVID software, functional and pathway enrichment analyses separately were conducted for the DEGs. And protein-protein interaction (PPI) network was constructed using Cytoscape software. Then, the pathscores of enriched pathways were calculated. Besides, functional features were screened and optimized using the recursive feature elimination (RFE) method. Additionally, the support vector machine (SVM) method was used to train model. Total 1923 DEGs were identified between the two groups. Hierarchical cluster analysis indicated that there were differences in gene level between the two groups. And SVM analysis indicated that the five features had potential diagnostic value. Importantly, MAPK1 (degree=30), SRC (degree=29), SMAD4 (degree=23), EEF1A1 (degree=21), TRAF2 (degree=21) and PLCG1 (degree=20) had higher degrees in the PPI network of the DEGs. They might be involved in smoking-induced lung cancer by interacting with each other (e.g. MAPK1-SMAD4, SMAD4-EEF1A1 and SRC-PLCG1). MAPK1, SRC, SMAD4, EEF1A1, TRAF2 and PLCG1 might be responsible for the development of smoking-induced lung cancer.