Identification of feature genes for smoking-related lung adenocarcinoma based on gene expression profile data.

This study aimed to identify the genes and pathways associated with smoking-related lung adenocarcinoma. Three lung adenocarcinoma associated datasets (GSE43458, GSE10072, and GSE50081), the subjects of which included smokers and nonsmokers, were downloaded to screen the differentially expressed feature genes between smokers and nonsmokers. Based on the identified feature genes, we constructed the protein-protein interaction (PPI) network and optimized feature genes using closeness centrality (CC) algorithm. Then, the support vector machine (SVM) classification model was constructed based on the feature genes with higher CC values. Finally, pathway enrichment analysis of the feature genes was performed. A total of 213 down-regulated and 83 up-regulated differentially expressed genes were identified. In the constructed PPI network, the top ten nodes with higher degrees and CC values included ANK3, EPHA4, FGFR2, etc. The SVM classifier was constructed with 27 feature genes, which could accurately identify smokers and nonsmokers. Pathways enrichment analysis for the 27 feature genes revealed that they were significantly enriched in five pathways, including proteoglycans in cancer (EGFR, SDC4, SDC2, etc.), and Ras signaling pathway (FGFR2, PLA2G1B, EGFR, etc.). The 27 feature genes, such as EPHA4, FGFR2, and EGFR for SVM classifier construction and cancer-related pathways of Ras signaling pathway and proteoglycans in cancer may play key roles in the progression and development of smoking-related lung adenocarcinoma.