目的：对非吸烟女性肺癌潜在相关基因进行生物信息学分析及功能预测，探讨非吸烟女性肺癌患者的发病机制及预后标志物。方法：选择从GEO数据库下载非吸烟女性肺癌患者的基因芯片并用GEO2R软件筛选出差异表达基因（differentially expressed gene，DEG），再利用STRING 在线分析软件对DEG 进行GO 和KEGG 分析以及蛋白互作（protein-protein interaction,PPI）网络分析，然后利用插件（M-CODE）对所有DEG进行可视化处理，筛选关键DEG，最后利用GEPIA及Kaplan-Meier plotter在线工具对关键DEG进行功能预测及预后分析。结果：共筛选出160 个DEG，其中上调54 个、下调106 个；GO分析其生物学功能主要与血管形成、单个生物细胞间黏附、GTPase活性正调控和信号转导密切相关（均P<0.05）。KEGG分析发现，可能主要与细胞黏附分子、白细胞迁移、紧密连接和胞吞作用相关（均P<0.05）。PPI 网络分析获得8 个关键DEG，分别是TIE1、PECAM1、VEGFD、ICAM2、ESAM、EMCN、ROBO4 和CLDN5。结论：TIE1、CLDN5、ICAM2、ESAM、VEGFD、ROBO4 可能是非吸烟女性肺癌发病机制的研究靶点，PECAM1、EMCN可能是预测非吸烟女性肺癌患者病情进展及预后的标志物。

Objective: To explore the pathogenosis and prognostic markers for non-smoking female lung cancer patients with bioinformatics analysis and functional prediction of potential lung cancer associated genes in female non-smokers. Methods: Data for nonsmoking female patients with lung cancer were downloaded from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using GEO2R. DAVID online data base was used to perform gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG), and STRING online software was used to perform protein-protein interaction (PPI) analysis; then the plug-in (M-CODE) was used to screen the key DEGs; finally, GEPIA and Kaplan-Meier plotter were used to perform function prediction and prognosis analysis of key DEGs. Results: A total of 160 DEGs were screened, including 54 up-regulated and 106 down-regulated genes; GO enrichment analysis showed that these DEGs were mainly related to neovascularization, single cell adhesion, positive regulation of GTPase activity and signal transduction (all P<0.05). KEGG pathway analysis revealed that DEGs were mainly involved in cell adhesion molecules (CAMs), leukocyte transendothelial migration, tight junction and endocytosis (all P<0.05);PPI network analysis revealed 8 key DEGs, including TIE1, PECAM1, CLDN5, VEGFD, ICAM2, ESAM, EMCN and ROBO4.Conclusion: TIE1, CLDN5, ICAM2, ESAM, VEGFD and ROBO4 may be the research targets of the pathogenesis of non-smoking female lung cancer patients. PECAM1 and EMCN may be the new bio-markers to predict the progression and prognosis of nonsmoking female lung cancer patients.

海南省重点专科项目-急诊科资助项目[No. (2019)124]