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Archive > Volume 27 Issue 7 > 2020,27(7):801-806. DOI:10.3872/j.issn.1007-385X.2020.07.013 Prev Next
Clinical Research
Bioinformatics analysis and functional prediction of potential lung cancer associated genes in female non-smokers
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Abstract:
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.
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Project Supported:
Project supported by the Key Specialty Program of Hainan Province-Emergency Department (No. [2019]124)
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