ISSN: 1837-9664Journal of Cancer
Global reach, higher impact
J Cancer 2019; 10(1):51-60. doi:10.7150/jca.26908 This issue Cite
Research Paper
Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis
1. Department of Geriatrics, Clinical Laboratory, Xiangya Hospital of Central South University, Changsha, People's Republic of China
2. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, People's Republic of China
3. Department of Pathology, the First Affiliated Hospital of Hunan University of Medicine, Huaihua, People's Republic of China
*These authors contributed equally to this study.
Abstract
Smoking is considered the major risk factor for lung cancer, but only a small portion of female lung adenocarcinoma patients are associated with smoking. Thus, identifying crucial genes and pathways related to nonsmoking female lung cancer patients is of great importance. Gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. The R software packages were applied to screen the differentially expressed genes (DEGs). GO term enrichment and KEGG pathway analyses were carried out using DAVID tools. The protein-protein interaction (PPI) network was constructed by Cytoscape software. In total, 487 downregulated and 199 upregulated DEGs were identified. The down-regulated DEGs were mainly enriched for behavior and response to wounding, and the upregulated DEGs were significantly enriched for multicellular organismal metabolic process and cell division. The KEGG pathway analysis revealed that the downregulated DEGs were significantly enriched for cell adhesion molecules and neuroactive ligand-receptor interaction, while the upregulated DEGs were mainly enriched for cell cycle and the p53 signaling pathway. The top 10 hub genes and top 3 gene interaction modules were selected from the PPI network. Of the ten hub genes, a high expression of five genes was related to a poor OS in female lung cancer patients who never smoked, including IL6, CXCR2, FPR2, PPBP and HBA1. However, a low expression of GNG11, LRRK2, CDH5, CAV1 and SELE was associated with a worse OS for the female lung cancer patients who never smoked. In conclusion, our study provides novel insight for a better understanding of the pathogenesis of nonsmoking female lung cancer, and these identified DEGs may serve as biomarkers for diagnostics and treatment.
Keywords: female lung cancer, never smoking, hub genes, bioinformatics
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