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Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis
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 we...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ivyspring International Publisher
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329865/ https://www.ncbi.nlm.nih.gov/pubmed/30662525 http://dx.doi.org/10.7150/jca.26908 |
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author | Shi, Ke Li, Na Yang, Meilan Li, Wei |
author_facet | Shi, Ke Li, Na Yang, Meilan Li, Wei |
author_sort | Shi, Ke |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6329865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-63298652019-01-18 Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis Shi, Ke Li, Na Yang, Meilan Li, Wei J Cancer Research Paper 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. Ivyspring International Publisher 2019-01-01 /pmc/articles/PMC6329865/ /pubmed/30662525 http://dx.doi.org/10.7150/jca.26908 Text en © Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
spellingShingle | Research Paper Shi, Ke Li, Na Yang, Meilan Li, Wei Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis |
title | Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis |
title_full | Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis |
title_fullStr | Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis |
title_full_unstemmed | Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis |
title_short | Identification of Key Genes and Pathways in Female Lung Cancer Patients Who Never Smoked by a Bioinformatics Analysis |
title_sort | identification of key genes and pathways in female lung cancer patients who never smoked by a bioinformatics analysis |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329865/ https://www.ncbi.nlm.nih.gov/pubmed/30662525 http://dx.doi.org/10.7150/jca.26908 |
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