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Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer

BACKGROUND: Non-small cell lung cancer (NSCLC) is the main histologic form of lung cancer that affects human health, but biomarkers for therapeutic diagnosis and prognosis of the disease are currently lacking. MATERIAL/METHODS: The gene expression profile GSE18842 was downloaded from the Gene Expres...

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Autores principales: Liu, Xingyuan, Liu, Xuefeng, Li, Jingyuan, Ren, Fu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6911305/
https://www.ncbi.nlm.nih.gov/pubmed/31805030
http://dx.doi.org/10.12659/MSM.918620
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author Liu, Xingyuan
Liu, Xuefeng
Li, Jingyuan
Ren, Fu
author_facet Liu, Xingyuan
Liu, Xuefeng
Li, Jingyuan
Ren, Fu
author_sort Liu, Xingyuan
collection PubMed
description BACKGROUND: Non-small cell lung cancer (NSCLC) is the main histologic form of lung cancer that affects human health, but biomarkers for therapeutic diagnosis and prognosis of the disease are currently lacking. MATERIAL/METHODS: The gene expression profile GSE18842 was downloaded from the Gene Expression Omnibus database in this prospective study, which consisted of 46 tumors and 45 controls. After screening differentially expressed genes (DEGs), we conducted functional enrichment analysis and KEGG analysis with upregulated differentially expressed genes (uDEGs) and downregulated differentially expressed genes (dDEGs), respectively. Protein–protein interaction (PPI) networks among DEGs and corresponding coding protein complexes, constructed using the STRING database, were analyzed using Cytoscape. Kaplan-Meier method was used to verify survival associated with hub genes. The GEPIA webserver was used to plot the gene expression level heat map of hub genes between NSCLC and adjacent lung tissues in the TCGA database. RESULTS: We identified 368 DEGs (168 uDEGs and 200 dDEGs) in NSCLC samples relative to control samples after gene integration. We established a PPI network for the DEGs, which had 249 nodes and 1472 edges protein pairs. Ten undefined hub genes with the highest connectivity degree (CDK1, UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11) were verified by survival analysis, and 9 of them were associated with poorer overall survival in NSCLC. The expression reliability of hub genes was verified by use of the GEPIA web tool. CONCLUSIONS: The results suggested that UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11 are inherent key biomarkers for diagnosis and prognosis, while KEGG analysis results showed the mitotic cell cycle pathway is a probable signaling pathway contributing to NSCLC progression. These genes could be promising biomarkers for diagnosis and provide a new approach for developing targeted therapeutic NSCLC drugs.
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spelling pubmed-69113052019-12-16 Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Liu, Xingyuan Liu, Xuefeng Li, Jingyuan Ren, Fu Med Sci Monit Special Reports BACKGROUND: Non-small cell lung cancer (NSCLC) is the main histologic form of lung cancer that affects human health, but biomarkers for therapeutic diagnosis and prognosis of the disease are currently lacking. MATERIAL/METHODS: The gene expression profile GSE18842 was downloaded from the Gene Expression Omnibus database in this prospective study, which consisted of 46 tumors and 45 controls. After screening differentially expressed genes (DEGs), we conducted functional enrichment analysis and KEGG analysis with upregulated differentially expressed genes (uDEGs) and downregulated differentially expressed genes (dDEGs), respectively. Protein–protein interaction (PPI) networks among DEGs and corresponding coding protein complexes, constructed using the STRING database, were analyzed using Cytoscape. Kaplan-Meier method was used to verify survival associated with hub genes. The GEPIA webserver was used to plot the gene expression level heat map of hub genes between NSCLC and adjacent lung tissues in the TCGA database. RESULTS: We identified 368 DEGs (168 uDEGs and 200 dDEGs) in NSCLC samples relative to control samples after gene integration. We established a PPI network for the DEGs, which had 249 nodes and 1472 edges protein pairs. Ten undefined hub genes with the highest connectivity degree (CDK1, UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11) were verified by survival analysis, and 9 of them were associated with poorer overall survival in NSCLC. The expression reliability of hub genes was verified by use of the GEPIA web tool. CONCLUSIONS: The results suggested that UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11 are inherent key biomarkers for diagnosis and prognosis, while KEGG analysis results showed the mitotic cell cycle pathway is a probable signaling pathway contributing to NSCLC progression. These genes could be promising biomarkers for diagnosis and provide a new approach for developing targeted therapeutic NSCLC drugs. International Scientific Literature, Inc. 2019-12-05 /pmc/articles/PMC6911305/ /pubmed/31805030 http://dx.doi.org/10.12659/MSM.918620 Text en © Med Sci Monit, 2019 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Special Reports
Liu, Xingyuan
Liu, Xuefeng
Li, Jingyuan
Ren, Fu
Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
title Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
title_full Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
title_fullStr Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
title_full_unstemmed Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
title_short Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
title_sort identification and integrated analysis of key biomarkers for diagnosis and prognosis of non-small cell lung cancer
topic Special Reports
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6911305/
https://www.ncbi.nlm.nih.gov/pubmed/31805030
http://dx.doi.org/10.12659/MSM.918620
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