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Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

OBJECTIVE: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. METHODS: Five sets of gene...

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Autores principales: Cai, Kaier, Xie, Zhilong, Liu, Yingao, Wu, Junfeng, Song, Hao, Liu, Wang, Wang, Xinyi, Xiong, Yinghuan, Gan, Siyuan, Sun, Yanqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876670/
https://www.ncbi.nlm.nih.gov/pubmed/36714024
http://dx.doi.org/10.1155/2023/2152432
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author Cai, Kaier
Xie, Zhilong
Liu, Yingao
Wu, Junfeng
Song, Hao
Liu, Wang
Wang, Xinyi
Xiong, Yinghuan
Gan, Siyuan
Sun, Yanqin
author_facet Cai, Kaier
Xie, Zhilong
Liu, Yingao
Wu, Junfeng
Song, Hao
Liu, Wang
Wang, Xinyi
Xiong, Yinghuan
Gan, Siyuan
Sun, Yanqin
author_sort Cai, Kaier
collection PubMed
description OBJECTIVE: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. METHODS: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan–Meier online plotter tool. RESULTS: CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. CONCLUSION: CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.
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spelling pubmed-98766702023-01-26 Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics Cai, Kaier Xie, Zhilong Liu, Yingao Wu, Junfeng Song, Hao Liu, Wang Wang, Xinyi Xiong, Yinghuan Gan, Siyuan Sun, Yanqin Biomed Res Int Research Article OBJECTIVE: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. METHODS: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan–Meier online plotter tool. RESULTS: CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. CONCLUSION: CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer. Hindawi 2023-01-18 /pmc/articles/PMC9876670/ /pubmed/36714024 http://dx.doi.org/10.1155/2023/2152432 Text en Copyright © 2023 Kaier Cai et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cai, Kaier
Xie, Zhilong
Liu, Yingao
Wu, Junfeng
Song, Hao
Liu, Wang
Wang, Xinyi
Xiong, Yinghuan
Gan, Siyuan
Sun, Yanqin
Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics
title Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics
title_full Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics
title_fullStr Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics
title_full_unstemmed Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics
title_short Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics
title_sort identification of potential key genes and prognostic biomarkers of lung cancer based on bioinformatics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876670/
https://www.ncbi.nlm.nih.gov/pubmed/36714024
http://dx.doi.org/10.1155/2023/2152432
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