Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1784878213611126784 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9876670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT caikaier identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT xiezhilong identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT liuyingao identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT wujunfeng identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT songhao identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT liuwang identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT wangxinyi identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT xiongyinghuan identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT gansiyuan identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics AT sunyanqin identificationofpotentialkeygenesandprognosticbiomarkersoflungcancerbasedonbioinformatics |