Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis

Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Meth...

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Autores principales:	Ke Gong MBBS, Huiling Zhou MBBS, Haidan Liu Ph.D, Ting Xie Ph.D, Yong Luo MBBS, Hui Guo MBBS, Jinlan Chen Ph.D, Zhiping Tan Ph.D, Yifeng Yang Ph.D, Li Xie Ph.D
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/c4a1cefa63d54b4cb8ab17c4f96f6236
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spelling	oai:doaj.org-article:c4a1cefa63d54b4cb8ab17c4f96f62362021-12-02T01:03:59ZIdentification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis1533-033810.1177/15330338211060202https://doaj.org/article/c4a1cefa63d54b4cb8ab17c4f96f62362021-11-01T00:00:00Zhttps://doi.org/10.1177/15330338211060202https://doaj.org/toc/1533-0338Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan–Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which ( CDC20 , CCNB2 , BUB1 , CCNB1 , CCNA2 , KIF11 , TOP2A , NDC80 , and ASPM ) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1 , CCNB2 , CCNA2 , and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion : CCNB1 , CCNB2 , CCNA2 , and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.Ke Gong MBBSHuiling Zhou MBBSHaidan Liu Ph.DTing Xie Ph.DYong Luo MBBSHui Guo MBBSJinlan Chen Ph.DZhiping Tan Ph.DYifeng Yang Ph.DLi Xie Ph.DSAGE PublishingarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENTechnology in Cancer Research & Treatment, Vol 20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Ke Gong MBBS Huiling Zhou MBBS Haidan Liu Ph.D Ting Xie Ph.D Yong Luo MBBS Hui Guo MBBS Jinlan Chen Ph.D Zhiping Tan Ph.D Yifeng Yang Ph.D Li Xie Ph.D Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
description	Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan–Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which ( CDC20 , CCNB2 , BUB1 , CCNB1 , CCNA2 , KIF11 , TOP2A , NDC80 , and ASPM ) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1 , CCNB2 , CCNA2 , and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion : CCNB1 , CCNB2 , CCNA2 , and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.
format	article
author	Ke Gong MBBS Huiling Zhou MBBS Haidan Liu Ph.D Ting Xie Ph.D Yong Luo MBBS Hui Guo MBBS Jinlan Chen Ph.D Zhiping Tan Ph.D Yifeng Yang Ph.D Li Xie Ph.D
author_facet	Ke Gong MBBS Huiling Zhou MBBS Haidan Liu Ph.D Ting Xie Ph.D Yong Luo MBBS Hui Guo MBBS Jinlan Chen Ph.D Zhiping Tan Ph.D Yifeng Yang Ph.D Li Xie Ph.D
author_sort	Ke Gong MBBS
title	Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
title_short	Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
title_full	Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
title_fullStr	Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
title_full_unstemmed	Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
title_sort	identification and integrate analysis of key biomarkers for diagnosis and prognosis of non-small cell lung cancer based on bioinformatics analysis
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/c4a1cefa63d54b4cb8ab17c4f96f6236
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Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis

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