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Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database

OBJECTIVE: The data of lung adenocarcinoma- (LUAD-) related gene expression profiles were mined from the Cancer Genome Atlas (TCGA) database using bioinformatics methods and potential biomarkers related to the occurrence, development, and prognosis of LUAD were screened out to explore the key progno...

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Autores principales: Shen, Youfeng, Tang, Xiaoqing, Zhou, Xiaoqin, Yi, Yuanxue, Qiu, Yuan, Xu, Jian, Tian, Xingzhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807302/
https://www.ncbi.nlm.nih.gov/pubmed/36600965
http://dx.doi.org/10.1155/2022/4435092
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author Shen, Youfeng
Tang, Xiaoqing
Zhou, Xiaoqin
Yi, Yuanxue
Qiu, Yuan
Xu, Jian
Tian, Xingzhong
author_facet Shen, Youfeng
Tang, Xiaoqing
Zhou, Xiaoqin
Yi, Yuanxue
Qiu, Yuan
Xu, Jian
Tian, Xingzhong
author_sort Shen, Youfeng
collection PubMed
description OBJECTIVE: The data of lung adenocarcinoma- (LUAD-) related gene expression profiles were mined from the Cancer Genome Atlas (TCGA) database using bioinformatics methods and potential biomarkers related to the occurrence, development, and prognosis of LUAD were screened out to explore the key prognostic genes and clinical significance. METHODS: Following the LUAD gene expression profile data that were initially exported from the TCGA database, R software DESeq2 was employed to analyze the difference between the expression profiles of LUAD and normal tissues. The R package “clusterProfiler” was subsequently utilized to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differential genes. A protein-protein interaction (PPI) network was constructed via the String database, and cytohubba, a plugin of Cytoscape, was applied to screen hub genes using the MCC algorithm. The Gene Expression Profile Data Interactive Analysis (GEPIA) was used to analyze expressions of 10 candidate genes in LUAD samples and healthy lung samples, and the selected genes were employed for survival analysis. RESULTS: A total of 1,598 differential genes were identified through differential analyses and data mining, with 1,394 genes upregulated and 204 downregulated. A total of 10 hub genes CCNA2, CDC20, CCNB2, KIF11, TOP2A, BUB1, BUB1B, CENPF, TPX2, and KIF2C were obtained using the cytohubba plugin. The results of the GEPIA analysis indicated that compared with normal lung tissue, the mRNA expression level of the described hub genes in LUAD tissue was significantly increased (P < 0.05). Survival analysis revealed that these genes had a significant impact on the overall survival time of LUAD patients (P < 0.05). CONCLUSION: The previously described key genes related to LUAD identified in the TCGA database may be used as potential prognostic biomarkers, which will contribute to further comprehension of the occurrence and development of LUAD and provide references for its diagnosis and treatment.
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spelling pubmed-98073022023-01-03 Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database Shen, Youfeng Tang, Xiaoqing Zhou, Xiaoqin Yi, Yuanxue Qiu, Yuan Xu, Jian Tian, Xingzhong J Oncol Research Article OBJECTIVE: The data of lung adenocarcinoma- (LUAD-) related gene expression profiles were mined from the Cancer Genome Atlas (TCGA) database using bioinformatics methods and potential biomarkers related to the occurrence, development, and prognosis of LUAD were screened out to explore the key prognostic genes and clinical significance. METHODS: Following the LUAD gene expression profile data that were initially exported from the TCGA database, R software DESeq2 was employed to analyze the difference between the expression profiles of LUAD and normal tissues. The R package “clusterProfiler” was subsequently utilized to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differential genes. A protein-protein interaction (PPI) network was constructed via the String database, and cytohubba, a plugin of Cytoscape, was applied to screen hub genes using the MCC algorithm. The Gene Expression Profile Data Interactive Analysis (GEPIA) was used to analyze expressions of 10 candidate genes in LUAD samples and healthy lung samples, and the selected genes were employed for survival analysis. RESULTS: A total of 1,598 differential genes were identified through differential analyses and data mining, with 1,394 genes upregulated and 204 downregulated. A total of 10 hub genes CCNA2, CDC20, CCNB2, KIF11, TOP2A, BUB1, BUB1B, CENPF, TPX2, and KIF2C were obtained using the cytohubba plugin. The results of the GEPIA analysis indicated that compared with normal lung tissue, the mRNA expression level of the described hub genes in LUAD tissue was significantly increased (P < 0.05). Survival analysis revealed that these genes had a significant impact on the overall survival time of LUAD patients (P < 0.05). CONCLUSION: The previously described key genes related to LUAD identified in the TCGA database may be used as potential prognostic biomarkers, which will contribute to further comprehension of the occurrence and development of LUAD and provide references for its diagnosis and treatment. Hindawi 2022-12-26 /pmc/articles/PMC9807302/ /pubmed/36600965 http://dx.doi.org/10.1155/2022/4435092 Text en Copyright © 2022 Youfeng Shen 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
Shen, Youfeng
Tang, Xiaoqing
Zhou, Xiaoqin
Yi, Yuanxue
Qiu, Yuan
Xu, Jian
Tian, Xingzhong
Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database
title Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database
title_full Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database
title_fullStr Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database
title_full_unstemmed Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database
title_short Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database
title_sort screening of key prognosis genes of lung adenocarcinoma based on expression analysis on tcga database
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9807302/
https://www.ncbi.nlm.nih.gov/pubmed/36600965
http://dx.doi.org/10.1155/2022/4435092
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