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Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis

BACKGROUND: Non-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide, but the driver genes and signaling pathways involved are largely unclear. Herein, our study aimed to identify significant genes with poor outcome and underlying mechanisms in NSC...

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Autores principales: Cui, Shengjin, Lou, Shuang, Feng, Jingying, Tang, Xi, Xiao, Xiaowei, Huang, Rong, Guo, Weiquan, Zhou, Yiwen, Huang, Feixia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9091047/
https://www.ncbi.nlm.nih.gov/pubmed/35571642
http://dx.doi.org/10.21037/tcr-21-1986
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author Cui, Shengjin
Lou, Shuang
Feng, Jingying
Tang, Xi
Xiao, Xiaowei
Huang, Rong
Guo, Weiquan
Zhou, Yiwen
Huang, Feixia
author_facet Cui, Shengjin
Lou, Shuang
Feng, Jingying
Tang, Xi
Xiao, Xiaowei
Huang, Rong
Guo, Weiquan
Zhou, Yiwen
Huang, Feixia
author_sort Cui, Shengjin
collection PubMed
description BACKGROUND: Non-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide, but the driver genes and signaling pathways involved are largely unclear. Herein, our study aimed to identify significant genes with poor outcome and underlying mechanisms in NSCLC using bioinformatics analyses. METHODS: Gene expression profiles (GSE33532, GSE19188, GSE102287, GSE27262), including 319 NSCLC and 232 adjacent lung tissues, were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by the GEO2R online tool. Functional and pathway enrichment analyses were performed via the DAVID database. The protein-protein interactions (PPIs) of these DEGs were constructed by the STRING website and visualized by the Cytoscape software platform. The expression of hub genes in NSCLC was validated through the GEPIA database. Kaplan-Meier plotter was used to analyse the survival rate with multivariate Cox regression. The expression of protein tyrosine kinase 2 (PTK2) in NSCLC and adjacent lung tissues was evaluated on the UALCAN database platform. RESULTS: A total of 225 significant DEGs were obtained between NSCLC and adjacent lung tissues, containing 52 upregulated genes and 173 downregulated genes. The DEGs were clustered based on functions and signaling pathways that may be closely associated with NSCLC occurrence. A total of 174 DEGs were identified from the PPI network complex. Top 10 hub genes were selected by CytoHubba plugin. As independent predictors, seven genes (COL1A1, ADAM12, VWF, OGN, EDN1, CAV1, ITGA8) were associated with poor prognosis in NSCLC via multivariate Cox regression (P<0.01). Four genes (VWF, CAV1, ITGA8, COL1A1) were found to be significantly enriched in the focal adhesion pathway (P=1.04E-04) and to be upstream regulators of PTK2. PTK2 was upregulated in NSCLC and associated with poor survival prognosis in lung squamous cell carcinoma (LUSC). CONCLUSIONS: Taken together, the important genes and pathways in NSCLC were identified by using integrated bioinformatics analysis. PTK2 could be a key gene associated with the biological process of NSCLC formation and progression and a potential therapeutic target for NSCLC treatment.
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spelling pubmed-90910472022-05-12 Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis Cui, Shengjin Lou, Shuang Feng, Jingying Tang, Xi Xiao, Xiaowei Huang, Rong Guo, Weiquan Zhou, Yiwen Huang, Feixia Transl Cancer Res Original Article BACKGROUND: Non-small cell lung cancer (NSCLC) is a common malignancy with a high morbidity and mortality rate worldwide, but the driver genes and signaling pathways involved are largely unclear. Herein, our study aimed to identify significant genes with poor outcome and underlying mechanisms in NSCLC using bioinformatics analyses. METHODS: Gene expression profiles (GSE33532, GSE19188, GSE102287, GSE27262), including 319 NSCLC and 232 adjacent lung tissues, were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by the GEO2R online tool. Functional and pathway enrichment analyses were performed via the DAVID database. The protein-protein interactions (PPIs) of these DEGs were constructed by the STRING website and visualized by the Cytoscape software platform. The expression of hub genes in NSCLC was validated through the GEPIA database. Kaplan-Meier plotter was used to analyse the survival rate with multivariate Cox regression. The expression of protein tyrosine kinase 2 (PTK2) in NSCLC and adjacent lung tissues was evaluated on the UALCAN database platform. RESULTS: A total of 225 significant DEGs were obtained between NSCLC and adjacent lung tissues, containing 52 upregulated genes and 173 downregulated genes. The DEGs were clustered based on functions and signaling pathways that may be closely associated with NSCLC occurrence. A total of 174 DEGs were identified from the PPI network complex. Top 10 hub genes were selected by CytoHubba plugin. As independent predictors, seven genes (COL1A1, ADAM12, VWF, OGN, EDN1, CAV1, ITGA8) were associated with poor prognosis in NSCLC via multivariate Cox regression (P<0.01). Four genes (VWF, CAV1, ITGA8, COL1A1) were found to be significantly enriched in the focal adhesion pathway (P=1.04E-04) and to be upstream regulators of PTK2. PTK2 was upregulated in NSCLC and associated with poor survival prognosis in lung squamous cell carcinoma (LUSC). CONCLUSIONS: Taken together, the important genes and pathways in NSCLC were identified by using integrated bioinformatics analysis. PTK2 could be a key gene associated with the biological process of NSCLC formation and progression and a potential therapeutic target for NSCLC treatment. AME Publishing Company 2022-04 /pmc/articles/PMC9091047/ /pubmed/35571642 http://dx.doi.org/10.21037/tcr-21-1986 Text en 2022 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
spellingShingle Original Article
Cui, Shengjin
Lou, Shuang
Feng, Jingying
Tang, Xi
Xiao, Xiaowei
Huang, Rong
Guo, Weiquan
Zhou, Yiwen
Huang, Feixia
Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis
title Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis
title_full Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis
title_fullStr Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis
title_full_unstemmed Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis
title_short Identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis
title_sort identification of genes and pathways leading to poor prognosis of non-small cell lung cancer using integrated bioinformatics analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9091047/
https://www.ncbi.nlm.nih.gov/pubmed/35571642
http://dx.doi.org/10.21037/tcr-21-1986
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