Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis

The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for i...

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Autores principales:	Panagiotis Giannos, Konstantinos S. Kechagias, Annamaria Gal
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	non-small cell lung cancer epithelial-mesenchymal transition TGF-β integrated bioinformatics analysis gene biomarkers Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0a0d46903ab54f1281b85c27de13981c
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spelling	oai:doaj.org-article:0a0d46903ab54f1281b85c27de13981c2021-11-25T16:47:56ZIdentification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis10.3390/biology101112002079-7737https://doaj.org/article/0a0d46903ab54f1281b85c27de13981c2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-7737/10/11/1200https://doaj.org/toc/2079-7737The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. In the current study, we performed an integrated bioinformatics analysis on gene expression datasets of TGF-β-induced EMT in NSCLC cells to identify novel gene biomarkers and elucidate their regulation in NSCLC progression. The gene expression datasets were extracted from the NCBI Gene Expression Omnibus repository, and differentially expressed genes (DEGs) between TGF-β-treated and untreated NSCLC cells were retrieved. A protein-protein interaction network was constructed and hub genes were identified. Functional and pathway enrichment analyses were conducted on module DEGs, and a correlation between the expression levels of module genes and survival of NSCLC patients was evaluated. Prediction of interactions of the biomarker genes with transcription factors and miRNAs was also carried out. We described four protein clusters in which DEGs were associated with ubiquitination (Module 1), regulation of cell death and cell adhesions (Module 2), oxidation-reduction reactions of aerobic respiration (Module 3) and mitochondrial translation (Module 4). From the module genes, we identified ten prognostic gene biomarkers in NSCLC. Low expression levels of <i>KCTD6</i>, <i>KBTBD7</i>, <i>LMO7</i>, <i>SPSB2</i>, <i>RNF19A</i>, <i>FOXA2</i>, <i>DHTKD1</i>, <i>CDH1</i> and <i>PDHB</i> and high expression level of <i>KLHL25</i> were associated with reduced overall survival of NSCLC patients. Most of these biomarker genes were involved in protein ubiquitination. The regulatory network of the gene biomarkers revealed their interaction with tumor suppressor miRNAs and transcription factors involved in the mechanisms of cancer progression. This ten-gene prognostic signature can be useful to improve risk prediction and therapeutic strategies in NSCLC. Our analysis also highlights the importance of deregulation of ubiquitination in EMT-associated NSCLC progression.Panagiotis GiannosKonstantinos S. KechagiasAnnamaria GalMDPI AGarticlenon-small cell lung cancerepithelial-mesenchymal transitionTGF-βintegrated bioinformatics analysisgene biomarkersBiology (General)QH301-705.5ENBiology, Vol 10, Iss 1200, p 1200 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	non-small cell lung cancer epithelial-mesenchymal transition TGF-β integrated bioinformatics analysis gene biomarkers Biology (General) QH301-705.5
spellingShingle	non-small cell lung cancer epithelial-mesenchymal transition TGF-β integrated bioinformatics analysis gene biomarkers Biology (General) QH301-705.5 Panagiotis Giannos Konstantinos S. Kechagias Annamaria Gal Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis
description	The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. In the current study, we performed an integrated bioinformatics analysis on gene expression datasets of TGF-β-induced EMT in NSCLC cells to identify novel gene biomarkers and elucidate their regulation in NSCLC progression. The gene expression datasets were extracted from the NCBI Gene Expression Omnibus repository, and differentially expressed genes (DEGs) between TGF-β-treated and untreated NSCLC cells were retrieved. A protein-protein interaction network was constructed and hub genes were identified. Functional and pathway enrichment analyses were conducted on module DEGs, and a correlation between the expression levels of module genes and survival of NSCLC patients was evaluated. Prediction of interactions of the biomarker genes with transcription factors and miRNAs was also carried out. We described four protein clusters in which DEGs were associated with ubiquitination (Module 1), regulation of cell death and cell adhesions (Module 2), oxidation-reduction reactions of aerobic respiration (Module 3) and mitochondrial translation (Module 4). From the module genes, we identified ten prognostic gene biomarkers in NSCLC. Low expression levels of <i>KCTD6</i>, <i>KBTBD7</i>, <i>LMO7</i>, <i>SPSB2</i>, <i>RNF19A</i>, <i>FOXA2</i>, <i>DHTKD1</i>, <i>CDH1</i> and <i>PDHB</i> and high expression level of <i>KLHL25</i> were associated with reduced overall survival of NSCLC patients. Most of these biomarker genes were involved in protein ubiquitination. The regulatory network of the gene biomarkers revealed their interaction with tumor suppressor miRNAs and transcription factors involved in the mechanisms of cancer progression. This ten-gene prognostic signature can be useful to improve risk prediction and therapeutic strategies in NSCLC. Our analysis also highlights the importance of deregulation of ubiquitination in EMT-associated NSCLC progression.
format	article
author	Panagiotis Giannos Konstantinos S. Kechagias Annamaria Gal
author_facet	Panagiotis Giannos Konstantinos S. Kechagias Annamaria Gal
author_sort	Panagiotis Giannos
title	Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis
title_short	Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis
title_full	Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis
title_fullStr	Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis
title_full_unstemmed	Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis
title_sort	identification of prognostic gene biomarkers in non-small cell lung cancer progression by integrated bioinformatics analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0a0d46903ab54f1281b85c27de13981c
work_keys_str_mv	AT panagiotisgiannos identificationofprognosticgenebiomarkersinnonsmallcelllungcancerprogressionbyintegratedbioinformaticsanalysis AT konstantinosskechagias identificationofprognosticgenebiomarkersinnonsmallcelllungcancerprogressionbyintegratedbioinformaticsanalysis AT annamariagal identificationofprognosticgenebiomarkersinnonsmallcelllungcancerprogressionbyintegratedbioinformaticsanalysis
_version_	1718412938321068032

Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis

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