Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification

Objective The aim of this study was to identify the molecular subtypes of chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using bioinformatics methods. Materials and Methods In this bioinformatics study, the gene expression dataset GSE76705 (including 229 COP...

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Main Authors: Jingming Zhao, Wei Cheng, Xigang He, Yanli Liu, Ji Li, Jiaxing Sun, Jinfeng Li, Fangfang Wang, Yufang Gao
Format: Article
Language:English
Published: Royan Institute (ACECR), Tehran 2018-06-01
Series:Cell Journal
Subjects:
Online Access:http://celljournal.org/journal/article/21034/download
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spelling doaj-34fbbf3758ec4c62991417c00439b19f2020-11-25T02:05:59ZengRoyan Institute (ACECR), TehranCell Journal2228-58062228-58142018-06-0120332633210.22074/cellj.2018.5412Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene IdentificationJingming Zhao0Wei Cheng1Xigang He2Yanli Liu3Ji Li4Jiaxing Sun5Jinfeng Li6Fangfang Wang7Yufang Gao8Department of Respiratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China Department of Respiratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China Department of Respiratory Medicine, People’s Hospital of RizhaoLanshan, Rizhao, ChinaDepartment of Respiratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China Department of Pharmacy, Qilu Hospital of Shandong University (Qingdao), Qingdao, ChinaDepartment of Respiratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China Department of Respiratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China Department of Respiratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China Department of President’s Office, The Affiliated Hospital of Qingdao University, Qingdao, ChinaObjective The aim of this study was to identify the molecular subtypes of chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using bioinformatics methods. Materials and Methods In this bioinformatics study, the gene expression dataset GSE76705 (including 229 COPD samples) and known COPD-related genes (candidate genes) were downloaded from the Gene Expression Omnibus (GEO) and the Online Mendelian Inheritance in Man (OMIM) databases respectively. Based on the expression values of the candidate genes, COPD samples were divided into molecular subtypes through hierarchical clustering analysis. Candidate genes were accordingly allocated into the defined molecular subtypes and functional enrichment analysis was undertaken. Pathway deviation scores were then analyzed, followed by the analysis of clinical indicators (FEV1, FEV1/FVC, age and gender) of COPD patients in each subtype, and prediction models were constructed. Furthermore, the gene expression dataset GSE71220 was used to bioinformatically validate our results. Results A total of 213 COPD-related genes were identified, which divided samples into three subtypes based on the gene expression values. After intersection analysis, 160 common genes including transforming growth factor β1 (TGFB1), epidermal growth factor receptor (EGFR) and interleukin 13 (IL13) were obtained. Functional enrichment analysis identified 22 pathways such as ‘hsa04060: cytokine-cytokine receptor interaction pathways, ‘hsa04110: cell cycle’ and ‘hsa05222: small cell lung cancer’. Pathways in subtype 2 had higher deviation scores. Furthermore, three receiver operating characteristic (ROC) curves (accuracies >80%) were constructed. The three subtypes in COPD samples were also identified in the validation dataset GSE71220. Conclusion COPD may be further subdivided into several molecular subtypes, which may be useful in improving COPD therapy based on the molecular subtype of a patient.http://celljournal.org/journal/article/21034/downloadChronic Obstructive Pulmonary DiseasePathwaySubtype
collection DOAJ
language English
format Article
sources DOAJ
author Jingming Zhao
Wei Cheng
Xigang He
Yanli Liu
Ji Li
Jiaxing Sun
Jinfeng Li
Fangfang Wang
Yufang Gao
spellingShingle Jingming Zhao
Wei Cheng
Xigang He
Yanli Liu
Ji Li
Jiaxing Sun
Jinfeng Li
Fangfang Wang
Yufang Gao
Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification
Cell Journal
Chronic Obstructive Pulmonary Disease
Pathway
Subtype
author_facet Jingming Zhao
Wei Cheng
Xigang He
Yanli Liu
Ji Li
Jiaxing Sun
Jinfeng Li
Fangfang Wang
Yufang Gao
author_sort Jingming Zhao
title Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification
title_short Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification
title_full Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification
title_fullStr Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification
title_full_unstemmed Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification
title_sort chronic obstructive pulmonary disease molecular subtyping and pathway deviation-based candidate gene identification
publisher Royan Institute (ACECR), Tehran
series Cell Journal
issn 2228-5806
2228-5814
publishDate 2018-06-01
description Objective The aim of this study was to identify the molecular subtypes of chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using bioinformatics methods. Materials and Methods In this bioinformatics study, the gene expression dataset GSE76705 (including 229 COPD samples) and known COPD-related genes (candidate genes) were downloaded from the Gene Expression Omnibus (GEO) and the Online Mendelian Inheritance in Man (OMIM) databases respectively. Based on the expression values of the candidate genes, COPD samples were divided into molecular subtypes through hierarchical clustering analysis. Candidate genes were accordingly allocated into the defined molecular subtypes and functional enrichment analysis was undertaken. Pathway deviation scores were then analyzed, followed by the analysis of clinical indicators (FEV1, FEV1/FVC, age and gender) of COPD patients in each subtype, and prediction models were constructed. Furthermore, the gene expression dataset GSE71220 was used to bioinformatically validate our results. Results A total of 213 COPD-related genes were identified, which divided samples into three subtypes based on the gene expression values. After intersection analysis, 160 common genes including transforming growth factor β1 (TGFB1), epidermal growth factor receptor (EGFR) and interleukin 13 (IL13) were obtained. Functional enrichment analysis identified 22 pathways such as ‘hsa04060: cytokine-cytokine receptor interaction pathways, ‘hsa04110: cell cycle’ and ‘hsa05222: small cell lung cancer’. Pathways in subtype 2 had higher deviation scores. Furthermore, three receiver operating characteristic (ROC) curves (accuracies >80%) were constructed. The three subtypes in COPD samples were also identified in the validation dataset GSE71220. Conclusion COPD may be further subdivided into several molecular subtypes, which may be useful in improving COPD therapy based on the molecular subtype of a patient.
topic Chronic Obstructive Pulmonary Disease
Pathway
Subtype
url http://celljournal.org/journal/article/21034/download
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