Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer
Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blo...
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description | Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis. |
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This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0274629</identifier><identifier>PMID: 36194576</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Air flow ; Annotations ; Biodegradation ; Bioinformatics ; Biology and Life Sciences ; Biomarkers ; Blood ; Bronchitis ; Chronic obstructive pulmonary disease ; Clusters ; Computer and Information Sciences ; Computer applications ; Cytokines ; Datasets ; Development and progression ; Diagnosis ; DNA microarrays ; Gene expression ; Gene set enrichment analysis ; Genes ; Genetic aspects ; Genetic markers ; Genomes ; Health aspects ; Identification and classification ; Immune system ; Inflammation ; Interactomes ; IRAK protein ; Lung cancer ; Lung diseases ; Lung diseases, Obstructive ; Medicine and Health Sciences ; Morbidity ; Obstructive lung disease ; Ontology ; Proteins ; Statistical analysis ; Tissues ; Transcriptomics</subject><ispartof>PloS one, 2022-10, Vol.17 (10), p.e0274629-e0274629</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Banaganapalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer</title><author>Banaganapalli, Babajan ; Mallah, Bayan ; Alghamdi, Kawthar Saad ; Albaqami, Walaa F ; Alshaer, Dalal Sameer ; Alrayes, Nuha ; Elango, Ramu ; Shaik, Noor A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c669t-a46320cb3a64c33e8903952e59b0bb9d8effaae6488bbaff7a5fa58fb17669593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Air flow</topic><topic>Annotations</topic><topic>Biodegradation</topic><topic>Bioinformatics</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Blood</topic><topic>Bronchitis</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Clusters</topic><topic>Computer and Information Sciences</topic><topic>Computer 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Bayan</au><au>Alghamdi, Kawthar Saad</au><au>Albaqami, Walaa F</au><au>Alshaer, Dalal Sameer</au><au>Alrayes, Nuha</au><au>Elango, Ramu</au><au>Shaik, Noor A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer</atitle><jtitle>PloS one</jtitle><date>2022-10-04</date><risdate>2022</risdate><volume>17</volume><issue>10</issue><spage>e0274629</spage><epage>e0274629</epage><pages>e0274629-e0274629</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>36194576</pmid><doi>10.1371/journal.pone.0274629</doi><tpages>e0274629</tpages><orcidid>https://orcid.org/0000-0001-8089-2210</orcidid><orcidid>https://orcid.org/0000-0002-0262-5050</orcidid><orcidid>https://orcid.org/0000-0003-4264-2135</orcidid><oa>free_for_read</oa></addata></record> |
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source | Public Library of Science (PLoS) Journals Open Access; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Air flow Annotations Biodegradation Bioinformatics Biology and Life Sciences Biomarkers Blood Bronchitis Chronic obstructive pulmonary disease Clusters Computer and Information Sciences Computer applications Cytokines Datasets Development and progression Diagnosis DNA microarrays Gene expression Gene set enrichment analysis Genes Genetic aspects Genetic markers Genomes Health aspects Identification and classification Immune system Inflammation Interactomes IRAK protein Lung cancer Lung diseases Lung diseases, Obstructive Medicine and Health Sciences Morbidity Obstructive lung disease Ontology Proteins Statistical analysis Tissues Transcriptomics |
title | Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer |
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