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Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers
BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is a fatal fibrotic lung disease occurring predominantly in middle-aged and older adults. The traditional diagnostic classification of IPF is based on clinical, radiological, and histopathological features. However, the considerable heterogeneity in IP...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5649521/ https://www.ncbi.nlm.nih.gov/pubmed/29058630 http://dx.doi.org/10.1186/s12890-017-0472-9 |
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author | Wang, Yunguan Yella, Jaswanth Chen, Jing McCormack, Francis X. Madala, Satish K. Jegga, Anil G. |
author_facet | Wang, Yunguan Yella, Jaswanth Chen, Jing McCormack, Francis X. Madala, Satish K. Jegga, Anil G. |
author_sort | Wang, Yunguan |
collection | PubMed |
description | BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is a fatal fibrotic lung disease occurring predominantly in middle-aged and older adults. The traditional diagnostic classification of IPF is based on clinical, radiological, and histopathological features. However, the considerable heterogeneity in IPF presentation suggests that differences in gene expression profiles can help to characterize and distinguish disease severity. METHODS: We used data-driven unsupervised clustering analysis, combined with a knowledge-based approach to identify and characterize IPF subgroups. RESULTS: Using transcriptional profiles on lung tissue from 131 patients with IPF/UIP and 12 non-diseased controls, we identified six subgroups of IPF that generally correlated with the disease severity and lung function decline. Network-informed clustering identified the most severe subgroup of IPF that was enriched with genes regulating inflammatory processes, blood pressure and branching morphogenesis of the lung. The differentially expressed genes in six subgroups of IPF compared to healthy control include transcripts of extracellular matrix, epithelial-mesenchymal cell cross-talk, calcium ion homeostasis, and oxygen transport. Further, we compiled differentially expressed gene signatures to identify unique gene clusters that can segregate IPF from normal, and severe from mild IPF. Additional validations of these signatures were carried out in three independent cohorts of IPF/UIP. Finally, using knowledge-based approaches, we identified several novel candidate genes which may also serve as potential biomarkers of IPF. CONCLUSIONS: Discovery of unique and redundant gene signatures for subgroups in IPF can be greatly facilitated through unsupervised clustering. Findings derived from such gene signatures may provide insights into pathogenesis of IPF and facilitate the development of clinically useful biomarkers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12890-017-0472-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5649521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56495212017-11-06 Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers Wang, Yunguan Yella, Jaswanth Chen, Jing McCormack, Francis X. Madala, Satish K. Jegga, Anil G. BMC Pulm Med Research Article BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is a fatal fibrotic lung disease occurring predominantly in middle-aged and older adults. The traditional diagnostic classification of IPF is based on clinical, radiological, and histopathological features. However, the considerable heterogeneity in IPF presentation suggests that differences in gene expression profiles can help to characterize and distinguish disease severity. METHODS: We used data-driven unsupervised clustering analysis, combined with a knowledge-based approach to identify and characterize IPF subgroups. RESULTS: Using transcriptional profiles on lung tissue from 131 patients with IPF/UIP and 12 non-diseased controls, we identified six subgroups of IPF that generally correlated with the disease severity and lung function decline. Network-informed clustering identified the most severe subgroup of IPF that was enriched with genes regulating inflammatory processes, blood pressure and branching morphogenesis of the lung. The differentially expressed genes in six subgroups of IPF compared to healthy control include transcripts of extracellular matrix, epithelial-mesenchymal cell cross-talk, calcium ion homeostasis, and oxygen transport. Further, we compiled differentially expressed gene signatures to identify unique gene clusters that can segregate IPF from normal, and severe from mild IPF. Additional validations of these signatures were carried out in three independent cohorts of IPF/UIP. Finally, using knowledge-based approaches, we identified several novel candidate genes which may also serve as potential biomarkers of IPF. CONCLUSIONS: Discovery of unique and redundant gene signatures for subgroups in IPF can be greatly facilitated through unsupervised clustering. Findings derived from such gene signatures may provide insights into pathogenesis of IPF and facilitate the development of clinically useful biomarkers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12890-017-0472-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-10-20 /pmc/articles/PMC5649521/ /pubmed/29058630 http://dx.doi.org/10.1186/s12890-017-0472-9 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Wang, Yunguan Yella, Jaswanth Chen, Jing McCormack, Francis X. Madala, Satish K. Jegga, Anil G. Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers |
title | Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers |
title_full | Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers |
title_fullStr | Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers |
title_full_unstemmed | Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers |
title_short | Unsupervised gene expression analyses identify IPF-severity correlated signatures, associated genes and biomarkers |
title_sort | unsupervised gene expression analyses identify ipf-severity correlated signatures, associated genes and biomarkers |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5649521/ https://www.ncbi.nlm.nih.gov/pubmed/29058630 http://dx.doi.org/10.1186/s12890-017-0472-9 |
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