Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Yunguan, Yella, Jaswanth, Chen, Jing, McCormack, Francis X., Madala, Satish K., Jegga, Anil G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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
_version_ 1783272559506096128
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
work_keys_str_mv AT wangyunguan unsupervisedgeneexpressionanalysesidentifyipfseveritycorrelatedsignaturesassociatedgenesandbiomarkers
AT yellajaswanth unsupervisedgeneexpressionanalysesidentifyipfseveritycorrelatedsignaturesassociatedgenesandbiomarkers
AT chenjing unsupervisedgeneexpressionanalysesidentifyipfseveritycorrelatedsignaturesassociatedgenesandbiomarkers
AT mccormackfrancisx unsupervisedgeneexpressionanalysesidentifyipfseveritycorrelatedsignaturesassociatedgenesandbiomarkers
AT madalasatishk unsupervisedgeneexpressionanalysesidentifyipfseveritycorrelatedsignaturesassociatedgenesandbiomarkers
AT jeggaanilg unsupervisedgeneexpressionanalysesidentifyipfseveritycorrelatedsignaturesassociatedgenesandbiomarkers