Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples

Abstract Chronic obstructive pulmonary disease (COPD) kills over three million people worldwide every year. Despite its high global impact, the knowledge about the underlying molecular mechanisms is still limited. In this study, we aimed to extend the available knowledge by identifying a small set o...

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Autores principales:	Fabienne K. Roessler, Birke J. Benedikter, Bernd Schmeck, Nadav Bar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/349f22dbbe944fe8b92fb7e7f7a439a9
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spelling	oai:doaj.org-article:349f22dbbe944fe8b92fb7e7f7a439a92021-12-02T16:50:27ZNovel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples10.1038/s41598-021-89762-82045-2322https://doaj.org/article/349f22dbbe944fe8b92fb7e7f7a439a92021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89762-8https://doaj.org/toc/2045-2322Abstract Chronic obstructive pulmonary disease (COPD) kills over three million people worldwide every year. Despite its high global impact, the knowledge about the underlying molecular mechanisms is still limited. In this study, we aimed to extend the available knowledge by identifying a small set of COPD-associated genes. We analysed different publicly available gene expression datasets containing whole lung tissue (WLT) and airway epithelium (AE) samples from over 400 human subjects for differentially expressed genes (DEGs). We reduced the resulting sets of 436 and 663 DEGs using a novel computational approach that utilises a random depth-first search to identify genes which improve the distinction between COPD patients and controls along the first principle component of the data. Our method identified small sets of 10 and 15 genes in the WLT and AE, respectively. These sets of genes significantly (p < 10–20) distinguish COPD patients from controls with high fidelity. The final sets revealed novel genes like cysteine rich protein 1 (CRIP1) or secretoglobin family 3A member 2 (SCGB3A2) that may underlie fundamental molecular mechanisms of COPD in these tissues.Fabienne K. RoesslerBirke J. BenedikterBernd SchmeckNadav BarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fabienne K. Roessler Birke J. Benedikter Bernd Schmeck Nadav Bar Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples
description	Abstract Chronic obstructive pulmonary disease (COPD) kills over three million people worldwide every year. Despite its high global impact, the knowledge about the underlying molecular mechanisms is still limited. In this study, we aimed to extend the available knowledge by identifying a small set of COPD-associated genes. We analysed different publicly available gene expression datasets containing whole lung tissue (WLT) and airway epithelium (AE) samples from over 400 human subjects for differentially expressed genes (DEGs). We reduced the resulting sets of 436 and 663 DEGs using a novel computational approach that utilises a random depth-first search to identify genes which improve the distinction between COPD patients and controls along the first principle component of the data. Our method identified small sets of 10 and 15 genes in the WLT and AE, respectively. These sets of genes significantly (p < 10–20) distinguish COPD patients from controls with high fidelity. The final sets revealed novel genes like cysteine rich protein 1 (CRIP1) or secretoglobin family 3A member 2 (SCGB3A2) that may underlie fundamental molecular mechanisms of COPD in these tissues.
format	article
author	Fabienne K. Roessler Birke J. Benedikter Bernd Schmeck Nadav Bar
author_facet	Fabienne K. Roessler Birke J. Benedikter Bernd Schmeck Nadav Bar
author_sort	Fabienne K. Roessler
title	Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples
title_short	Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples
title_full	Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples
title_fullStr	Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples
title_full_unstemmed	Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples
title_sort	novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/349f22dbbe944fe8b92fb7e7f7a439a9
work_keys_str_mv	AT fabiennekroessler novelcomputationalanalysisoflargetranscriptomedatasetsidentifiessetsofgenesdistinguishingchronicobstructivepulmonarydiseasefromhealthylungsamples AT birkejbenedikter novelcomputationalanalysisoflargetranscriptomedatasetsidentifiessetsofgenesdistinguishingchronicobstructivepulmonarydiseasefromhealthylungsamples AT berndschmeck novelcomputationalanalysisoflargetranscriptomedatasetsidentifiessetsofgenesdistinguishingchronicobstructivepulmonarydiseasefromhealthylungsamples AT nadavbar novelcomputationalanalysisoflargetranscriptomedatasetsidentifiessetsofgenesdistinguishingchronicobstructivepulmonarydiseasefromhealthylungsamples
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Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples

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