Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

Abstract The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected l...

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Autores principales:	Quang-Huy Nguyen, Duc-Hau Le
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5b15dbd6e1264c7ba9ef5c966c1d0c16
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spelling	oai:doaj.org-article:5b15dbd6e1264c7ba9ef5c966c1d0c162021-12-02T15:10:31ZImproving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data10.1038/s41598-020-77318-12045-2322https://doaj.org/article/5b15dbd6e1264c7ba9ef5c966c1d0c162020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77318-1https://doaj.org/toc/2045-2322Abstract The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene .Quang-Huy NguyenDuc-Hau LeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Quang-Huy Nguyen Duc-Hau Le Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
description	Abstract The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene .
format	article
author	Quang-Huy Nguyen Duc-Hau Le
author_facet	Quang-Huy Nguyen Duc-Hau Le
author_sort	Quang-Huy Nguyen
title	Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
title_short	Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
title_full	Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
title_fullStr	Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
title_full_unstemmed	Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
title_sort	improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/5b15dbd6e1264c7ba9ef5c966c1d0c16
work_keys_str_mv	AT quanghuynguyen improvingexistinganalysispipelinetoidentifyandanalyzecancerdrivergenesusingmultiomicsdata AT duchaule improvingexistinganalysispipelinetoidentifyandanalyzecancerdrivergenesusingmultiomicsdata
_version_	1718387690552950784

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

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