A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer

Abstract A subset of prostate cancer displays a poor clinical outcome. Therefore, identifying this poor prognostic subset within clinically aggressive groups (defined as a Gleason score (GS) ≧8) and developing effective treatments are essential if we are to improve prostate cancer survival. Here, we...

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Autores principales:	Teppei Iwata, Anna S. Sedukhina, Manabu Kubota, Shigeko Oonuma, Ichiro Maeda, Miki Yoshiike, Wataru Usuba, Kimino Minagawa, Eleina Hames, Rei Meguro, Sunny Cho, Stephen H. H. Chien, Shiro Urabe, Sookhee Pae, Kishore Palanisamy, Toshio Kumai, Kazuo Yudo, Eiji Kikuchi, Ko Sato
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/57ba76a682034c09bcdb46e58c83ae0b
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spelling	oai:doaj.org-article:57ba76a682034c09bcdb46e58c83ae0b2021-12-02T13:20:11ZA new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer10.1038/s41598-021-85086-92045-2322https://doaj.org/article/57ba76a682034c09bcdb46e58c83ae0b2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85086-9https://doaj.org/toc/2045-2322Abstract A subset of prostate cancer displays a poor clinical outcome. Therefore, identifying this poor prognostic subset within clinically aggressive groups (defined as a Gleason score (GS) ≧8) and developing effective treatments are essential if we are to improve prostate cancer survival. Here, we performed a bioinformatics analysis of a TCGA dataset (GS ≧8) to identify pathways upregulated in a prostate cancer cohort with short survival. When conducting bioinformatics analyses, the definition of factors such as “overexpression” and “shorter survival” is vital, as poor definition may lead to mis-estimations. To eliminate this possibility, we defined an expression cutoff value using an algorithm calculated by a Cox regression model, and the hazard ratio for each gene was set so as to identify genes whose expression levels were associated with shorter survival. Next, genes associated with shorter survival were entered into pathway analysis to identify pathways that were altered in a shorter survival cohort. We identified pathways involving upregulation of GRB2. Overexpression of GRB2 was linked to shorter survival in the TCGA dataset, a finding validated by histological examination of biopsy samples taken from the patients for diagnostic purposes. Thus, GRB2 is a novel biomarker that predicts shorter survival of patients with aggressive prostate cancer (GS ≧8).Teppei IwataAnna S. SedukhinaManabu KubotaShigeko OonumaIchiro MaedaMiki YoshiikeWataru UsubaKimino MinagawaEleina HamesRei MeguroSunny ChoStephen H. H. ChienShiro UrabeSookhee PaeKishore PalanisamyToshio KumaiKazuo YudoEiji KikuchiKo SatoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Teppei Iwata Anna S. Sedukhina Manabu Kubota Shigeko Oonuma Ichiro Maeda Miki Yoshiike Wataru Usuba Kimino Minagawa Eleina Hames Rei Meguro Sunny Cho Stephen H. H. Chien Shiro Urabe Sookhee Pae Kishore Palanisamy Toshio Kumai Kazuo Yudo Eiji Kikuchi Ko Sato A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer
description	Abstract A subset of prostate cancer displays a poor clinical outcome. Therefore, identifying this poor prognostic subset within clinically aggressive groups (defined as a Gleason score (GS) ≧8) and developing effective treatments are essential if we are to improve prostate cancer survival. Here, we performed a bioinformatics analysis of a TCGA dataset (GS ≧8) to identify pathways upregulated in a prostate cancer cohort with short survival. When conducting bioinformatics analyses, the definition of factors such as “overexpression” and “shorter survival” is vital, as poor definition may lead to mis-estimations. To eliminate this possibility, we defined an expression cutoff value using an algorithm calculated by a Cox regression model, and the hazard ratio for each gene was set so as to identify genes whose expression levels were associated with shorter survival. Next, genes associated with shorter survival were entered into pathway analysis to identify pathways that were altered in a shorter survival cohort. We identified pathways involving upregulation of GRB2. Overexpression of GRB2 was linked to shorter survival in the TCGA dataset, a finding validated by histological examination of biopsy samples taken from the patients for diagnostic purposes. Thus, GRB2 is a novel biomarker that predicts shorter survival of patients with aggressive prostate cancer (GS ≧8).
format	article
author	Teppei Iwata Anna S. Sedukhina Manabu Kubota Shigeko Oonuma Ichiro Maeda Miki Yoshiike Wataru Usuba Kimino Minagawa Eleina Hames Rei Meguro Sunny Cho Stephen H. H. Chien Shiro Urabe Sookhee Pae Kishore Palanisamy Toshio Kumai Kazuo Yudo Eiji Kikuchi Ko Sato
author_facet	Teppei Iwata Anna S. Sedukhina Manabu Kubota Shigeko Oonuma Ichiro Maeda Miki Yoshiike Wataru Usuba Kimino Minagawa Eleina Hames Rei Meguro Sunny Cho Stephen H. H. Chien Shiro Urabe Sookhee Pae Kishore Palanisamy Toshio Kumai Kazuo Yudo Eiji Kikuchi Ko Sato
author_sort	Teppei Iwata
title	A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer
title_short	A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer
title_full	A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer
title_fullStr	A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer
title_full_unstemmed	A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer
title_sort	new bioinformatics approach identifies overexpression of grb2 as a poor prognostic biomarker for prostate cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/57ba76a682034c09bcdb46e58c83ae0b
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A new bioinformatics approach identifies overexpression of GRB2 as a poor prognostic biomarker for prostate cancer

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