Identification of prognostic alternative splicing events in sarcoma

Abstract Sarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomark...

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Autores principales:	Hongshuai Li, Jie Yang, Guohui Yang, Jia Ren, Yu Meng, Peiyi Qi, Nan Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8d19cd2aac4a43e6b3b4cf05a67318fa
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spelling	oai:doaj.org-article:8d19cd2aac4a43e6b3b4cf05a67318fa2021-12-02T16:17:34ZIdentification of prognostic alternative splicing events in sarcoma10.1038/s41598-021-94485-x2045-2322https://doaj.org/article/8d19cd2aac4a43e6b3b4cf05a67318fa2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94485-xhttps://doaj.org/toc/2045-2322Abstract Sarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.Hongshuai LiJie YangGuohui YangJia RenYu MengPeiyi QiNan WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hongshuai Li Jie Yang Guohui Yang Jia Ren Yu Meng Peiyi Qi Nan Wang Identification of prognostic alternative splicing events in sarcoma
description	Abstract Sarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.
format	article
author	Hongshuai Li Jie Yang Guohui Yang Jia Ren Yu Meng Peiyi Qi Nan Wang
author_facet	Hongshuai Li Jie Yang Guohui Yang Jia Ren Yu Meng Peiyi Qi Nan Wang
author_sort	Hongshuai Li
title	Identification of prognostic alternative splicing events in sarcoma
title_short	Identification of prognostic alternative splicing events in sarcoma
title_full	Identification of prognostic alternative splicing events in sarcoma
title_fullStr	Identification of prognostic alternative splicing events in sarcoma
title_full_unstemmed	Identification of prognostic alternative splicing events in sarcoma
title_sort	identification of prognostic alternative splicing events in sarcoma
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8d19cd2aac4a43e6b3b4cf05a67318fa
work_keys_str_mv	AT hongshuaili identificationofprognosticalternativesplicingeventsinsarcoma AT jieyang identificationofprognosticalternativesplicingeventsinsarcoma AT guohuiyang identificationofprognosticalternativesplicingeventsinsarcoma AT jiaren identificationofprognosticalternativesplicingeventsinsarcoma AT yumeng identificationofprognosticalternativesplicingeventsinsarcoma AT peiyiqi identificationofprognosticalternativesplicingeventsinsarcoma AT nanwang identificationofprognosticalternativesplicingeventsinsarcoma
_version_	1718384242887491584

Identification of prognostic alternative splicing events in sarcoma

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