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Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas

BACKGROUND: Surgery, adjuvant chemotherapy, and radiotherapy are the primary treatment options for soft tissue sarcomas (STSs). However, identifying ways to improve the prognosis of patients with STS remains a considerable challenge. Evidence shows that the dysregulation of alternative splicing (AS)...

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Autores principales: Yang, Xia, Huang, Wen-ting, He, Rong-quan, Ma, Jie, Lin, Peng, Xie, Zu-cheng, Ma, Fu-chao, Chen, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6708253/
https://www.ncbi.nlm.nih.gov/pubmed/31443718
http://dx.doi.org/10.1186/s12967-019-2029-6
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author Yang, Xia
Huang, Wen-ting
He, Rong-quan
Ma, Jie
Lin, Peng
Xie, Zu-cheng
Ma, Fu-chao
Chen, Gang
author_facet Yang, Xia
Huang, Wen-ting
He, Rong-quan
Ma, Jie
Lin, Peng
Xie, Zu-cheng
Ma, Fu-chao
Chen, Gang
author_sort Yang, Xia
collection PubMed
description BACKGROUND: Surgery, adjuvant chemotherapy, and radiotherapy are the primary treatment options for soft tissue sarcomas (STSs). However, identifying ways to improve the prognosis of patients with STS remains a considerable challenge. Evidence shows that the dysregulation of alternative splicing (AS) events is involved in tumor pathogenesis and progression. The present study objective was to identify survival-associated AS events that could serve as prognostic biomarkers and potentially serve as tumor-selective STS drug targets. METHODS: STS-specific ‘percent spliced in’ (PSI) values for splicing events in 206 STS samples were downloaded from The Cancer Genome Atlas SpliceSeq(®) database. Prognostic analyses were performed on seven types of AS events to determine their prognostic value in STS patients, for which prediction models were constructed with the risk score formula [Formula: see text] . Prediction models were also constructed to determine the prognostic value of AS events, and Spearman’s rank correlation coefficients were calculated to determine the degree of correlation between splicing factor expression and the PSI values. RESULTS: A total 10,439 events were found to significantly correlate with patient survival rates. The area under the time-dependent receiver operating characteristic curve for the prognostic predictor of STS overall survival was 0.826. Notably, the splicing events of certain STS key genes were significantly associated with STS 2-year overall survival in the present study, including exon skip (ES) events in MDM2 and EWSR1, alternate terminator events in CDKN2A and HMGA2 for dedifferentiated liposarcoma, ES in MDM2 and alternate promoter events in CDKN2A for leiomyosarcoma, and ES in EWSR1 for undifferentiated pleomorphic sarcoma. Moreover, splicing correlation networks between AS events and splicing factors revealed that almost all of the AS events showed negatively correlations with the expression of splicing factors. CONCLUSION: An in-depth analysis of alternative RNA splicing could provide new insights into the mechanisms of STS oncogenesis and the potential for novel approaches to this type of cancer therapy.
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spelling pubmed-67082532019-08-28 Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas Yang, Xia Huang, Wen-ting He, Rong-quan Ma, Jie Lin, Peng Xie, Zu-cheng Ma, Fu-chao Chen, Gang J Transl Med Research BACKGROUND: Surgery, adjuvant chemotherapy, and radiotherapy are the primary treatment options for soft tissue sarcomas (STSs). However, identifying ways to improve the prognosis of patients with STS remains a considerable challenge. Evidence shows that the dysregulation of alternative splicing (AS) events is involved in tumor pathogenesis and progression. The present study objective was to identify survival-associated AS events that could serve as prognostic biomarkers and potentially serve as tumor-selective STS drug targets. METHODS: STS-specific ‘percent spliced in’ (PSI) values for splicing events in 206 STS samples were downloaded from The Cancer Genome Atlas SpliceSeq(®) database. Prognostic analyses were performed on seven types of AS events to determine their prognostic value in STS patients, for which prediction models were constructed with the risk score formula [Formula: see text] . Prediction models were also constructed to determine the prognostic value of AS events, and Spearman’s rank correlation coefficients were calculated to determine the degree of correlation between splicing factor expression and the PSI values. RESULTS: A total 10,439 events were found to significantly correlate with patient survival rates. The area under the time-dependent receiver operating characteristic curve for the prognostic predictor of STS overall survival was 0.826. Notably, the splicing events of certain STS key genes were significantly associated with STS 2-year overall survival in the present study, including exon skip (ES) events in MDM2 and EWSR1, alternate terminator events in CDKN2A and HMGA2 for dedifferentiated liposarcoma, ES in MDM2 and alternate promoter events in CDKN2A for leiomyosarcoma, and ES in EWSR1 for undifferentiated pleomorphic sarcoma. Moreover, splicing correlation networks between AS events and splicing factors revealed that almost all of the AS events showed negatively correlations with the expression of splicing factors. CONCLUSION: An in-depth analysis of alternative RNA splicing could provide new insights into the mechanisms of STS oncogenesis and the potential for novel approaches to this type of cancer therapy. BioMed Central 2019-08-23 /pmc/articles/PMC6708253/ /pubmed/31443718 http://dx.doi.org/10.1186/s12967-019-2029-6 Text en © The Author(s) 2019 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
Yang, Xia
Huang, Wen-ting
He, Rong-quan
Ma, Jie
Lin, Peng
Xie, Zu-cheng
Ma, Fu-chao
Chen, Gang
Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas
title Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas
title_full Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas
title_fullStr Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas
title_full_unstemmed Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas
title_short Determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from The Cancer Genome Atlas
title_sort determining the prognostic significance of alternative splicing events in soft tissue sarcoma using data from the cancer genome atlas
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6708253/
https://www.ncbi.nlm.nih.gov/pubmed/31443718
http://dx.doi.org/10.1186/s12967-019-2029-6
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