Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence

Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV)...

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Autores principales:	Zhipeng Xu, Hui Chen, Jin Sun, Weipu Mao, Shuqiu Chen, Ming Chen
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	multi-omics data copy number variation mutation bladder cancer recurrence long non-coding rnas1 Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/5cfa0496cd654b00b08f725b529112a1
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spelling	oai:doaj.org-article:5cfa0496cd654b00b08f725b529112a12021-12-01T14:41:00ZMulti-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence2165-59792165-598710.1080/21655979.2021.2000122https://doaj.org/article/5cfa0496cd654b00b08f725b529112a12021-12-01T00:00:00Zhttp://dx.doi.org/10.1080/21655979.2021.2000122https://doaj.org/toc/2165-5979https://doaj.org/toc/2165-5987Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV) data, mutation annotation files, RNA expression profiles and clinical data of The Cancer Genome Atlas (TCGA) BLCA cohort (303 cases) and GSE31684 (93 cases) were downloaded from public database. With multi-omics analysis, twenty lncRNAs were identified as the candidates related with BLCA recurrence, CNVs and mutations in training set. Ten-lncRNA signature were established using least absolute shrinkage and selection operation (LASSO) and Cox regression. Then, various survival analysis was used to assess the power of lncRNA model in predicting BLCA recurrence. The results showed that the recurrence-free survival time of high-risk group was significantly shorter than that of low-risk group in training and testing sets, and the predictive value of ten-lncRNA signature was robust and independent of other clinical variables. Gene Set Enrichment Analysis (GSEA) showed this signature were associated with immune disorders, indicating this signature may be involved in tumor immunology. After compared with the other reported lncRNA signatures, ten-lncRNA signature was validated as a superior prognostic model in predicting the recurrence of BLCA. The effectiveness of the model was also evaluated in bladder cancer samples via qRT-PCR. Thus, the novel ten-lncRNA signature, constructed based on multi-omics data, had robust prognostic power in predicting the recurrence of BLCA and potential clinical implications as biomarkers.Zhipeng XuHui ChenJin SunWeipu MaoShuqiu ChenMing ChenTaylor & Francis Grouparticlemulti-omics datacopy number variationmutationbladder cancer recurrencelong non-coding rnas1BiotechnologyTP248.13-248.65ENBioengineered, Vol 12, Iss 2, Pp 11108-11125 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-omics data copy number variation mutation bladder cancer recurrence long non-coding rnas1 Biotechnology TP248.13-248.65
spellingShingle	multi-omics data copy number variation mutation bladder cancer recurrence long non-coding rnas1 Biotechnology TP248.13-248.65 Zhipeng Xu Hui Chen Jin Sun Weipu Mao Shuqiu Chen Ming Chen Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence
description	Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV) data, mutation annotation files, RNA expression profiles and clinical data of The Cancer Genome Atlas (TCGA) BLCA cohort (303 cases) and GSE31684 (93 cases) were downloaded from public database. With multi-omics analysis, twenty lncRNAs were identified as the candidates related with BLCA recurrence, CNVs and mutations in training set. Ten-lncRNA signature were established using least absolute shrinkage and selection operation (LASSO) and Cox regression. Then, various survival analysis was used to assess the power of lncRNA model in predicting BLCA recurrence. The results showed that the recurrence-free survival time of high-risk group was significantly shorter than that of low-risk group in training and testing sets, and the predictive value of ten-lncRNA signature was robust and independent of other clinical variables. Gene Set Enrichment Analysis (GSEA) showed this signature were associated with immune disorders, indicating this signature may be involved in tumor immunology. After compared with the other reported lncRNA signatures, ten-lncRNA signature was validated as a superior prognostic model in predicting the recurrence of BLCA. The effectiveness of the model was also evaluated in bladder cancer samples via qRT-PCR. Thus, the novel ten-lncRNA signature, constructed based on multi-omics data, had robust prognostic power in predicting the recurrence of BLCA and potential clinical implications as biomarkers.
format	article
author	Zhipeng Xu Hui Chen Jin Sun Weipu Mao Shuqiu Chen Ming Chen
author_facet	Zhipeng Xu Hui Chen Jin Sun Weipu Mao Shuqiu Chen Ming Chen
author_sort	Zhipeng Xu
title	Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence
title_short	Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence
title_full	Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence
title_fullStr	Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence
title_full_unstemmed	Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence
title_sort	multi-omics analysis identifies a lncrna-related prognostic signature to predict bladder cancer recurrence
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/5cfa0496cd654b00b08f725b529112a1
work_keys_str_mv	AT zhipengxu multiomicsanalysisidentifiesalncrnarelatedprognosticsignaturetopredictbladdercancerrecurrence AT huichen multiomicsanalysisidentifiesalncrnarelatedprognosticsignaturetopredictbladdercancerrecurrence AT jinsun multiomicsanalysisidentifiesalncrnarelatedprognosticsignaturetopredictbladdercancerrecurrence AT weipumao multiomicsanalysisidentifiesalncrnarelatedprognosticsignaturetopredictbladdercancerrecurrence AT shuqiuchen multiomicsanalysisidentifiesalncrnarelatedprognosticsignaturetopredictbladdercancerrecurrence AT mingchen multiomicsanalysisidentifiesalncrnarelatedprognosticsignaturetopredictbladdercancerrecurrence
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Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence

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