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A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer

BACKGROUND: Inflammation and long noncoding RNAs (lncRNAs) are gradually becoming important in the development of bladder cancer (BC). Nevertheless, the potential of inflammatory response-related lncRNAs (IRRlncRNAs) as a prognostic signature remains unexplored in BC. METHODS: The Cancer Genome Atla...

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Autores principales: Tang, Fucai, Zhang, Jiahao, Lu, Zechao, Liao, Haiqin, Hu, Chuxian, Mai, Yuexue, Lai, Yongchang, Lu, Zeguang, Tang, Zhicheng, Li, Zhibiao, He, Zhaohui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375404/
https://www.ncbi.nlm.nih.gov/pubmed/35964079
http://dx.doi.org/10.1186/s41065-022-00245-w
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author Tang, Fucai
Zhang, Jiahao
Lu, Zechao
Liao, Haiqin
Hu, Chuxian
Mai, Yuexue
Lai, Yongchang
Lu, Zeguang
Tang, Zhicheng
Li, Zhibiao
He, Zhaohui
author_facet Tang, Fucai
Zhang, Jiahao
Lu, Zechao
Liao, Haiqin
Hu, Chuxian
Mai, Yuexue
Lai, Yongchang
Lu, Zeguang
Tang, Zhicheng
Li, Zhibiao
He, Zhaohui
author_sort Tang, Fucai
collection PubMed
description BACKGROUND: Inflammation and long noncoding RNAs (lncRNAs) are gradually becoming important in the development of bladder cancer (BC). Nevertheless, the potential of inflammatory response-related lncRNAs (IRRlncRNAs) as a prognostic signature remains unexplored in BC. METHODS: The Cancer Genome Atlas (TCGA) provided RNA expression profiles and clinical information of BC samples, and GSEA Molecular Signatures database provided 1171 inflammation-related genes. IRRlncRNAs were identified using Pearson correlation analysis. After that, consensus clustering was performed to form molecular subtypes. After performing least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, a risk model constructed based on the prognostic IRRlncRNAs was validated in an independent cohort. Kaplan–Meier (KM) analysis, univariate and multivariate Cox regression, clinical stratification analysis, and time-dependent receiver operating characteristic (ROC) curves were utilized to assess clinical effectiveness and accuracy of the risk model. In clusters and risk model, functional enrichment was investigated using GSEA and GSVA, and immune cell infiltration analysis was demonstrated by ESTIMATE and CIBERSORT analysis. RESULTS: A total of 174 prognostic IRRlncRNAs were confirmed, and 406 samples were divided into 2 clusters, with cluster 2 having a significantly inferior prognosis. Moreover, cluster 2 exhibited a higher ESTIMATE score, immune infiltration, and PD-L1 expression, with close relationships with the inflammatory response. Further, 12 IRRlncRNAs were identified and applied to construct the risk model and divide BC samples into low-risk and high-risk groups successfully. KM, ROC, and clinical stratification analysis demonstrated that the risk model performed well in predicting prognosis. The risk score was identified as an independently significant indicator, enriched in immune, cell cycle, and apoptosis-related pathways, and correlated with 9 immune cells. CONCLUSION: We developed an inflammatory response-related subtypes and steady prognostic risk model based on 12 IRRlncRNAs, which was valuable for individual prognostic prediction and stratification and outfitted new insight into inflammatory response in BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-022-00245-w.
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spelling pubmed-93754042022-08-14 A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer Tang, Fucai Zhang, Jiahao Lu, Zechao Liao, Haiqin Hu, Chuxian Mai, Yuexue Lai, Yongchang Lu, Zeguang Tang, Zhicheng Li, Zhibiao He, Zhaohui Hereditas Research BACKGROUND: Inflammation and long noncoding RNAs (lncRNAs) are gradually becoming important in the development of bladder cancer (BC). Nevertheless, the potential of inflammatory response-related lncRNAs (IRRlncRNAs) as a prognostic signature remains unexplored in BC. METHODS: The Cancer Genome Atlas (TCGA) provided RNA expression profiles and clinical information of BC samples, and GSEA Molecular Signatures database provided 1171 inflammation-related genes. IRRlncRNAs were identified using Pearson correlation analysis. After that, consensus clustering was performed to form molecular subtypes. After performing least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, a risk model constructed based on the prognostic IRRlncRNAs was validated in an independent cohort. Kaplan–Meier (KM) analysis, univariate and multivariate Cox regression, clinical stratification analysis, and time-dependent receiver operating characteristic (ROC) curves were utilized to assess clinical effectiveness and accuracy of the risk model. In clusters and risk model, functional enrichment was investigated using GSEA and GSVA, and immune cell infiltration analysis was demonstrated by ESTIMATE and CIBERSORT analysis. RESULTS: A total of 174 prognostic IRRlncRNAs were confirmed, and 406 samples were divided into 2 clusters, with cluster 2 having a significantly inferior prognosis. Moreover, cluster 2 exhibited a higher ESTIMATE score, immune infiltration, and PD-L1 expression, with close relationships with the inflammatory response. Further, 12 IRRlncRNAs were identified and applied to construct the risk model and divide BC samples into low-risk and high-risk groups successfully. KM, ROC, and clinical stratification analysis demonstrated that the risk model performed well in predicting prognosis. The risk score was identified as an independently significant indicator, enriched in immune, cell cycle, and apoptosis-related pathways, and correlated with 9 immune cells. CONCLUSION: We developed an inflammatory response-related subtypes and steady prognostic risk model based on 12 IRRlncRNAs, which was valuable for individual prognostic prediction and stratification and outfitted new insight into inflammatory response in BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-022-00245-w. BioMed Central 2022-08-13 /pmc/articles/PMC9375404/ /pubmed/35964079 http://dx.doi.org/10.1186/s41065-022-00245-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Tang, Fucai
Zhang, Jiahao
Lu, Zechao
Liao, Haiqin
Hu, Chuxian
Mai, Yuexue
Lai, Yongchang
Lu, Zeguang
Tang, Zhicheng
Li, Zhibiao
He, Zhaohui
A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer
title A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer
title_full A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer
title_fullStr A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer
title_full_unstemmed A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer
title_short A novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer
title_sort novel molecular subtypes and risk model based on inflammatory response-related lncrnas for bladder cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375404/
https://www.ncbi.nlm.nih.gov/pubmed/35964079
http://dx.doi.org/10.1186/s41065-022-00245-w
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