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A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer

This study constructed a novel cuproptosis-related lncRNAs signature to predict the prognosis of BLCA patients. The Cancer Genome Atlas (TCGA) database was used to retrieve the RNA-seq data together with the relevant clinical information. The cuproptosis-related genes were first discovered. The cupr...

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Autores principales: Wu, Lingfeng, Chen, Wei, Cao, Yifang, Chen, Bin, He, Yi, Wang, Xueping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373974/
https://www.ncbi.nlm.nih.gov/pubmed/37424068
http://dx.doi.org/10.18632/aging.204861
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author Wu, Lingfeng
Chen, Wei
Cao, Yifang
Chen, Bin
He, Yi
Wang, Xueping
author_facet Wu, Lingfeng
Chen, Wei
Cao, Yifang
Chen, Bin
He, Yi
Wang, Xueping
author_sort Wu, Lingfeng
collection PubMed
description This study constructed a novel cuproptosis-related lncRNAs signature to predict the prognosis of BLCA patients. The Cancer Genome Atlas (TCGA) database was used to retrieve the RNA-seq data together with the relevant clinical information. The cuproptosis-related genes were first discovered. The cuproptosis-related lncRNAs were then acquired by univariate, the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to create a predictive signature. An eight cuproptosis-related lncRNAs (AC005261.1, AC008074.2, AC021321.1, AL024508.2, AL354919.2, ARHGAP5-AS1, LINC01106, LINC02446) predictive signature was created. Compared with the low-risk group, the prognosis was poorer for the high-risk group. The signature served as an independent overall survival (OS) predictor. Receiver operating characteristic (ROC) curve indicated that the signature demonstrated superior predictive ability, as evidenced by the area under the curve (AUC) of 0.782 than the clinicopathological variables. When we performed a subgroup analysis of the different variables, the high-risk group’s OS for BLCA patients was lower than that of the low-risk group’s patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk groups were clearly enriched in many immune-related biological processes and tumor-related signaling pathways. Single sample gene set enrichment analysis (ssGSEA) revealed that the immune infiltration level was different between the two groups. Finally, quantitative RT-PCR showed that AC005261.1, AC021321.1, AL024508.2, LINC02446 and LINC01106 were lowly expressed in tumor cells, while ARHGAP5-AS1 showed the opposite trend. In summary, the predictive signature can independently predict the prognosis and provide clinical treatment guidance for BLCA patients.
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spelling pubmed-103739742023-07-28 A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer Wu, Lingfeng Chen, Wei Cao, Yifang Chen, Bin He, Yi Wang, Xueping Aging (Albany NY) Research Paper This study constructed a novel cuproptosis-related lncRNAs signature to predict the prognosis of BLCA patients. The Cancer Genome Atlas (TCGA) database was used to retrieve the RNA-seq data together with the relevant clinical information. The cuproptosis-related genes were first discovered. The cuproptosis-related lncRNAs were then acquired by univariate, the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to create a predictive signature. An eight cuproptosis-related lncRNAs (AC005261.1, AC008074.2, AC021321.1, AL024508.2, AL354919.2, ARHGAP5-AS1, LINC01106, LINC02446) predictive signature was created. Compared with the low-risk group, the prognosis was poorer for the high-risk group. The signature served as an independent overall survival (OS) predictor. Receiver operating characteristic (ROC) curve indicated that the signature demonstrated superior predictive ability, as evidenced by the area under the curve (AUC) of 0.782 than the clinicopathological variables. When we performed a subgroup analysis of the different variables, the high-risk group’s OS for BLCA patients was lower than that of the low-risk group’s patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk groups were clearly enriched in many immune-related biological processes and tumor-related signaling pathways. Single sample gene set enrichment analysis (ssGSEA) revealed that the immune infiltration level was different between the two groups. Finally, quantitative RT-PCR showed that AC005261.1, AC021321.1, AL024508.2, LINC02446 and LINC01106 were lowly expressed in tumor cells, while ARHGAP5-AS1 showed the opposite trend. In summary, the predictive signature can independently predict the prognosis and provide clinical treatment guidance for BLCA patients. Impact Journals 2023-07-09 /pmc/articles/PMC10373974/ /pubmed/37424068 http://dx.doi.org/10.18632/aging.204861 Text en Copyright: © 2023 Wu et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Wu, Lingfeng
Chen, Wei
Cao, Yifang
Chen, Bin
He, Yi
Wang, Xueping
A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer
title A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer
title_full A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer
title_fullStr A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer
title_full_unstemmed A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer
title_short A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer
title_sort novel cuproptosis-related lncrnas signature predicts prognosis in bladder cancer
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373974/
https://www.ncbi.nlm.nih.gov/pubmed/37424068
http://dx.doi.org/10.18632/aging.204861
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