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Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs

BACKGROUND: In reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the t...

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Autores principales: Liu, Dongze, Xu, Shengxian, Chang, Taihao, Ma, Shenfei, Wang, Kaibin, Sun, Guangyu, Chen, Shuaiqi, Xu, Yong, Zhang, Hongtuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289196/
https://www.ncbi.nlm.nih.gov/pubmed/35860239
http://dx.doi.org/10.3389/fimmu.2022.916800
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author Liu, Dongze
Xu, Shengxian
Chang, Taihao
Ma, Shenfei
Wang, Kaibin
Sun, Guangyu
Chen, Shuaiqi
Xu, Yong
Zhang, Hongtuan
author_facet Liu, Dongze
Xu, Shengxian
Chang, Taihao
Ma, Shenfei
Wang, Kaibin
Sun, Guangyu
Chen, Shuaiqi
Xu, Yong
Zhang, Hongtuan
author_sort Liu, Dongze
collection PubMed
description BACKGROUND: In reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the tumor microenvironment (TME) and the immunotherapeutic response for accurate dose. METHODS: To obtain the required data, bladder urothelial carcinoma transcriptome data were searched from Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). We used co-expression analysis, differential expression analysis, and univariate Cox regression to screen out prognostic lncRNAs associated with necroptosis in BLCA. Then the least absolute shrinkage and selection operator (LASSO) was conducted to construct the necroptosis-associated lncRNAs model. Based on this model, we also performed the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) to estimate the prognostic power of risk score. Multivariate and univariate Cox regression analysis were performed to build up a nomogram. Calibration curves, and time-dependent ROC were also conducted to evaluate nomogram. Principal component analysis (PCA) revealed a difference between high- and low-risk groups. In addition, we explored immune analysis, gene set enrichment analyses (GSEA), and evaluation of the half-maximal inhibitory concentration (IC50) in constructed model. Finally, the entire samples were divided into three clusters based on model of necroptosis-associated lncRNAs to further compare immunotherapy in cold and hot tumors. RESULTS: A model was built up based on necroptosis-associated lncRNAs. The model revealed good consistence between calibration plots and prognostic prediction. The area of 1-, 3-, and 5-year OS under the ROC curve (AUC) were 0.707, 0.679, and 0.675. Risk groups could be helpful for systemic therapy due to the markedly diverse IC50 between risk groups. To our delight, clusters could effectively identify cold and hot tumors, which would be beneficial to accurate mediation. Clusters 2 and 3 were considered the hot tumor, which was more sensitive to immunotherapeutic drugs. CONCLUSIONS: The outcomes of our study suggested that necroptosis-associated lncRNAs could effectively predict patients with BLCA prognosis, which may be helpful for distinguishing the cold and hot tumors and improving individual treatment of BLCA.
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spelling pubmed-92891962022-07-19 Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs Liu, Dongze Xu, Shengxian Chang, Taihao Ma, Shenfei Wang, Kaibin Sun, Guangyu Chen, Shuaiqi Xu, Yong Zhang, Hongtuan Front Immunol Immunology BACKGROUND: In reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the tumor microenvironment (TME) and the immunotherapeutic response for accurate dose. METHODS: To obtain the required data, bladder urothelial carcinoma transcriptome data were searched from Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). We used co-expression analysis, differential expression analysis, and univariate Cox regression to screen out prognostic lncRNAs associated with necroptosis in BLCA. Then the least absolute shrinkage and selection operator (LASSO) was conducted to construct the necroptosis-associated lncRNAs model. Based on this model, we also performed the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) to estimate the prognostic power of risk score. Multivariate and univariate Cox regression analysis were performed to build up a nomogram. Calibration curves, and time-dependent ROC were also conducted to evaluate nomogram. Principal component analysis (PCA) revealed a difference between high- and low-risk groups. In addition, we explored immune analysis, gene set enrichment analyses (GSEA), and evaluation of the half-maximal inhibitory concentration (IC50) in constructed model. Finally, the entire samples were divided into three clusters based on model of necroptosis-associated lncRNAs to further compare immunotherapy in cold and hot tumors. RESULTS: A model was built up based on necroptosis-associated lncRNAs. The model revealed good consistence between calibration plots and prognostic prediction. The area of 1-, 3-, and 5-year OS under the ROC curve (AUC) were 0.707, 0.679, and 0.675. Risk groups could be helpful for systemic therapy due to the markedly diverse IC50 between risk groups. To our delight, clusters could effectively identify cold and hot tumors, which would be beneficial to accurate mediation. Clusters 2 and 3 were considered the hot tumor, which was more sensitive to immunotherapeutic drugs. CONCLUSIONS: The outcomes of our study suggested that necroptosis-associated lncRNAs could effectively predict patients with BLCA prognosis, which may be helpful for distinguishing the cold and hot tumors and improving individual treatment of BLCA. Frontiers Media S.A. 2022-07-04 /pmc/articles/PMC9289196/ /pubmed/35860239 http://dx.doi.org/10.3389/fimmu.2022.916800 Text en Copyright © 2022 Liu, Xu, Chang, Ma, Wang, Sun, Chen, Xu and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Liu, Dongze
Xu, Shengxian
Chang, Taihao
Ma, Shenfei
Wang, Kaibin
Sun, Guangyu
Chen, Shuaiqi
Xu, Yong
Zhang, Hongtuan
Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs
title Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs
title_full Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs
title_fullStr Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs
title_full_unstemmed Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs
title_short Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs
title_sort predicting prognosis and distinguishing cold and hot tumors in bladder urothelial carcinoma based on necroptosis-associated lncrnas
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289196/
https://www.ncbi.nlm.nih.gov/pubmed/35860239
http://dx.doi.org/10.3389/fimmu.2022.916800
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