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A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer
Background: Numerous lncRNAs have been shown to affect colon cancer (CC) progression, and tumor necroptosis is regulated by several of them. However, the prognostic value of necroptosis-related lncRNA in CC has rarely been reported. In this study, a necroptosis-related lncRNA prognostic model was co...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453677/ https://www.ncbi.nlm.nih.gov/pubmed/36092933 http://dx.doi.org/10.3389/fgene.2022.984696 |
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author | Luo, Jian Peng, Jiayu Xiao, Wanying Huang, Shujing Cao, Yanqing Wang, Ting Wang, Xicheng |
author_facet | Luo, Jian Peng, Jiayu Xiao, Wanying Huang, Shujing Cao, Yanqing Wang, Ting Wang, Xicheng |
author_sort | Luo, Jian |
collection | PubMed |
description | Background: Numerous lncRNAs have been shown to affect colon cancer (CC) progression, and tumor necroptosis is regulated by several of them. However, the prognostic value of necroptosis-related lncRNA in CC has rarely been reported. In this study, a necroptosis-related lncRNA prognostic model was constructed, which can provide a reference for clinical diagnosis and treatment. Methods: The Cancer Genome Atlas (TCGA) database provided gene expression and lncRNA sequencing data for CC patients, and GSEA provided necroptosis gene data. Differentially expressed necroptosis-related lncRNAs related to prognosis were identified by differential expression analysis, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression. Based on the results of the multivariate COX regression analysis, a risk scoring model was constructed, A Kaplan-Meier analysis was performed to compare overall survival (OS) between low-risk and high-risk groups. A nomogram was then developed and validated based on the clinical data and risk scores of CC patients. In addition, Gene Set Enrichment Analysis (GSEA) and immune correlation analysis were conducted to explore the possible pathways and immune regulatory effects of these necroptosis-related lncRNAs. Results: In total, we identified 326 differentially expressed necroptosis-related lncRNAs in the TCGA database. Survival analysis showed that the OS of patients in the low-risk group was significantly better than that in the high-risk group (p < 0.05). Finally, 10 prognostic necroptosis-related lncRNAs were used to construct the nomogram. The composite nomogram prediction model evaluated and validated with good prediction performance (3-year AUC = 0.85, 5-years AUC = 0.82, C-index = 0.78). The GSEA and immune correlation analyses indicated that these lncRNAs may participate in multiple pathways involved in CC pathogenesis and progression. Conclusion: We established a novel necroptosis-related lncRNA CC prognosis prediction model, which can provide a reference for clinicians to formulate personalized treatment and review plans for CC patients. In addition, we also found that these necroptosis-related lncRNAs may affect the pathogenesis and progression of colon cancer through multiple pathways, including altering the activity of various immune cells. |
format | Online Article Text |
id | pubmed-9453677 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94536772022-09-09 A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer Luo, Jian Peng, Jiayu Xiao, Wanying Huang, Shujing Cao, Yanqing Wang, Ting Wang, Xicheng Front Genet Genetics Background: Numerous lncRNAs have been shown to affect colon cancer (CC) progression, and tumor necroptosis is regulated by several of them. However, the prognostic value of necroptosis-related lncRNA in CC has rarely been reported. In this study, a necroptosis-related lncRNA prognostic model was constructed, which can provide a reference for clinical diagnosis and treatment. Methods: The Cancer Genome Atlas (TCGA) database provided gene expression and lncRNA sequencing data for CC patients, and GSEA provided necroptosis gene data. Differentially expressed necroptosis-related lncRNAs related to prognosis were identified by differential expression analysis, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression. Based on the results of the multivariate COX regression analysis, a risk scoring model was constructed, A Kaplan-Meier analysis was performed to compare overall survival (OS) between low-risk and high-risk groups. A nomogram was then developed and validated based on the clinical data and risk scores of CC patients. In addition, Gene Set Enrichment Analysis (GSEA) and immune correlation analysis were conducted to explore the possible pathways and immune regulatory effects of these necroptosis-related lncRNAs. Results: In total, we identified 326 differentially expressed necroptosis-related lncRNAs in the TCGA database. Survival analysis showed that the OS of patients in the low-risk group was significantly better than that in the high-risk group (p < 0.05). Finally, 10 prognostic necroptosis-related lncRNAs were used to construct the nomogram. The composite nomogram prediction model evaluated and validated with good prediction performance (3-year AUC = 0.85, 5-years AUC = 0.82, C-index = 0.78). The GSEA and immune correlation analyses indicated that these lncRNAs may participate in multiple pathways involved in CC pathogenesis and progression. Conclusion: We established a novel necroptosis-related lncRNA CC prognosis prediction model, which can provide a reference for clinicians to formulate personalized treatment and review plans for CC patients. In addition, we also found that these necroptosis-related lncRNAs may affect the pathogenesis and progression of colon cancer through multiple pathways, including altering the activity of various immune cells. Frontiers Media S.A. 2022-08-25 /pmc/articles/PMC9453677/ /pubmed/36092933 http://dx.doi.org/10.3389/fgene.2022.984696 Text en Copyright © 2022 Luo, Peng, Xiao, Huang, Cao, Wang and Wang. 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 | Genetics Luo, Jian Peng, Jiayu Xiao, Wanying Huang, Shujing Cao, Yanqing Wang, Ting Wang, Xicheng A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer |
title | A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer |
title_full | A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer |
title_fullStr | A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer |
title_full_unstemmed | A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer |
title_short | A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer |
title_sort | novel necroptosis-related lncrna signature for predicting prognosis and immune response of colon cancer |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453677/ https://www.ncbi.nlm.nih.gov/pubmed/36092933 http://dx.doi.org/10.3389/fgene.2022.984696 |
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