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Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs
BACKGROUND: Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study th...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840007/ https://www.ncbi.nlm.nih.gov/pubmed/36647499 http://dx.doi.org/10.21037/jtd-22-1534 |
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author | Zheng, Miaosen Zhou, Hao Xie, Jing Zhang, Haifeng Shen, Xiaojian Zhu, Dongbing |
author_facet | Zheng, Miaosen Zhou, Hao Xie, Jing Zhang, Haifeng Shen, Xiaojian Zhu, Dongbing |
author_sort | Zheng, Miaosen |
collection | PubMed |
description | BACKGROUND: Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD. METHODS: The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the “ConsensusClusterPlus” package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy. RESULTS: Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group. CONCLUSIONS: Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis. |
format | Online Article Text |
id | pubmed-9840007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-98400072023-01-15 Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs Zheng, Miaosen Zhou, Hao Xie, Jing Zhang, Haifeng Shen, Xiaojian Zhu, Dongbing J Thorac Dis Original Article BACKGROUND: Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD. METHODS: The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the “ConsensusClusterPlus” package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy. RESULTS: Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group. CONCLUSIONS: Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis. AME Publishing Company 2022-12 /pmc/articles/PMC9840007/ /pubmed/36647499 http://dx.doi.org/10.21037/jtd-22-1534 Text en 2022 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Zheng, Miaosen Zhou, Hao Xie, Jing Zhang, Haifeng Shen, Xiaojian Zhu, Dongbing Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs |
title | Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs |
title_full | Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs |
title_fullStr | Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs |
title_full_unstemmed | Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs |
title_short | Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs |
title_sort | molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncrnas |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840007/ https://www.ncbi.nlm.nih.gov/pubmed/36647499 http://dx.doi.org/10.21037/jtd-22-1534 |
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