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A novel necroptosis signature for predicting survival in lung adenocarcinoma

BACKGROUND: To explore the necroptosis-related genes (NRGs) signature and its predictive values in lung adenocarcinoma (LUAD). METHODS: The training cohort consisted of tumor samples from The Cancer Genome Atlas, and the validation set comprised data from the Gene Expression Omnibus. Univariate and...

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Autores principales: Zang, Kui, Wang, Min, Zhu, Xingxing, Yao, Bin, Huang, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685572/
https://www.ncbi.nlm.nih.gov/pubmed/38017445
http://dx.doi.org/10.1186/s12920-023-01748-9
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author Zang, Kui
Wang, Min
Zhu, Xingxing
Yao, Bin
Huang, Ying
author_facet Zang, Kui
Wang, Min
Zhu, Xingxing
Yao, Bin
Huang, Ying
author_sort Zang, Kui
collection PubMed
description BACKGROUND: To explore the necroptosis-related genes (NRGs) signature and its predictive values in lung adenocarcinoma (LUAD). METHODS: The training cohort consisted of tumor samples from The Cancer Genome Atlas, and the validation set comprised data from the Gene Expression Omnibus. Univariate and multivariate Cox regression analyses were applied to identify the prognostic NRG signature as an independent molecular indicator. Correlation analysis was used for the association assessment between the NRG signature and immune checkpoint molecules. RESULTS: NRGs involved in necroptosis and immune NOD-like receptor signaling. The NRG signature based on eight NRGs can divide tumors into high-risk and low-risk groups, which was significantly associated with worse survival. Multivariate Cox regression analysis showed that this NRG signature remained an independent prognostic indicator. Stratification analyses demonstrated that this NRG signature was still effective for predicting survival in each stratum of age, gender, and tumor stage. The ROC curve showed a good predictive ability using the NRG signature in the validation cohort (AUC = 0.81). The NRG signature was related to immune checkpoint molecules PD − 1, PD-L1, and PD-L2. CONCLUSIONS: The NRG signature could be a novel predictor of the prognosis and may become a potential therapeutic target in LUAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01748-9.
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spelling pubmed-106855722023-11-30 A novel necroptosis signature for predicting survival in lung adenocarcinoma Zang, Kui Wang, Min Zhu, Xingxing Yao, Bin Huang, Ying BMC Med Genomics Research BACKGROUND: To explore the necroptosis-related genes (NRGs) signature and its predictive values in lung adenocarcinoma (LUAD). METHODS: The training cohort consisted of tumor samples from The Cancer Genome Atlas, and the validation set comprised data from the Gene Expression Omnibus. Univariate and multivariate Cox regression analyses were applied to identify the prognostic NRG signature as an independent molecular indicator. Correlation analysis was used for the association assessment between the NRG signature and immune checkpoint molecules. RESULTS: NRGs involved in necroptosis and immune NOD-like receptor signaling. The NRG signature based on eight NRGs can divide tumors into high-risk and low-risk groups, which was significantly associated with worse survival. Multivariate Cox regression analysis showed that this NRG signature remained an independent prognostic indicator. Stratification analyses demonstrated that this NRG signature was still effective for predicting survival in each stratum of age, gender, and tumor stage. The ROC curve showed a good predictive ability using the NRG signature in the validation cohort (AUC = 0.81). The NRG signature was related to immune checkpoint molecules PD − 1, PD-L1, and PD-L2. CONCLUSIONS: The NRG signature could be a novel predictor of the prognosis and may become a potential therapeutic target in LUAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01748-9. BioMed Central 2023-11-28 /pmc/articles/PMC10685572/ /pubmed/38017445 http://dx.doi.org/10.1186/s12920-023-01748-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Zang, Kui
Wang, Min
Zhu, Xingxing
Yao, Bin
Huang, Ying
A novel necroptosis signature for predicting survival in lung adenocarcinoma
title A novel necroptosis signature for predicting survival in lung adenocarcinoma
title_full A novel necroptosis signature for predicting survival in lung adenocarcinoma
title_fullStr A novel necroptosis signature for predicting survival in lung adenocarcinoma
title_full_unstemmed A novel necroptosis signature for predicting survival in lung adenocarcinoma
title_short A novel necroptosis signature for predicting survival in lung adenocarcinoma
title_sort novel necroptosis signature for predicting survival in lung adenocarcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685572/
https://www.ncbi.nlm.nih.gov/pubmed/38017445
http://dx.doi.org/10.1186/s12920-023-01748-9
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