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Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma

BACKGROUND: Both N6-methyladenosine (m(6)A) and ferroptosis-related genes are associated with the prognosis of lung adenocarcinoma. However, the predictive value of m(6)A-related ferroptosis genes remains unclear. Here, we aimed to identify the prognostic value of m(6)A-related ferroptosis genes in...

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Autores principales: Li, Dongdong, Chen, Ting, Li, Qiu-Gen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111681/
https://www.ncbi.nlm.nih.gov/pubmed/37072786
http://dx.doi.org/10.1186/s12890-023-02410-x
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author Li, Dongdong
Chen, Ting
Li, Qiu-Gen
author_facet Li, Dongdong
Chen, Ting
Li, Qiu-Gen
author_sort Li, Dongdong
collection PubMed
description BACKGROUND: Both N6-methyladenosine (m(6)A) and ferroptosis-related genes are associated with the prognosis of lung adenocarcinoma. However, the predictive value of m(6)A-related ferroptosis genes remains unclear. Here, we aimed to identify the prognostic value of m(6)A-related ferroptosis genes in lung adenocarcinoma. METHODS: Lung adenocarcinoma sample data were downloaded from the University of California Santa Cruz Xena and Gene Expression Omnibus databases. Spearman’s correlation analysis was used to screen for m(6)A-related ferroptosis genes. Univariate Cox regression, Kaplan–Meier, and Lasso analyses were conducted to identify prognostic m(6)A-related ferroptosis genes, and stepwise regression was used to construct a prognostic gene signature. The predictive value of the gene signature was assessed using a multivariate Cox analysis. In the validation cohort, survival analysis was performed to verify gene signature stability. The training cohort was divided into high- and low-risk groups according to the median risk score to assess differences between the two groups in terms of gene set variation analysis, somatic mutations, and tumor immune infiltration cells. RESULTS: Six m(6)A-related ferroptosis genes were used to construct a gene signature in the training cohort and a multivariate Cox analysis was conducted to determine the independent prognostic value of these genes in lung adenocarcinoma. In the validation cohort, Kaplan–Meier and receiver operating characteristic analyses confirmed the strong predictive power of this signature for the prognosis of lung adenocarcinoma. Gene set variation analysis showed that the low-risk group was mainly related to immunity, and the high-risk group was mainly related to DNA replication. Somatic mutation analysis revealed that the TP53 gene had the highest mutation rate in the high-risk group. Tumor immune infiltration cell analysis showed that the low-risk group had higher levels of resting CD4 memory T cells and lower levels of M0 macrophages. CONCLUSION: Our study identified a novel m(6)A-related ferroptosis-associated six-gene signature (comprising SLC2A1, HERPUD1, EIF2S1, ACSL3, NCOA4, and CISD1) for predicting lung adenocarcinoma prognosis, yielding a useful prognostic biomarker and potential therapeutic target. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02410-x.
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spelling pubmed-101116812023-04-19 Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma Li, Dongdong Chen, Ting Li, Qiu-Gen BMC Pulm Med Research BACKGROUND: Both N6-methyladenosine (m(6)A) and ferroptosis-related genes are associated with the prognosis of lung adenocarcinoma. However, the predictive value of m(6)A-related ferroptosis genes remains unclear. Here, we aimed to identify the prognostic value of m(6)A-related ferroptosis genes in lung adenocarcinoma. METHODS: Lung adenocarcinoma sample data were downloaded from the University of California Santa Cruz Xena and Gene Expression Omnibus databases. Spearman’s correlation analysis was used to screen for m(6)A-related ferroptosis genes. Univariate Cox regression, Kaplan–Meier, and Lasso analyses were conducted to identify prognostic m(6)A-related ferroptosis genes, and stepwise regression was used to construct a prognostic gene signature. The predictive value of the gene signature was assessed using a multivariate Cox analysis. In the validation cohort, survival analysis was performed to verify gene signature stability. The training cohort was divided into high- and low-risk groups according to the median risk score to assess differences between the two groups in terms of gene set variation analysis, somatic mutations, and tumor immune infiltration cells. RESULTS: Six m(6)A-related ferroptosis genes were used to construct a gene signature in the training cohort and a multivariate Cox analysis was conducted to determine the independent prognostic value of these genes in lung adenocarcinoma. In the validation cohort, Kaplan–Meier and receiver operating characteristic analyses confirmed the strong predictive power of this signature for the prognosis of lung adenocarcinoma. Gene set variation analysis showed that the low-risk group was mainly related to immunity, and the high-risk group was mainly related to DNA replication. Somatic mutation analysis revealed that the TP53 gene had the highest mutation rate in the high-risk group. Tumor immune infiltration cell analysis showed that the low-risk group had higher levels of resting CD4 memory T cells and lower levels of M0 macrophages. CONCLUSION: Our study identified a novel m(6)A-related ferroptosis-associated six-gene signature (comprising SLC2A1, HERPUD1, EIF2S1, ACSL3, NCOA4, and CISD1) for predicting lung adenocarcinoma prognosis, yielding a useful prognostic biomarker and potential therapeutic target. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02410-x. BioMed Central 2023-04-18 /pmc/articles/PMC10111681/ /pubmed/37072786 http://dx.doi.org/10.1186/s12890-023-02410-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Li, Dongdong
Chen, Ting
Li, Qiu-Gen
Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma
title Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma
title_full Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma
title_fullStr Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma
title_full_unstemmed Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma
title_short Identification of a m(6)A-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma
title_sort identification of a m(6)a-related ferroptosis signature as a potential predictive biomarker for lung adenocarcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111681/
https://www.ncbi.nlm.nih.gov/pubmed/37072786
http://dx.doi.org/10.1186/s12890-023-02410-x
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