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Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning

BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations su...

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Autores principales: Wang, Yixin, Wang, Qiaozhu, Liu, Peng, Jin, Lingyan, Qin, Xinghua, Zheng, Qiangsun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464108/
https://www.ncbi.nlm.nih.gov/pubmed/37620966
http://dx.doi.org/10.1186/s41065-023-00297-6
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author Wang, Yixin
Wang, Qiaozhu
Liu, Peng
Jin, Lingyan
Qin, Xinghua
Zheng, Qiangsun
author_facet Wang, Yixin
Wang, Qiaozhu
Liu, Peng
Jin, Lingyan
Qin, Xinghua
Zheng, Qiangsun
author_sort Wang, Yixin
collection PubMed
description BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis. RESULTS: Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman’s rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4(+) T cells. Conversely, a negative correlation was detected between LIAS and CD8(+) T cells and regulatory T cells. CONCLUSIONS: This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-023-00297-6.
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spelling pubmed-104641082023-08-30 Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning Wang, Yixin Wang, Qiaozhu Liu, Peng Jin, Lingyan Qin, Xinghua Zheng, Qiangsun Hereditas Research BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis. RESULTS: Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman’s rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4(+) T cells. Conversely, a negative correlation was detected between LIAS and CD8(+) T cells and regulatory T cells. CONCLUSIONS: This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41065-023-00297-6. BioMed Central 2023-08-24 /pmc/articles/PMC10464108/ /pubmed/37620966 http://dx.doi.org/10.1186/s41065-023-00297-6 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
Wang, Yixin
Wang, Qiaozhu
Liu, Peng
Jin, Lingyan
Qin, Xinghua
Zheng, Qiangsun
Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning
title Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning
title_full Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning
title_fullStr Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning
title_full_unstemmed Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning
title_short Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning
title_sort construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464108/
https://www.ncbi.nlm.nih.gov/pubmed/37620966
http://dx.doi.org/10.1186/s41065-023-00297-6
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