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Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning

BACKGROUND AND AIMS: Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). METHOD: The gene expression profiles of NAFLD we...

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Autores principales: Ouyang, Guoqing, Wu, Zhan, Liu, Zhipeng, Pan, Guandong, Wang, Yong, Liu, Jing, Guo, Jixu, Liu, Tao, Huang, Guozhen, Zeng, Yonglian, Wei, Zaiwa, He, Songqing, Yuan, Guandou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562635/
https://www.ncbi.nlm.nih.gov/pubmed/37822923
http://dx.doi.org/10.3389/fimmu.2023.1251750
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author Ouyang, Guoqing
Wu, Zhan
Liu, Zhipeng
Pan, Guandong
Wang, Yong
Liu, Jing
Guo, Jixu
Liu, Tao
Huang, Guozhen
Zeng, Yonglian
Wei, Zaiwa
He, Songqing
Yuan, Guandou
author_facet Ouyang, Guoqing
Wu, Zhan
Liu, Zhipeng
Pan, Guandong
Wang, Yong
Liu, Jing
Guo, Jixu
Liu, Tao
Huang, Guozhen
Zeng, Yonglian
Wei, Zaiwa
He, Songqing
Yuan, Guandou
author_sort Ouyang, Guoqing
collection PubMed
description BACKGROUND AND AIMS: Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). METHOD: The gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein–protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes. RESULTS: Four datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAFLD. CIBERSORT and single-sample gene set enrichment analysis indicated that their expression levels in macrophages, mast cells, NK cells, Treg cells, resting dendritic cells, and tumor-infiltrating lymphocytes were higher in NAFLD compared with control liver samples. The ceRNA network demonstrated a complex regulatory relationship between the three hub genes. The mRNA level of these hub genes were further confirmed in a mouse NAFLD liver samples. CONCLUSION: Our study comprehensively demonstrated the relationship between NAFLD and cuproptosis, developed a promising diagnostic model, and provided potential targets for NAFLD treatment and new insights for exploring the mechanism for NAFLD.
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spelling pubmed-105626352023-10-11 Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning Ouyang, Guoqing Wu, Zhan Liu, Zhipeng Pan, Guandong Wang, Yong Liu, Jing Guo, Jixu Liu, Tao Huang, Guozhen Zeng, Yonglian Wei, Zaiwa He, Songqing Yuan, Guandou Front Immunol Immunology BACKGROUND AND AIMS: Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). METHOD: The gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein–protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes. RESULTS: Four datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAFLD. CIBERSORT and single-sample gene set enrichment analysis indicated that their expression levels in macrophages, mast cells, NK cells, Treg cells, resting dendritic cells, and tumor-infiltrating lymphocytes were higher in NAFLD compared with control liver samples. The ceRNA network demonstrated a complex regulatory relationship between the three hub genes. The mRNA level of these hub genes were further confirmed in a mouse NAFLD liver samples. CONCLUSION: Our study comprehensively demonstrated the relationship between NAFLD and cuproptosis, developed a promising diagnostic model, and provided potential targets for NAFLD treatment and new insights for exploring the mechanism for NAFLD. Frontiers Media S.A. 2023-09-26 /pmc/articles/PMC10562635/ /pubmed/37822923 http://dx.doi.org/10.3389/fimmu.2023.1251750 Text en Copyright © 2023 Ouyang, Wu, Liu, Pan, Wang, Liu, Guo, Liu, Huang, Zeng, Wei, He and Yuan 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 Immunology
Ouyang, Guoqing
Wu, Zhan
Liu, Zhipeng
Pan, Guandong
Wang, Yong
Liu, Jing
Guo, Jixu
Liu, Tao
Huang, Guozhen
Zeng, Yonglian
Wei, Zaiwa
He, Songqing
Yuan, Guandou
Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning
title Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning
title_full Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning
title_fullStr Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning
title_full_unstemmed Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning
title_short Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning
title_sort identification and validation of potential diagnostic signature and immune cell infiltration for nafld based on cuproptosis-related genes by bioinformatics analysis and machine learning
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562635/
https://www.ncbi.nlm.nih.gov/pubmed/37822923
http://dx.doi.org/10.3389/fimmu.2023.1251750
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