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Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NA...

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Autores principales: Han, Na, He, Juan, Shi, Lixin, Zhang, Miao, Zheng, Jing, Fan, Yuanshuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676265/
https://www.ncbi.nlm.nih.gov/pubmed/36419827
http://dx.doi.org/10.3389/fgene.2022.1020899
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author Han, Na
He, Juan
Shi, Lixin
Zhang, Miao
Zheng, Jing
Fan, Yuanshuo
author_facet Han, Na
He, Juan
Shi, Lixin
Zhang, Miao
Zheng, Jing
Fan, Yuanshuo
author_sort Han, Na
collection PubMed
description Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD.
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spelling pubmed-96762652022-11-22 Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study Han, Na He, Juan Shi, Lixin Zhang, Miao Zheng, Jing Fan, Yuanshuo Front Genet Genetics Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD. Frontiers Media S.A. 2022-11-07 /pmc/articles/PMC9676265/ /pubmed/36419827 http://dx.doi.org/10.3389/fgene.2022.1020899 Text en Copyright © 2022 Han, He, Shi, Zhang, Zheng and Fan. 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 Genetics
Han, Na
He, Juan
Shi, Lixin
Zhang, Miao
Zheng, Jing
Fan, Yuanshuo
Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study
title Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study
title_full Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study
title_fullStr Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study
title_full_unstemmed Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study
title_short Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study
title_sort identification of biomarkers in nonalcoholic fatty liver disease: a machine learning method and experimental study
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676265/
https://www.ncbi.nlm.nih.gov/pubmed/36419827
http://dx.doi.org/10.3389/fgene.2022.1020899
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