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Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients. METHODS:...

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Autores principales: Jiang, Hua, Hu, Yang, Zhang, Zhibo, Chen, Xujia, Gao, Jianpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494330/
https://www.ncbi.nlm.nih.gov/pubmed/37697333
http://dx.doi.org/10.1186/s12944-023-01911-2
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author Jiang, Hua
Hu, Yang
Zhang, Zhibo
Chen, Xujia
Gao, Jianpeng
author_facet Jiang, Hua
Hu, Yang
Zhang, Zhibo
Chen, Xujia
Gao, Jianpeng
author_sort Jiang, Hua
collection PubMed
description BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients. METHODS: GSE48452, GSE63067, and GSE89632 datasets including nonalcoholic steatohepatitis (NASH) and healthy controls (HC) analyzed in this study were retrieved from the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) between NASH and HC samples were obtained. Next, metabolism-related DEGs (MR-DEGs) were identified by overlapping DEGs and metabolism-related genes (MRG). Further, a protein–protein interaction (PPI) network was developed to show the interaction among MR-DEGs. Subsequently, the “Least absolute shrinkage and selection operator regression” and “Random Forest” algorithms were used to screen metabolism-related genes (MRGs) in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses (GSEA) were performed on these metabolism-related genes. Finally, the expression of metabolism-related gene was determined at the transcription level. RESULTS: First, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1, were identified using machine learning algorithms. Moreover, a regulatory network consisting of four genes, 25 microRNAs (miRNAs), and 41 transcription factors (TFs) was constructed. Finally, a significant increase in FMO1 and LPL expression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the HC group. CONCLUSION: Metabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12944-023-01911-2.
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spelling pubmed-104943302023-09-12 Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease Jiang, Hua Hu, Yang Zhang, Zhibo Chen, Xujia Gao, Jianpeng Lipids Health Dis Research BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients. METHODS: GSE48452, GSE63067, and GSE89632 datasets including nonalcoholic steatohepatitis (NASH) and healthy controls (HC) analyzed in this study were retrieved from the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) between NASH and HC samples were obtained. Next, metabolism-related DEGs (MR-DEGs) were identified by overlapping DEGs and metabolism-related genes (MRG). Further, a protein–protein interaction (PPI) network was developed to show the interaction among MR-DEGs. Subsequently, the “Least absolute shrinkage and selection operator regression” and “Random Forest” algorithms were used to screen metabolism-related genes (MRGs) in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses (GSEA) were performed on these metabolism-related genes. Finally, the expression of metabolism-related gene was determined at the transcription level. RESULTS: First, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1, were identified using machine learning algorithms. Moreover, a regulatory network consisting of four genes, 25 microRNAs (miRNAs), and 41 transcription factors (TFs) was constructed. Finally, a significant increase in FMO1 and LPL expression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the HC group. CONCLUSION: Metabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12944-023-01911-2. BioMed Central 2023-09-11 /pmc/articles/PMC10494330/ /pubmed/37697333 http://dx.doi.org/10.1186/s12944-023-01911-2 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
Jiang, Hua
Hu, Yang
Zhang, Zhibo
Chen, Xujia
Gao, Jianpeng
Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease
title Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease
title_full Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease
title_fullStr Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease
title_full_unstemmed Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease
title_short Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease
title_sort identification of metabolic biomarkers associated with nonalcoholic fatty liver disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494330/
https://www.ncbi.nlm.nih.gov/pubmed/37697333
http://dx.doi.org/10.1186/s12944-023-01911-2
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