ArticleLipids in health and disease2023
Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease.
Article in Lipids in health and disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 12 citations in OpenAlex.
- MCD biomarkers Egfr, Hmox1, Lgmn identified in NAFLD.BMC endocrine disorders · 2026Article
- The multifaceted roles of microRNAs in nonalcoholic steatohepatitis: from pathogenic mechanisms to diagnostic and therapeutic opportunities.Frontiers in genetics · 2026Review
- Integrated bioinformatics and machine learning identify early diagnostic biomarkers for MAFLD with comorbid psoriasis.Frontiers in immunology · 2026Article
- Integrated bulk and single-cell transcriptomics identify RELB, S100A9, and SOCS1 as key autophagy-endoplasmic reticulum stress genes linking T2DM with MAFLD.Scientific reports · 2025Article
- From Gut to Heart: Targeting Trimethylamine N-Oxide as a Novel Strategy in Heart Failure Management.Biomolecules · 2025Review
- Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3.Hormones (Athens, Greece) · 2025Article
- Identification of tryptophan metabolism-related biomarkers for nonalcoholic fatty liver disease through network analysis.Endocrine connections · 2025Article
- Identification and Validation of Biomarkers in Metabolic Dysfunction-Associated Steatohepatitis Using Machine Learning and Bioinformatics.Molecular genetics & genomic medicine · 2025Article
- The gut microbiome in diabetic patients with hepatocellular carcinoma: distinct bacterial compositional shifts after hepatitis C virus eradication.Frontiers in microbiology · 2025Article
- Identification and Characterization of Genes Associated with Intestinal Ischemia-Reperfusion Injury and Oxidative Stress: A Bioinformatics and Experimental Approach Integrating High-Throughput Sequencing, Machine Learning, and Validation.Journal of inflammation research · 2025Article
- Article
- P4HA1: an important target for treating fibrosis related diseases and cancer.Frontiers in pharmacology · 2024Review
- Effect of Cytokeratin-18, C-peptide, MHR, and MACK-3 Biomarkers in Metabolic Dysfunction-Associated Fatty Liver Disease After Laparoscopic Sleeve Gastrectomy.Biomarker insights · 2024Article
- MicroRNAs and Nonalcoholic Steatohepatitis: A Review.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNonalcoholic 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.
methodsGSE48452, 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.
resultsFirst, 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.
conclusionMetabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD.
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