ArticleJournal of translational medicine2023
Identifying a distinct fibrosis subset of NAFLD via molecular profiling and the involvement of profibrotic macrophages.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH.NPJ digital medicine · 2026Article
- The Role of Kupffer Cells and Liver Macrophages in the Pathogenesis of Metabolic Dysfunction-Associated Steatotic Liver Disease.Biomedicines · 2026Review
- Mechanisms underlying the role of the oral-gut-liver axis in the promotion of liver fibrosis by periodontal disease: an interacting network of inflammation, microbiota, and barrier function.Frontiers in cellular and infection microbiology · 2026Review
- Liver diseases: epidemiology, causes, trends and predictions.Signal transduction and targeted therapy · 2025Review
- Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis.Scientific reports · 2024Article
- Myeloid cell MHC I expression drives CD8Frontiers in immunology · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThere are emerging studies suggesting that non-alcoholic fatty liver disease (NAFLD) is a heterogeneous disease with multiple etiologies and molecular phenotypes. Fibrosis is the key process in NAFLD progression. In this study, we aimed to explore molecular phenotypes of NAFLD with a particular focus on the fibrosis phenotype and also aimed to explore the changes of macrophage subsets in the fibrosis subset of NAFLD.
methodsTo assess the transcriptomic alterations of key factors in NAFLD and fibrosis progression, we included 14 different transcriptomic datasets of liver tissues. In addition, two single-cell RNA sequencing (scRNA-seq) datasets were included to construct transcriptomic signatures that could represent specific cells. To explore the molecular subsets of fibrosis in NAFLD based on the transcriptomic features, we used a high-quality RNA-sequencing (RNA-seq) dataset of liver tissues from patients with NAFLD. Non-negative matrix factorization (NMF) was used to analyze the molecular subsets of NAFLD based on the gene set variation analysis (GSVA) enrichment scores of key molecule features in liver tissues.
resultsThe key transcriptomic signatures on NAFLD including non-alcoholic steatohepatitis (NASH) signature, fibrosis signature, non-alcoholic fatty liver (NAFL) signature, liver aging signature and TGF-β signature were constructed by liver transcriptome datasets. We analyzed two liver scRNA-seq datasets and constructed cell type-specific transcriptomic signatures based on the genes that were highly expressed in each cell subset. We analyzed the molecular subsets of NAFLD by NMF and categorized four main subsets of NAFLD. Cluster 4 subset is mainly characterized by liver fibrosis. Patients with Cluster 4 subset have more advanced liver fibrosis than patients with other subsets, or may have a high risk of liver fibrosis progression. Furthermore, we identified two key monocyte-macrophage subsets which were both significantly correlated with the progression of liver fibrosis in NAFLD patients.
conclusionOur study revealed the molecular subtypes of NAFLD by integrating key information from transcriptomic expression profiling and liver microenvironment, and identified a novel and distinct fibrosis subset of NAFLD. The fibrosis subset is significantly correlated with the profibrotic macrophages and M2 macrophage subset. These two liver macrophage subsets may be important players in the progression of liver fibrosis of NAFLD patients.
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