ArticleLipids in health and disease2024
Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis.
Article in Lipids in health and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An endometrial tissue-based predictive model for polycystic ovary syndrome constructed from immuno-metabolic dysregulation features mediated by ACO1.Journal of ovarian research · 2026Article
- SPP1 and MMP1 as key therapeutic targets of Jingfang Granule in idiopathic pulmonary fibrosis: integrated bioinformatics and machine learning analysis.Frontiers in pharmacology · 2026Article
- Hepatic mitochondrial signaling as a systemic hub: inter-organ communication networks in aging and aging-related diseases.Frontiers in cell and developmental biology · 2026Review
- Unraveling the Metabolic Pathways Between Metabolic-Associated Fatty Liver Disease (MAFLD) and Sarcopenia.International journal of molecular sciences · 2025Review
- Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts.Lipids in health and disease · 2025Article
- Metabolic-Associated Fatty Liver Disease: The Influence of Oxidative Stress, Inflammation, Mitochondrial Dysfunctions, and the Role of Polyphenols.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Regulation of Inflammation, Lipid Metabolism, and Liver Fibrosis by Core Genes of M1 Macrophages in NASH.Journal of inflammation research · 2024Article
- Machine Learning Identifies SCO2 as a Metabolic Reprogramming Gene Promoting Lipid Accumulation in MAFLD.BioFactors (Oxford, England)Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundEvidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitochondrial genes linked to NASH may unveil potential therapeutic targets.
methodsMitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms.
resultsThe Random Forest algorithm (RF), the most effective among the 134 algorithms, identified three genes: Aldo-keto reductase family 1 member B10 (AKR1B10), thymidylate synthase (TYMS), and triggering receptor expressed in myeloid cell 2 (TREM2). They were upregulated and positively associated with genes promoting inflammation, genes involved in lipid synthesis, fibrosis, and nonalcoholic steatohepatitis activity scores in patients with NASH. Moreover, using these three genes, patients with NASH were accurately categorized into cluster 1, exhibiting heightened disease severity, and cluster 2, distinguished by milder disease activity.
conclusionThese three genes are pivotal mitochondrial genes implicated in NASH progression.
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