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Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis
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Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis
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Journal Article
Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis
2024
Overview
Background
Evidence 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.
Methods
Mitochondrial hub genes implicated in NASH were identified via analysis using 134 algorithms.
Results
The 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.
Conclusion
These three genes are pivotal mitochondrial genes implicated in NASH progression.
Publisher
BioMed Central,BioMed Central Ltd,BMC
