Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis.
Lipids Health Dis
; 23(1): 137, 2024 May 08.
Article
in En
| MEDLINE
| ID: mdl-38720280
ABSTRACT
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Non-alcoholic Fatty Liver Disease
/
Machine Learning
Limits:
Humans
Language:
En
Journal:
Lipids Health Dis
Journal subject:
BIOQUIMICA
/
METABOLISMO
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: