Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation - Summary - MDSpire
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Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation
To identify key mitochondria-related genes associated with liver fibrosis and explore their mechanistic roles and therapeutic potential using a multi-omics mining strategy.
Key Findings:
38 mitochondria-related DEGs were identified in CCl4-induced fibrosis, indicating potential biomarkers for liver fibrosis.
Machine learning prioritized Acot9, Aldh1b1, and Pck2 as key targets, suggesting their roles in fibrogenesis.
ACOT9, ALDH1B1, and PCK2 were upregulated in fibrotic liver tissue, correlating with disease severity.
Silencing ACOT9 in human LX-2 cells downregulated classical fibrotic markers, highlighting its regulatory role.
Interpretation:
ACOT9 was identified as a key mitochondrial target associated with liver fibrosis, with its upregulation linked to fibrogenesis through mechanisms that warrant further investigation.
Limitations:
Conclusion:
ACOT9's modulation of fibrosis markers in hepatic stellate cells highlights its mechanistic relevance and potential as a therapeutic target.
