Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation - Takeaways - MDSpire

Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation

  • By

  • Yupeng Ma

  • Xinhong Chen

  • Lujin Yin

  • Yongbin Chi

  • Denghai Zhang

  • Xiaocheng Xue

  • Xue Zhang

  • May 28, 2026

  • 0 min

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  • 1

    Mitochondrial dysfunction is a significant driver of fibrogenesis in liver diseases, highlighting the need for identifying related biomarkers.

  • 2

    The study identified 38 mitochondria-related differentially expressed genes (DEGs) linked to liver fibrosis through RNA-seq and WGCNA.

  • 3

    Key mitochondrial targets Acot9, Aldh1b1, and Pck2 were prioritized using machine learning, revealing distinct expression patterns in liver cell types.

  • 4

    In vivo validation showed upregulation of ACOT9, ALDH1B1, and PCK2 in fibrotic liver tissue, confirming their relevance to liver fibrosis.

  • 5

    Silencing ACOT9 in hepatic stellate cells downregulated classical fibrotic markers, indicating its potential as a therapeutic target.

Original Source(s)

Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation

  • by Yupeng Ma, Xinhong Chen, Lujin Yin, Yongbin Chi, Denghai Zhang, Xiaocheng Xue, Xue Zhang

  • May 28, 2026

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