Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH - Takeaways - MDSpire

Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH

  • By

  • Weiheng Wen

  • Zenghui Liu

  • Wenliang Tan

  • Yingzheng Tan

  • Wei Li

  • Jian Wan

  • Hongsai Hu

  • Zhengwu Jiang

  • Xing Tang

  • Jing Yang

  • Jiao Xiao

  • Xiongjin Tan

  • Xun Chen

  • Peili Wu

  • Yukun Li

  • January 16, 2026

  • 0 min

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  • 1

    The study investigates the transition from MASLD to MASH, linking it to cirrhosis and hepatocellular carcinoma.

  • 2

    Monocytes/macrophages and hepatic stellate cells were found to be significantly enriched during MASH progression.

  • 3

    A distinct DTNA+ macrophage subpopulation was identified, showing characteristics of M2 polarization and enhanced inflammatory signaling.

  • 4

    RUNX2 was identified as a key transcriptional regulator in the interaction between DTNA+ macrophages and activated HSCs.

  • 5

    DTNA was determined to be the optimal predictive biomarker for distinguishing MASLD from MASH using machine learning models.

Original Source(s)

Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH

  • by Weiheng Wen, Zenghui Liu, Wenliang Tan, Yingzheng Tan, Wei Li, Jian Wan, Hongsai Hu, Zhengwu Jiang, Xing Tang, Jing Yang, Jiao Xiao, Xiongjin Tan, Xun Chen, Peili Wu, Yukun Li

  • January 16, 2026

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