A machine learning integrated multi-omics framework for risk prediction and target discovery in insomnia aggravated sepsis induced acute lung injury - Takeaways - MDSpire

A machine learning integrated multi-omics framework for risk prediction and target discovery in insomnia aggravated sepsis induced acute lung injury

  • By

  • Jinquan Zhang

  • Yuwei Zhang

  • Zeyu Liu

  • Xiaona Chen

  • Zhengzheng Yan

  • Zhixia Chen

  • Quan Li

  • June 1, 2026

  • 0 min

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  • 1

    The study identifies PTPN6 as a critical biomarker linking insomnia to exacerbated acute lung injury in sepsis patients.

  • 2

    Mendelian randomization analysis established insomnia as a causal factor in increased susceptibility to sepsis.

  • 3

    Weighted gene co-expression network analysis revealed 1,294 co-dysregulated genes associated with insomnia and acute lung injury.

  • 4

    Machine learning techniques identified ISG20, MYO1F, and PTPN6 as key hub genes, with PTPN6 prioritized for diagnostic evaluation.

  • 5

    PTPN6's expression in macrophages modulates the JAK/STAT3 signaling pathway, influencing pro-inflammatory responses.

Original Source(s)

A machine learning integrated multi-omics framework for risk prediction and target discovery in insomnia aggravated sepsis induced acute lung injury

  • by Jinquan Zhang, Yuwei Zhang, Zeyu Liu, Xiaona Chen, Zhengzheng Yan, Zhixia Chen, Quan Li

  • June 1, 2026

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