Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis

• Published in: Clinical and experimental pharmacology & physiology Vol. 52; no. 7; pp. e70031 - n/a
• Main Authors: Li, Ruichen, Wang, Qiushi, Gao, Ru, Shen, Rutao, Wang, Qihao, Cui, Xiuliang, Jiang, Zhiming, Zhang, Lijie, Fang, Jingjing
• Format: Journal Article
• Language: English
• Published: Australia Wiley Subscription Services, Inc 01.07.2025
• Subjects: 1-Phosphatidylinositol 3-kinase; AKT protein; Artificial intelligence; Biomarkers; Curcumin; Deep Learning; Explainable artificial intelligence; Gene Expression Profiling - methods; Gene Expression Regulation; Genes; Humans; immune infiltration analysis; Immune response; Immune system; Immunoregulation; molecular docking; Necroptosis; Rotenone; Sepsis; Sepsis - genetics; Sepsis - immunology; Signal transduction; Signal Transduction - genetics; Tissue inhibitor of metalloproteinase 1; Tissue Inhibitor of Metalloproteinase-1 - genetics; Transcriptome - genetics; Transcriptomics; explainable artificial intelligence; molecular docking; biomarkers; immune infiltration analysis; sepsis
• ISSN: 0305-1870; 1440-1681; 1440-1681
• DOI: 10.1111/1440-1681.70031

ABSTRACT Sepsis is a life‐threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P‐NET, a biologically informed explainable artificial intelligence model, to evaluate the gene importance for sepsis. About 688 important genes were identified, and these genes were enriched in pathways involved in inflammation and immune regulation, such as the PI3K‐Akt signalling pathway, necroptosis and the NF‐κB signalling pathway. We further selected differentially expressed genes both at bulk and single‐cell levels and found TIMP1, GSTO1 and MYL6 exhibited significant different expressions in multiple cell types. Moreover, the expression levels of these 3 genes were correlated with the abundance of important immune cells, such as M‐MDSC cells. Further analysis demonstrated that these three genes were highly expressed in sepsis patients with worse outcomes, such as severe, non‐survived and shock sepsis patients. Using a drug repositioning strategy, we found navitoclax, curcumin and rotenone could down‐regulate and bind to these genes. In conclusion, TIMP1, GSTO1 and MYL6 may serve as promising biomarkers and targets for sepsis treatment. We used XAI to identify key sepsis‐related genes TIMP1, GSTO1 and MYL6, which are highly expressed in various cell types and clinical features. Molecular docking results show that navitoclax, curcumin and rotenone may have a strong binding affinity with these genes.