International journal of biological macromolecules · 2025
Chemokine-like factor-like MARVEL transmembrane domain-containing 1 identified as a novel target protein for immune dysregulation in sepsis: A machine learning and molecular dynamics framework for diagnostic biomarkers and therapeutic exploration.
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Lin Jingnan, Zhong Kaiqiang, Ma Shuqi, Pan Qiusha, Liu Shiyi, Zeng Ruifeng, Xi Xiaotu, Li Jun, Rui Chen
Аннотация
BACKGROUND: Sepsis, a systemic inflammatory response syndrome precipitated by infection, involves critical immune dysregulation. Here, we aimed to evaluate immune-derived diagnostic markers, investigate immune cell infiltration, and explore targeted pharmacological interventions.
METHODS: Weighted gene co-expression network analysis and multiple machine learning (ML) algorithms were used to screen hub markers and build a sepsis prediction model. Immune cell infiltration was analyzed using CIBERSORT. In vivo validation of mRNA trends was performed in murine sepsis models. Drug-Gene Interaction and Connectivity Map databases, molecular docking and dynamics simulations were used to identify and validate biomarker - targeted compounds.
RESULTS: Six immune-derived biomarkers were identified: CMTM1, IL18R1, JAK2, MAPK14, S100A6, and S100A8. The prediction model achieved an area under the curve (AUC) of 0.958. Immune infiltration characteristics and biomarker-immune cell correlations were identified. Murine models recapitulated human biomarker expression. Biomarker protein-drug combinations exhibited excellent binding capabilities, highlighting AT9283, tozasertib, SD-169, and LY294002 as potential therapeutic agents.
CONCLUSIONS: This study identified key immune biomarkers for sepsis, developed a highly accurate prediction model, and revealed promising therapeutic compounds. Notably, this is the first study to link CMTM1 to sepsis. These findings offer novel perspectives on accurate diagnosis and targeted therapy of immune dysregulation in sepsis.
METHODS: Weighted gene co-expression network analysis and multiple machine learning (ML) algorithms were used to screen hub markers and build a sepsis prediction model. Immune cell infiltration was analyzed using CIBERSORT. In vivo validation of mRNA trends was performed in murine sepsis models. Drug-Gene Interaction and Connectivity Map databases, molecular docking and dynamics simulations were used to identify and validate biomarker - targeted compounds.
RESULTS: Six immune-derived biomarkers were identified: CMTM1, IL18R1, JAK2, MAPK14, S100A6, and S100A8. The prediction model achieved an area under the curve (AUC) of 0.958. Immune infiltration characteristics and biomarker-immune cell correlations were identified. Murine models recapitulated human biomarker expression. Biomarker protein-drug combinations exhibited excellent binding capabilities, highlighting AT9283, tozasertib, SD-169, and LY294002 as potential therapeutic agents.
CONCLUSIONS: This study identified key immune biomarkers for sepsis, developed a highly accurate prediction model, and revealed promising therapeutic compounds. Notably, this is the first study to link CMTM1 to sepsis. These findings offer novel perspectives on accurate diagnosis and targeted therapy of immune dysregulation in sepsis.