1. Integrated multi-omics analysis and machine learning developed diagnostic markers and prognostic model based on Efferocytosis-associated signatures for septic cardiomyopathy.
- Author
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Li X, Jiang S, Wang B, He S, Guo X, Lin J, and Wei Y
- Subjects
- Humans, Prognosis, Male, Female, Middle Aged, Leukocytes, Mononuclear metabolism, Aged, Molecular Docking Simulation, Efferocytosis, Multiomics, Cardiomyopathies genetics, Cardiomyopathies diagnosis, Biomarkers, Machine Learning, Sepsis genetics, Sepsis diagnosis, Phagocytosis genetics
- Abstract
Septic cardiomyopathy (SCM) is characterized by an abnormal inflammatory response and increased mortality. The role of efferocytosis in SCM is not well understood. We used integrated multi-omics analysis to explore the clinical and genetic roles of efferocytosis in SCM. We identified six module genes (ATP11C, CD36, CEBPB, MAPK3, MAPKAPK2, PECAM1) strongly associated with SCM, leading to an accurate predictive model. Subgroups defined by EFFscore exhibited distinct clinical features and immune infiltration levels. Survival analysis showed that the C1 subtype with a lower EFFscore had better survival outcomes. scRNA-seq analysis of peripheral blood mononuclear cells (PBMCs) from sepsis patients identified four genes (CEBPB, CD36, PECAM1, MAPKAPK2) associated with high EFFscores, highlighting their role in SCM. Molecular docking confirmed interactions between diagnostic genes and tamibarotene. Experimental validation supported our computational results. In conclusion, our study identifies a novel efferocytosis-related SCM subtype and diagnostic biomarkers, offering new insights for clinical diagnosis and therapy., Competing Interests: Declaration of competing interest The authors have declared that no competing interest exists., (Copyright © 2024 Elsevier Inc. All rights reserved.)
- Published
- 2024
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