Integrated multi-omics analysis and machine learning developed diagnostic markers and prognostic model based on Efferocytosis-associated signatures for septic cardiomyopathy.
Clin Immunol
; 265: 110301, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38944364
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Phagocytosis
/
Biomarkers
/
Sepsis
/
Machine Learning
/
Cardiomyopathies
Limits:
Aged
/
Female
/
Humans
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Male
/
Middle aged
Language:
En
Journal:
Clin Immunol
/
Clin. immunol
/
Clinical immunology
Journal subject:
ALERGIA E IMUNOLOGIA
Year:
2024
Document type:
Article
Country of publication:
Estados Unidos