Integrated multi-omics analysis and machine learning developed diagnostic markers and prognostic model based on Efferocytosis-associated signatures for septic cardiomyopathy.

Journal: Clinical immunology (Orlando, Fla.)
PMID: 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.

Authors

  • Xuelian Li
    Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
  • Shijiu Jiang
    Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Engineering Research Center for Immunological Diagnosis and Therapy of Cardiovascular Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Cardiology, The First Affifiliated Hospital, Shihezi University, Shihezi, Xinjiang, China.
  • Boyuan Wang
    School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao, Macao SAR, China.
  • Shaolin He
    Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Engineering Research Center for Immunological Diagnosis and Therapy of Cardiovascular Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiaopeng Guo
    Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
  • Jibin Lin
    Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Engineering Research Center for Immunological Diagnosis and Therapy of Cardiovascular Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: linjibin713@live.cn.
  • Yumiao Wei
    Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Engineering Research Center for Immunological Diagnosis and Therapy of Cardiovascular Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Electronic address: ymwei12@163.com.

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