Incorporating WGCNA and Machine Learning to Identify ADAP2 as a Critical Efferocytosis-Related Gene in Sepsis

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Sepsis involves dysregulated host responses and impaired macrophage efferocytosis, causing inflammation and organ dysfunction.
• Current sepsis biomarkers lack specificity and sensitivity for early prognostic stratification.
• Novel indicators are crucial for predicting sepsis severity and patient outcomes.

Purpose of the Study:

• To identify core genes associated with macrophage efferocytosis in sepsis.
• To evaluate the prognostic value of identified genes in sepsis patients.
• To explore potential therapeutic targets for sepsis management.

Main Methods:

• Integrated bulk and single-cell RNA-sequencing data from human and mouse sepsis cohorts.
• Employed Weighted Gene Co-expression Network Analysis (WGCNA) and five machine-learning algorithms.
• Validated gene expression using quantitative PCR in peripheral blood mononuclear cells and mouse models.

Main Results:

• ADAP2 was identified as a core efferocytosis-associated gene with consensus across algorithms.
• ADAP2 expression was upregulated early and depleted late in sepsis, primarily in monocytes/macrophages.
• Lower ADAP2 levels correlated with increased sepsis severity (SOFA score) and a trend toward worse survival.

Conclusions:

• ADAP2 serves as a candidate biomarker for sepsis prognosis, closely related to macrophage efferocytosis.
• Findings suggest ADAP2's utility in early sepsis risk stratification.
• ADAP2 may inform the development of macrophage-targeted immunotherapies for sepsis.