Endoplasmic Reticulum Stress-Related Immune Signature in Atrial Fibrillation: Machine Learning and Single-Cell Transcriptomics.

This study describes a reproducible computational workflow for identifying endoplasmic reticulum stress (ERS)-related gene signatures in atrial fibrillation (AF) by integrating bulk transcriptomics, machine learning, immune infiltration analysis, and single-cell transcriptomics. Public bulk transcriptomic datasets were retrieved from the Gene Expression Omnibus (GEO), followed by phenotype harmonization, normalization, batch-effect correction, and differential expression analysis. Weighted gene co-expression network analysis (WGCNA) was combined with ERS-related gene sets to identify candidate ERS-associated genes. A multi-algorithm machine-learning framework was then used to compare feature-selection and model-fitting strategies. The selected model was evaluated in an independent external validation cohort (GSE115574) and further assessed in an additional cohort (GSE14975), with discriminatory performance quantified by receiver operating characteristic (ROC) analysis and the area under the curve (AUC). Using this workflow, 22 ERS-related core genes were identified, and an 18-gene Elastic Net (Enet) model showed the highest overall discriminatory performance across the training and validation cohorts. SHapley Additive exPlanations (SHAP) analysis highlighted the major contribution of genes such as RPS11, NCF2, and S100A4 to model prediction. Immune deconvolution and single-cell transcriptomic analysis further mapped the ERS-related signature predominantly to the monocyte-macrophage lineage, suggesting its potential involvement in AF-associated immune remodeling. This workflow provides a reproducible strategy for linking disease-associated transcriptomic signatures to specific immune cell populations and can be adapted to other disease contexts with suitable bulk and single-cell datasets.