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Research Article

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

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DOI:

10.3791/71532

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August 7th, 2026

 ,  ,  ,  ,  ,  , 
1Department of Traditional Chinese Medicine, Aerospace Center Hospital, 2Third Affiliated Hospital, Beijing University of Chinese Medicine, 3College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, 4School of Basic Medical Sciences, Harbin Medical University

* These authors contributed equally

In This Article

Summary

Here, we present a protocol to identify endoplasmic reticulum stress-related immune signatures in atrial fibrillation by integrating public bulk transcriptomics, machine learning, immune infiltration analysis, and single-cell transcriptomics for reproducible biomarker prioritization and cell-type localization.

Abstract

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.

Introduction

Atrial fibrillation (AF) is the most common sustained arrhythmia in clinical practice, characterized by disordered electrical activity in the atria and loss of mechanical function. It significantly increases the risks of stroke, heart failure, and all-cause mortality, becoming a major global public health burden1. Current clinical management of AF faces significant challenges: traditional antiarrhythmic drugs have limited long-term efficacy in maintaining sinus rhythm, and their adverse effects, such as arrhythmias and cardiotoxicity, limit their continuous use2,3. Catheter ablation also remains challenging in maintaining long-term sinus rhythm for persistent AF4. Novel treatment strategies are currently limited by the lack of a systematic understanding of the molecular landscape of upstream mechanisms of AF, which hampers the identification and translation of precise targets. Research has shown that the pathological progression of AF involves complex interactions across multiple dimensions, including electrical, structural, and autonomic nervous system remodeling. Among these, atrial structural remodeling, centered around myocardial fibrosis, is a key mechanism for the maintenance of AF5,6,7.

Therefore, elucidating the upstream stress pathways that drive fibrosis is one of the keys to overcoming the therapeutic bottleneck in persistent AF. Recent studies suggest that endoplasmic reticulum stress (ERS), a pivotal regulatory hub that enables cells to cope with intra- and extracellular perturbations, is a highly conserved adaptive response triggered when ER homeostasis is disrupted, leading to excessive accumulation of unfolded or misfolded proteins within the ER lumen. ERS may participate in atrial remodeling by regulating proteostasis, calcium cycling, inflammatory responses, and apoptosis8,9. However, the upstream triggers, key effector molecules, and downstream pathways of ERS in AF have not yet been systematically characterized, making it difficult to define its critical nodes and actionable intervention points in atrial remodeling.

Compared with traditional bulk transcriptomic studies, multi-omics integration can yield more robust population-level disease-associated signals. Moreover, incorporating single-cell transcriptomics enables the dissection of cellular heterogeneity and the identification of the cellular sources of these signals, thereby mapping association results to specific cell types and their potential interaction pathways. This provides a foundation for subsequent mechanistic investigations and target prioritization. In the present study, AF-related bulk transcriptomic and single-cell transcriptomic datasets were retrieved from the Gene Expression Omnibus (GEO) database. ERS-related core modules were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA). By integrating single-cell analyses, key molecules were further localized to specific cell types and their interaction networks. Collectively, this study aims to delineate ERS-related key networks in AF and their associations with structural remodeling, thereby providing a rationale for target discovery and mechanism-driven, stratified therapeutic strategies.

Compared with single-dataset differential expression analysis or single-algorithm biomarker screening, this workflow improves robustness and interpretability by integrating cross-cohort validation, biologically constrained feature selection, multi-algorithm model comparison, immune deconvolution, and single-cell transcriptomics. These steps help prioritize reproducible ERS-related signatures and localize bulk-derived signals to specific immune cell populations and interaction networks. This workflow is suitable for studies using public or in-house transcriptomic datasets with well-defined disease and control groups, comparable tissue sources, adequate sample size, and available phenotype annotations. It is particularly useful for hypothesis generation, biomarker prioritization, and cell-type localization of disease-associated molecular programs. However, its performance depends on dataset quality, phenotype consistency, and effective batch-effect correction; therefore, candidate signatures identified by this workflow require validation in independent cohorts and experimental models before clinical translation. The overall study workflow is shown in Figure 1.

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Protocol

In accordance with the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects promulgated in China on February 18, 2023, research using publicly available data may meet the criteria for exemption from ethical review. This study used only publicly available, de-identified secondary transcriptomic data and did not involve new human participant recruitment, human sample collection, or animal experiments. Therefore, additional institutional ethical approval was not required. No animal experiments were performed in this study. Therefore, approval from the institutional animal care and use committee was not applicable.

Data sources for endoplasmic reticulum stress-related genes in atrial fibrillation
In this study, publicly available AF-related transcriptomic datasets were retrieved from the GEO database, including GSE41177, GSE79768, GSE115574, GSE14975, and GSE165838. Detailed information on the GSE datasets is provided in Supplementary File 1—Supplementary Table S1. GSE41177 and GSE79768 were used to construct the integrated bulk transcriptomic training cohort, whereas GSE115574 and GSE14975 were used as two independent external validation cohorts. GSE165838 was used for single-cell transcriptomic analysis. Because these datasets were generated on different platforms and may differ in tissue source, clinical background, and sample composition, each dataset was preprocessed separately according to its platform characteristics prior to integration or validation. Batch-effect correction was then performed using the sva R package for the merged training cohort. The endoplasmic reticulum stress-related gene set was retrieved from the GeneCards database with a relevance score ≥ 3 and, after deduplication, formed the target gene list used in this study.

Analysis of differentially expressed genes
After data standardization and normalization, the R package limma was used to identify differentially expressed genes (DEGs) in the integrated training set. DEGs were defined using the following significance criteria: false discovery rate-adjusted P value (adj.P.Val) < 0.05 and |log2FC| > 0.58510. To visualize the expression patterns of DEGs, volcano plots and heatmaps were generated using the ggplot2 and pheatmap packages, respectively.

WGCNA analysis
To elucidate potential mechanisms of coordinated gene regulation, define the association patterns between co-expression modules and clinical trait variables, and identify core biomarkers or therapeutic targets with translational potential, WGCNA was applied11.

A weighted co-expression network was constructed using the WGCNA package in R. The soft-thresholding power (β) was selected according to the scale-free topology criterion; the corresponding β value was chosen for subsequent analyses when the scale-free topology fit index (R2) reached and remained above 0.8512. During module identification, parameters related to dynamic tree cutting and module detection sensitivity were optimized to improve module boundary resolution and stability. Finally, modules significantly associated with the target trait were extracted, and intramodular hub genes were identified as candidate gene sets for downstream analyses.

Enrichment analysis of AF-related DEGs
To precisely identify hub genes, the DEGs were first intersected with genes from the key WGCNA modules to define a set of genes implicated in AF pathogenesis. Next, this AF gene set was further intersected with ERS-related genes, and the resulting overlapping genes were retained for subsequent analyses.

Functional enrichment of the screened genes was evaluated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. GO terms were analyzed with the R package clusterProfiler to summarize enrichment across biological process (BP), cellular component (CC), and molecular function (MF) categories13. KEGG analysis was then used to identify enriched pathways associated with the target genes14. Enrichment results with an adjusted P value < 0.05 were considered statistically significant. The leading GO terms and KEGG pathways were displayed as bar plots and bubble plots using ggplot2.

Protein-protein interaction (PPI) analysis
PPI analysis was performed by uploading the intersected gene set to the STRING database, with the organism restricted to Homo sapiens. Disconnected nodes were removed, and interactions were retrieved using a medium confidence score threshold (combined score ≥ 0.4). The resulting PPI network was then imported into a network visualization and analysis tool for topological analysis to identify key nodes.

Construction of a candidate AF-ERS classification model based on 12 machine-learning algorithms
In this study, an ensemble classification framework based on twelve conventional machine-learning algorithms was developed to screen ERS-related candidate signature genes associated with AF and to optimize classification performance. For data partitioning, after standardization and normalization, GSE41177 and GSE79768 were merged to generate the training cohort expression matrix. GSE115574 was used as an independent external validation cohort to evaluate model generalizability. Specifically, DEGs were first identified in the training cohort (|log2FC| >0.585, adjusted p < 0.05). These DEGs were then intersected with genes from the key WGCNA modules and ERS-related genes, and the resulting gene set was used as the input features for model construction.

To link ERS-related genes with the AF phenotype, a candidate classification model was developed using 12 machine-learning approaches: Lasso, Ridge, stepwise generalized linear model (Stepglm), extreme gradient boosting (XGBoost), random forest (RF), elastic net (Enet), partial least squares regression for generalized linear models (plsRglm), generalized boosted regression modeling (GBM), naive Bayes, linear discriminant analysis (LDA), glmBoost, and support vector machine (SVM). A systematic combinatorial modeling strategy was adopted by adding a second algorithm to the first and integrating them via the tuning parameter α, yielding 113 combinations of feature selection and model fitting that were comprehensively evaluated. Model discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC). According to previously reported model-selection criteria, the final candidate framework was defined as the model with the best overall performance, as assessed by the mean AUC across the training and validation cohorts.

This combinatorial modeling strategy was informed by prior studies in biomedical machine learning15,16,17. Collectively, these studies indicate that no single algorithm consistently outperforms others across datasets and analytical tasks. Based on this premise, adopting an ensemble learning and combinatorial modeling framework can increase the likelihood of obtaining a high-performing candidate model with more stable generalizability and improve the robustness of model selection.

Subsequently, SHapley Additive exPlanations (SHAP) values were applied to interpret the machine-learning model by visualizing the key features driving AF classification, thereby quantifying the contribution of each feature to the predicted outcome and illustrating how individual signature genes influence the final model output18.

Evaluation of model performance and external validation of the optimal model
The performance of the optimal model was evaluated in the training cohort and in the independent external validation cohort (GSE115574). At the model level, a confusion matrix was constructed based on the predicted class labels, and the corresponding classification metrics were reported. Receiver operating characteristic (ROC) curves were generated using the R package pROC, and the AUC was calculated to quantify discriminative performance.

At the biomarker level, single-gene ROC curves were plotted for each key gene in the optimal model, and the corresponding AUCs were computed to assess their individual discriminatory ability. In addition, differential expression of the key genes was summarized using a volcano plot, and boxplots were used to depict their expression distributions in disease versus healthy samples. To further evaluate the generalizability of the previously defined optimal model-derived gene signature, an additional independent external validation was performed using GSE14975. GSE14975 contains transcriptomic data from left atrial appendage samples, including five atrial fibrillation samples and five sinus rhythm/control samples. All genes included in the locked signature were available in this dataset. To maintain consistency with the original cross-cohort analytical workflow, the development cohort and GSE14975 were harmonized using ComBat with the dataset source as the batch variable. This harmonization was performed in an unsupervised manner. Importantly, disease/control labels from GSE14975 were not used for feature selection, coefficient estimation, threshold determination, or hyperparameter tuning.

The optimal model-derived scoring model was fitted using the development cohort only and then applied to GSE14975 for external validation. Model performance in GSE14975 was assessed using receiver operating characteristic curve analysis, area under the curve, 95% confidence interval (CI), sensitivity, specificity, accuracy, positive and negative predictive values, and Brier score. In addition, single-gene ROC curves were generated for all optimal model-derived genes in GSE14975 to illustrate their individual discriminatory ability. To further assess potential overfitting in the development cohort, repeated 10-fold cross-validation and bootstrap optimism correction were performed using the gene signature derived from the locked optimal model. For repeated cross-validation, the development cohort was repeatedly partitioned into 10 folds, and model discrimination was summarized across all iterations. For bootstrap validation, 1,000 bootstrap resamples were generated to estimate the optimism of the apparent development-set performance and to calculate the optimism-corrected AUC. Because the final signature was derived from the optimal model, the contribution of each gene was interpreted primarily according to the absolute magnitude and direction of the model coefficients. In addition, single-gene ROC analyses were performed in GSE14975 to illustrate the individual discriminatory ability of each component gene. For visualization purposes, single-gene ROC curves were oriented to reflect discriminatory ability regardless of whether higher or lower expression was associated with AF.

Gene set enrichment analysis (GSEA)
To explore the functional implications of the key genes, GSEA was performed using samples from the disease group19. For each key gene, samples were stratified into high- and low-expression subgroups using the median expression value in the disease group as the cutoff. The mean expression difference between the two subgroups for each gene was calculated, and a ranked gene list was generated in descending order as input for enrichment analysis. GSEA was conducted using the R package clusterProfiler, with gene sets obtained from the MSigDB collection c2.cp.kegg.Hs.symbols.gmt. Statistical significance was defined as p < 0.05. Enrichment direction was determined by the sign of the normalized enrichment score (NES), and enrichment plots were generated for representative pathways.

Assessment of immune cell subtype abundance and differential expression
The CIBERSORT deconvolution algorithm was applied to estimate the relative abundance of infiltrating immune cell subsets and their interrelationships across samples. Based on the LM22 leukocyte signature matrix, immune cell composition was quantitatively inferred from gene expression profiles using the R package CIBERSORT20. A threshold of p < 0.05 was used to filter results, and only samples passing this criterion were retained for subsequent analyses. Boxplots were generated to compare the estimated relative fractions of immune-cell subsets between the AF and control groups. In addition, Spearman's correlation analysis was performed to assess associations between immune cell infiltration levels and hub gene expression.

Single-cell analysis
Single-cell transcriptomic analysis was performed using the GEO dataset GSE165838. Raw gene-cell count matrices were imported into R and processed using Seurat v4.4.0. For each sample, a Seurat object was generated using CreateSeuratObject with min.cells = 5 and min.features = 300. Quality-control metrics, including the number of detected genes, total unique molecular identifier (UMI) counts, mitochondrial gene percentage, ribosomal gene percentage, and hemoglobin gene percentage, were calculated for each cell. Cells were retained if they had more than 500 detected genes, fewer than 5,000 UMI counts, mitochondrial gene percentage < 25%, ribosomal gene percentage > 3%, and hemoglobin gene percentage < 1%. Genes detected in fewer than three cells were removed. MALAT1 and mitochondrial genes were also excluded before downstream analysis. DoubletFinder was used to detect and exclude potential doublets. Briefly, cells were split by sample identity, and doublet detection was performed separately for each sample using principal components 1–30.

The pN parameter was set to 0.25, and the optimal pK value was selected according to the maximal BC metric obtained from parameter sweeping. The expected doublet rate was estimated according to the number of recovered cells in each sample, with rates of 2.5%, 5%, and 6.5% used for samples with relatively low, intermediate, and high cell numbers, respectively. Only cells classified as singlets were retained. Ambient RNA contamination was further estimated using DecontX, and cells with a contamination score ≥ 0.2 were excluded. After quality control, doublet removal, and ambient RNA filtering, 40,886 cells and 23,947 genes were retained for downstream analysis. The filtered single-cell dataset was normalized with the LogNormalize method using a scale factor of 10,000, followed by identification of highly variable genes. The data were then scaled before principal component analysis.

To reduce sample-specific batch effects, Harmony was applied using orig.ident as the batch variable. Uniform Manifold Approximation and Projection (UMAP) visualization and nearest-neighbor graph construction were performed using the first 15 Harmony-corrected dimensions21. Clustering was performed using the Louvain algorithm, and multiple clustering resolutions were evaluated. The final major cell-type annotation was based on the clustering result at resolution 0.05. Cell clusters were manually annotated according to canonical marker-gene expression. This marker-based annotation strategy is consistent with prior single-cell immune-profiling studies22. T cells were identified by CD3D, CD3E, and TRAC; natural killer (NK) cells by NKG7, GNLY, NCAM1, and KLRG1; monocyte-macrophage cells by LYZ, CD14, FCGR3A, CD68, CD163, FCN1, TYROBP, S100A8, and S100A9; B cells by MS4A1 and CD79A; plasma cells by MZB1 and XBP1; endothelial cells by PECAM1, VWF, and CDH5; vascular smooth muscle cells by ACTA2, TAGLN, MYH11, and MYL9; fibroblasts by DCN, LUM, COL1A1, COL1A2, and PDGFRA; neutrophil-like cells by FCGR3B, CXCR2, S100A8, and MPO; mast cells by TPSB2; and dendritic cells by LILRA4, CD1C, and XCR1. Marker-gene expression across clusters was visualized using dot plots, and the expression distribution of the final ERS-related hub genes was visualized on UMAP embeddings.

To quantify ERS-related transcriptional activity at the single-cell level, the final hub-gene set was used to calculate cell-wise signature scores using AUCell, single-sample gene set enrichment analysis, and Seurat AddModuleScore. For AUCell, cell rankings were constructed from the normalized RNA expression matrix, and AUC scores were calculated using the hub-gene set with the top 10% of ranked genes as the maximum ranking threshold. For ssGSEA, enrichment scores were calculated using the GSVA package. The three scoring outputs were centered and scaled, then min-max normalized, and finally summed to generate an integrated ERS-related composite score for each cell. The distribution of the composite score was compared across annotated cell populations to evaluate cell-type heterogeneity of the ERS-related program. Because the monocyte-macrophage lineage exhibited prominent ERS-related signature enrichment and was closely associated with immune-inflammatory remodeling, it was selected for subsequent within-lineage analyses. Monocyte-macrophage cells were divided into high- and low-score groups according to the median ERS-related composite score. Pseudotime trajectory analysis was then performed on monocyte-macrophage cells using Monocle.

For pseudotime analysis, a CellDataSet object was created from the raw count matrix using a negative binomial expression model. Size factors and dispersions were then estimated. Ordering genes were selected using a mean expression threshold of ≥ 0.1 and empirical dispersion greater than the fitted dispersion. Dimensionality was reduced with the DDRTree algorithm, and cells were ordered along the inferred trajectory. The dynamic expression patterns of ERS-related hub genes along pseudotime were visualized. Cell-cell communication analysis was performed using CellChat to explore potential ligand-receptor interactions involving monocyte-macrophage cells with different ERS-related scores. For this analysis, monocyte-macrophage cells were labeled as high-score or low-score according to the median composite score, while other cells retained their original cell-type labels. The normalized RNA expression matrix and corresponding cell-group annotations were used to create the CellChat object. For cell-cell communication analysis, the human CellChatDB database was selected, and only secreted signaling interactions were evaluated. Overexpressed genes and ligand-receptor pairs were detected before calculating communication probabilities. Cell groups containing fewer than 10 cells were excluded from the interaction analysis. Pathway-level communication probabilities were subsequently estimated and aggregated to compare the number and strength of interactions among cell populations. To facilitate reproducibility, a checkpoint table is provided below linking each protocol step to its corresponding expected output figure or table (Supplementary File 1—Supplementary Table S2).

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Results

Identification of differentially expressed genes in AF
To improve comparability across cohorts, two AF-related transcriptomic datasets (GSE41177 and GSE79768) were integrated, and batch effects were corrected on the merged expression matrix. Figure 2A,B shows boxplots of global expression distributions before and after batch-effect correction, which were used to evaluate whether expression intensity distributions were comparable across samples.

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Discussion

This study integrated bulk and single-cell transcriptomes to investigate the role of ERS in AF. An ERS-related gene signature with favorable cross-cohort discrimination was derived, and these signals were mapped predominantly to the monocyte-macrophage lineage and were associated with extensive intercellular communication. Collectively, the findings suggest that ERS-associated programs are coupled to immune-cell-centric remodeling in AF. Compared with bulk-only biomarker screening, this workflow provides a multi-layered ...

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Disclosures

The authors report no conflicts of interest in this work. During the revision of this manuscript, ChatGPT from OpenAI was used only to assist with English-language polishing. The authors reviewed, verified, and edited all AI-assisted text and take full responsibility for the accuracy and integrity of the final manuscript. No AI-assisted tools were used to generate research ideas, perform data analysis, interpret results, create figures or tables, or draw scientific conclusions. The work reported in the article has been performed by the authors. F.T, PR.W: Writing-original draft, Software, Methodology,Visualization, Validation, and Data curation. SY.T: Investigation and Methodology. FF.B: Supervision. QR.L: Supervision, Methodology, Data curation. XY.J, YX.X: Literature retrieval and data interpretation.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
AddModuleScoreSeurat functionversion 4.4.0NA
AUCellBioconductorversion 1.32.0RRID:SCR_021327
caretCRANversion 7.0.1RRID:SCR_022524
celda / decontXBioconductorversion 1.24.0NA
CellChatGitHub / CellChatversion 2.2.0RRID:SCR_021946
CIBERSORT / LM22 signature matrixCIBERSORTLM22NA
clusterProfilerBioconductorversion 4.12.6RRID:SCR_016884
CytoscapeCytoscape Consortiumversion 3.10RRID:SCR_003032
DoubletFinderGitHub / McGinnis Labversion 2.0.4NA
e1071CRANversion 1.7.16NA
gbmCRANversion 2.2.2NA
Gene Expression Omnibus (GEO) databaseNational Center for Biotechnology Information (NCBI)GSE41177NA
Gene Expression Omnibus (GEO) databaseNCBIGSE79768NA
Gene Expression Omnibus (GEO) databaseNCBIGSE115574NA
Gene Expression Omnibus (GEO) databaseNCBIGSE14975NA
Gene Expression Omnibus (GEO) databaseNCBIGSE165838NA
glmnetCRANversion 4.1.8NA
HarmonyCRANversion 1.2.4NA
limmaBioconductorversion 3.60.6RRID:SCR_010943
MASSCRANversion 7.3.61NA
mboostCRANversion 2.9.11NA
MonocleBioconductorversion 2.38.0RRID:SCR_016339
org.Hs.eg.dbBioconductorversion 3.19.1NA
plsRglmCRANversion 1.5.1NA
pROCCRANversion 1.18.5RRID:SCR_024286
R statistical softwareR Foundation for Statistical Computingversion 4.4.2RRID:SCR_001905
randomForestCRANversion 4.7.1.2RRID:SCR_015718
RStudioPosit Software, PBCversion 2024.4.1.748RRID:SCR_000432
SeuratCRAN / Satija Labversion 4.4.0RRID:SCR_016341
shapvizCRANversion 0.10.2NA
svaBioconductorversion 3.52.0NA
WGCNACRANversion 1.73RRID:SCR_003302
xgboostCRANversion 1.7.8.1NA

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