Reanalysis of Public Transcriptomes Reveals Shared Immune Signatures Between Major Depressive Disorder And Dermatomyositis With Single-Cell Context

Summary

This study aimed to use an integrative bioinformatic reanalysis of public GEO datasets, combined with single-cell contextualization, to identify candidate shared genes between major depressive disorder and dermatomyositis and to characterize their distribution across immune cell subsets in a dermatomyositis-related single-cell dataset.

Abstract

This study aimed to identify candidate shared transcriptomic signals between major depressive disorder and dermatomyositis through an integrative bioinformatic reanalysis of public GEO datasets with single-cell contextualization. The analytical workflow included Weighted Gene Co-expression Network Analysis (WGCNA) for key module identification, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional characterization, GeneMANIA- and a network visualization platform-based network analysis for candidate-gene prioritization, and evaluation of 113 machine-learning models combined with SHapley Additive exPlanations (SHAP) for diagnostic feature selection. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and single-cell RNA-seq-based contextualization were subsequently performed to further characterize the immune-related cellular context of the identified signals. Integration of dermatomyositis-related GEO datasets identified 570 differentially expressed genes, from which 33 candidate shared genes were obtained via WGCNA. Functional enrichment and network analyses highlighted immune defense, cytotoxicity, and pathways including PPAR, IL-17, and antigen processing, with ELANE, PPBP, and CTSG emerging as highly connected nodes. Machine-learning-based feature prioritization retained 8 candidate model-selected genes, namely KIF4A, OLR1, KIR2DL4, KRT23, KIR3DS1, AZU1, SCG5, and LRRC37E. Immune infiltration analysis associated these shared genes with regulatory T cells (Tregs), resting mast cells, resting dendritic cells, and both classically activated (M1) and alternatively activated (M2) macrophages. Single-cell RNA-seq contextualization further suggested that CD8⁺ T-cell subsets with different candidate-gene score states showed distinct intercellular communication patterns. Among these, the MIF–(CD74+CD44) axis and signals from naive/central memory T cells were notable features requiring further validation. Overall, this study identified candidate shared transcriptomic signals between major depressive disorder and dermatomyositis and highlighted immune-related cellular contexts that warrant further validation in true comorbid cohorts.

Introduction

Dermatomyositis is a chronic systemic autoimmune disease characterized by inflammatory involvement of the skin and skeletal muscle, clinically manifested by symmetrical proximal muscle weakness and distinctive cutaneous lesions, and, in severe cases, multiorgan dysfunction¹. Accumulating clinical evidence highlights that patients with dermatomyositis frequently experience psychiatric comorbidities, most notably major depressive disorder^2,3,4. The pathogenesis of dermatomyositis-associated major depressive disorder is multifactorial, arising from a comp....

Protocol

This study used only publicly available, deidentified datasets from the Gene Expression Omnibus (GEO) database. Because the work involved secondary analysis of existing public data and did not include direct participant contact, intervention, or access to identifiable personal information, additional ethics committee approval and informed consent were not required.

Data sources and preprocessing

All gene expression and single‑cell dataset were obtained from the GEO database²⁴. For major depressive disorder, dataset GSE98793 was used, which comprises peripheral blood....

Results

Identification of candidate shared genes between major depressive disorder and dermatomyositis

Following data merging, normalization, and batch correction of dermatomyositis-related GEO datasets (Figure 2A,B), a total of 570 differentially expressed genes were identified (Figure 2C,D), comprising 517 up-regulated and 53 down-regulated genes. This dermatomyositis differential expressio.......

Discussion

Dermatomyositis is a chronic systemic autoimmune disease with prominent skin and muscle involvement, and accumulating clinical observations suggest that patients with dermatomyositis may also experience substantial psychiatric burden, including symptoms consistent with major depressive disorder. In this context, the present study applied an integrative bioinformatic reanalysis framework to identify candidate shared transcriptomic signals between major depressive disorder and dermatomyositis, with additional immune-infilt.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
AddModuleScore	Seurat function	version 4.4.0	Module-score calculation within Seurat RRID: NA
AUCell	Bioconductor	version 1.32.0	Single-cell gene-set activity scoring RRID: SCR_021327
caret	CRAN	version 7.0.1	Machine-learning workflow support RRID: SCR_022524
celda / decontX	Bioconductor	version 1.24.0	Ambient RNA contamination estimation RRID: NA
CellChat	GitHub / CellChat	version 2.2.0	Cell-cell communication analysis RRID: SCR_021946
CIBERSORT / LM22 signature matrix	CIBERSORT	LM22	Immune-cell infiltration estimation RRID: NA
clusterProfiler	Bioconductor	version 4.12.6	Functional enrichment analysis RRID: SCR_016884
Cytoscape	Cytoscape Consortium	version 3.10	a network visualization platform for visualization and analysis RRID: SCR_003032
DoubletFinder	GitHub / McGinnis Lab	version 2.0.4	Doublet detection in single-cell datasets RRID: NA
e1071	CRAN	version 1.7.16	Support Vector Machine and Naive Bayes modeling RRID: NA
gbm	CRAN	version 2.2.2	Gradient Boosting Machine modeling RRID: NA
Gene Expression Omnibus (GEO) database	National Center for Biotechnology Information (NCBI)	GSE98793	Major depressive disorder bulk transcriptome dataset RRID: NA
Gene Expression Omnibus (GEO) database	NCBI	GSE1551	Dermatomyositis training dataset; skeletal muscle biopsy samples RRID: NA
Gene Expression Omnibus (GEO) database	NCBI	GSE46239	Dermatomyositis training dataset; skin biopsy samples RRID: NA
Gene Expression Omnibus (GEO) database	NCBI	GSE128470	Dermatomyositis training dataset; dermatomyositis samples extracted from inflammatory myopathy cohort RRID: NA
Gene Expression Omnibus (GEO) database	NCBI	GSE5370	Independent dermatomyositis validation dataset; untreated adult muscle samples RRID: NA
Gene Expression Omnibus (GEO) database	NCBI	GSE11971	Independent dermatomyositis validation dataset RRID: NA
Gene Expression Omnibus (GEO) database	NCBI	GSE39454	Independent dermatomyositis validation dataset; dermatomyositis samples extracted from inflammatory myopathy cohort RRID: NA
Gene Expression Omnibus (GEO) database	NCBI	GSE190510	Dermatomyositis-related single-cell RNA-seq dataset RRID: NA
GeneMANIA	University of Toronto / GeneMANIA	web server version accessed in this study	Functional association network construction RRID: RRID:SCR_005709
glmnet	CRAN	version 4.1.8	LASSO, Ridge, and Elastic Net modeling RRID: NA
GSVA	Bioconductor	version 2.0.7	ssGSEA scoring RRID: NA
Harmony	CRAN	version 1.2.4	Batch correction for single-cell data integration RRID: NA
limma	Bioconductor	version 3.60.6	Differential expression analysis, probe summarization, and normalization utilities RRID: SCR_010943
MASS	CRAN	version 7.3.61	Linear discriminant analysis RRID: NA
mboost	CRAN	version 2.9.11	glmBoost modeling RRID: NA
Monocle	Bioconductor	version 2.38.0	Pseudotime trajectory analysis RRID: SCR_016339
org.Hs.eg.db	Bioconductor	version 3.19.1	Human gene annotation database RRID: NA
plsRglm	CRAN	version 1.5.1	Partial least squares generalized linear modeling RRID: NA
pROC	CRAN	version 1.18.5	ROC curve analysis RRID: SCR_024286
R statistical software	R Foundation for Statistical Computing	version 4.4.2	Main statistical computing environment RRID: SCR_001905
randomForest	CRAN	version 4.7.1.2	Random Forest modeling RRID: SCR_015718
RStudio	Posit Software, PBC	version 2024.4.1.748	Integrated development environment for R RRID: SCR_000432
Seurat	CRAN / Satija Lab	version 4.4.0	Single-cell RNA-seq preprocessing, clustering, and visualization RRID: SCR_016341
shapviz	CRAN	version 0.10.2	SHAP-based model interpretability analysis RRID: NA
sva	Bioconductor	version 3.52.0	Batch-effect correction using ComBat RRID: NA
WGCNA	CRAN	version 1.73	Weighted gene co-expression network analysis RRID: SCR_003302
xgboost	CRAN	version 1.7.8.1	Extreme gradient boosting RRID: NA