CFD+CD14+ monocytes: potential pathogenic subset in myasthenia gravis uncovered by multi-omics integration and machine learning analysis

Background Transcriptome−wide association study (TWAS) contributes to discovering novel susceptibility genes related to diseases. Single-cell RNA sequencing (scRNA-seq) has been extensively applied to characterize cellular heterogeneity in various biological and pathological conditions. However, few studies have integrated TWAS and scRNA-seq to decode the pathogenesis of myasthenia gravis (MG) at cellular resolution. Methods We integrated multi-omics data from GEO, genome-wide association study (GWAS) catalog, and Genotype-Tissue Expression (GTEx) databases, including scRNA-seq data, GWAS summary data, and expression quantitative trait loci. A set of risk genes genetically associated with MG were identifies using TWAS analysis. TWAS activity status across cell types in MG at single cell resolution was assessed based on these genes. Seven machine learning algorithms were performed to identify high TWAS activity related signature genes. Machine learning benchmark was incorporated to select the best model using R package “mlr3”. Meanwhile, we performed scRNA-seq on peripheral blood collected from MG patients and healthy controls to generate a validation dataset. Results scRNA-seq analysis revealed an elevated proportion of CD14 + monocytes in MG patients, accompanied by notable transcriptomic reprogramming. Compared with other cell types, CD14 + monocytes exhibited higher TWAS activity. High TWAS activity subsets mainly enriched in the MG group and functionally associated with mTORC1 signaling, complement, and inflammatory response pathways. Machine learning-based feature selection revealed six robust signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, and CFD). Among these, CFD showed elevated expression early in high TWAS activity monocyte development. Compared to control group, CFD maintained higher levels throughout disease progression in MG group. CFD + CD14 + monocytes exhibited enhancer plasticity than CFD - CD14 + monocytes and were related to complement and coagulation cascades, antigen processing and presentation, and proteasome pathways. CFD + CD14 + monocytes in MG exhibit enhanced intercellular communication activity compared to CFD - cells, demonstrating greater signal reception and transmission capacity. Conclusion In summary, this study systematically delineates the important role of CFD + CD14 + monocytes in MG pathogenesis through multi-omics analysis, providing novel insights for a deeper understanding of MG immunopathology and potential therapeutic targets.

Authors

Institutions

Publication Details

Journal
Frontiers in Immunology
Published
2026-05-01
DOI
https://doi.org/10.3389/fimmu.2026.1813107
Primary Topic
Myasthenia Gravis and Thymoma
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CFD+CD14+ monocytes: potential pathogenic subset in myasthenia gravis uncovered by multi-omics integration and machine learning analysis

S S Li, Chenlu Hou, Yifan Zhang
Frontiers in Immunology
Myasthenia Gravis and Thymoma
article

CFD+CD14+ monocytes: potential pathogenic subset in myasthenia gravis uncovered by multi-omics integration and machine learning analysis

S S Li, Chenlu Hou, Yifan Zhang
article en

Abstract

Background Transcriptome−wide association study (TWAS) contributes to discovering novel susceptibility genes related to diseases. Single-cell RNA sequencing (scRNA-seq) has been extensively applied to characterize cellular heterogeneity in various biological and pathological conditions. However, few studies have integrated TWAS and scRNA-seq to decode the pathogenesis of myasthenia gravis (MG) at cellular resolution. Methods We integrated multi-omics data from GEO, genome-wide association study (GWAS) catalog, and Genotype-Tissue Expression (GTEx) databases, including scRNA-seq data, GWAS summary data, and expression quantitative trait loci. A set of risk genes genetically associated with MG were identifies using TWAS analysis. TWAS activity status across cell types in MG at single cell resolution was assessed based on these genes. Seven machine learning algorithms were performed to identify high TWAS activity related signature genes. Machine learning benchmark was incorporated to select the best model using R package “mlr3”. Meanwhile, we performed scRNA-seq on peripheral blood collected from MG patients and healthy controls to generate a validation dataset. Results scRNA-seq analysis revealed an elevated proportion of CD14 + monocytes in MG patients, accompanied by notable transcriptomic reprogramming. Compared with other cell types, CD14 + monocytes exhibited higher TWAS activity. High TWAS activity subsets mainly enriched in the MG group and functionally associated with mTORC1 signaling, complement, and inflammatory response pathways. Machine learning-based feature selection revealed six robust signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, and CFD). Among these, CFD showed elevated expression early in high TWAS activity monocyte development. Compared to control group, CFD maintained higher levels throughout disease progression in MG group. CFD + CD14 + monocytes exhibited enhancer plasticity than CFD - CD14 + monocytes and were related to complement and coagulation cascades, antigen processing and presentation, and proteasome pathways. CFD + CD14 + monocytes in MG exhibit enhanced intercellular communication activity compared to CFD - cells, demonstrating greater signal reception and transmission capacity. Conclusion In summary, this study systematically delineates the important role of CFD + CD14 + monocytes in MG pathogenesis through multi-omics analysis, providing novel insights for a deeper understanding of MG immunopathology and potential therapeutic targets.

Frontiers in ImmunologyVol. 17
Northwest University (US), Tang Du Hospital (CN), Air Force Medical University (CN)
Openalex Percentile: Top 10%
Myasthenia Gravis and Thymoma
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.