ArticleGenes2026
Identification of Potential Biomarkers for Rheumatoid Arthritis Based on Integrated Bioinformatics and Single-Cell RNA-Seq.
Article in Genes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
Funding
Abstract
BACKGROUND/
objectivesRheumatoid arthritis (RA) is a chronic autoimmune disease that causes progressive joint damage and systemic complications. Despite multiple treatment options, many patients fail to achieve sustained remission. Our study aimed to integrate bioinformatics and single-cell RNA-seq analyses to identify potential biomarkers and therapeutic targets and explore bioactive compounds from traditional Chinese medicine (TCM).
methodsWe integrated gene expression quantitative trait loci (eQTL), protein quantitative trait loci (pQTL), and genome-wide association study (GWAS) data for RA using two-sample Mendelian randomization to identify causal druggable genes. Bulk transcriptomics and machine learning were used for candidate gene screening and validation, while single-cell RNA-seq analysis characterized cell type-specific expression and functional relevance. TCM compound screening, molecular docking, and molecular dynamics (MD) simulations were subsequently performed.
results
conclusionsThis integrative framework identified
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