{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T17:03:37Z","timestamp":1784653417089,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["PL2024F005"],"award-info":[{"award-number":["PL2024F005"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background\/objectives: Rheumatoid 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). Methods: We 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: CXCL6, IFNG, and SLAMF1 were identified as RA-associated candidate targets with distinct cell type-specific expression patterns, strong immune associations, and favorable diagnostic performance. Functional analyses linked these genes to immune activation and intercellular communication. In silico analyses prioritized sesamin, (+)-Ganoderic acid Mf, and (24R)-saringosterol as candidate compounds, with the IFNG\u2013(+)-Ganoderic acid Mf complex showing stable behavior during MD simulation. Conclusions: This integrative framework identified CXCL6, IFNG, and SLAMF1 as candidate biomarkers and druggable targets for RA. Sesamin, (+)-Ganoderic acid Mf, and (24R)-saringosterol warrant further experimental evaluation. These findings provide a basis for future mechanistic and translational studies.<\/jats:p>","DOI":"10.3390\/genes17070828","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T16:16:39Z","timestamp":1784650599000},"page":"828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Potential Biomarkers for Rheumatoid Arthritis Based on Integrated Bioinformatics and Single-Cell RNA-Seq"],"prefix":"10.3390","volume":"17","author":[{"given":"Jinling","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Pharmacy, Harbin University of Commerce, Harbin 150076, China"},{"name":"Heilongjiang Provincial Key Laboratory of Fluid Engineering Equipment and Digital Intelligence Technology, Harbin University of Commerce, Harbin 150029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0279-4150","authenticated-orcid":false,"given":"Ke","family":"Han","sequence":"additional","affiliation":[{"name":"School of Pharmacy, Harbin University of Commerce, Harbin 150076, China"},{"name":"Heilongjiang Provincial Key Laboratory of Fluid Engineering Equipment and Digital Intelligence Technology, Harbin University of Commerce, Harbin 150029, China"},{"name":"School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref_1","unstructured":"Global Burden of Disease Collaborative Network (2020). 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