ORIGINAL RESEARCH article
Front. Med.
Sec. Rheumatology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1557307
This article is part of the Research TopicDecoding SLE with Machine Learning: New Tools to Change the Future of This Rare ConditionView all articles
Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning
Provisionally accepted
- 1Department of Rheumatology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China, Quanzhou, Fujian, China
- 2Department of Gastroenterology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China, Quanzhou, Fujian, China
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Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that involves multiple systems. SLE is characterized by the production of autoantibodies and inflammatory tissue damage. This study further explored the role of immune-related genes in SLE. We downloaded the expression profiles of GSE50772 using the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) in SLE.The DEGs were also analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The gene modules most closely associated with SLE were then derived by Weighted Gene Co-expression Network Analysis (WGCNA). Differentially expressed immune-related genes (DE-IRGs) in SLE were obtained by DEGs, key gene modules and IRGs. The protein-protein interaction (PPI) network was constructed through the STRING database. Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. Then, we constructed a diagnostic model. The model was validated by external cohort GSE61635 and peripheral blood mononuclear cells (PBMC) from SLE patients. Immune cell abundance assessment was achieved by CIBERSORT. The hub DE-IRGs and miRNA networks were made accessible through the NetworkAnalys database. We screened 945 DEGs, which are closely related to the type I interferon pathway and NOD-like receptor signaling pathway. Machine learning identified a total of five hub DE-IRGs (CXCL2, CXCL8, FOS, NFKBIA, CXCR2), and validated in GSE6165 and PBMC from SLE patients. Immune cell abundance analysis showed that the hub genes may be involved in the development of SLE by regulating immune cells (especially neutrophils). In this study, we identified five hub DE-IRGs in SLE and constructed an effective predictive model. These hub genes are closely associated with immune cell in SLE. These may provide new insights into the immune-related pathogenesis of SLE.
Keywords: bioinformatics, Hub genes, immune cell, machine learning, systemic lupus erythematosus
Received: 08 Jan 2025; Accepted: 23 Apr 2025.
Copyright: © 2025 Zhang, Hu, Tang, Lin and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaoqing Chen, Department of Rheumatology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China, Quanzhou, Fujian, China
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