Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning
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 GS...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1557307/full |
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| author | Su Zhang Weitao Hu Yuchao Tang Hongjie Lin Xiaoqing Chen |
| author_facet | Su Zhang Weitao Hu Yuchao Tang Hongjie Lin Xiaoqing Chen |
| author_sort | Su Zhang |
| collection | DOAJ |
| description | 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 NetworkAnalyst 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 GSE61635 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. |
| format | Article |
| id | doaj-art-9ec41492011345ac8381605f28483786 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-9ec41492011345ac8381605f284837862025-08-20T03:09:48ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15573071557307Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learningSu Zhang0Weitao Hu1Yuchao Tang2Hongjie Lin3Xiaoqing Chen4Department of Rheumatology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, ChinaDepartment of Gastroenterology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, ChinaDepartment of Gastroenterology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, ChinaDepartment of Gastroenterology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, ChinaDepartment of Rheumatology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, ChinaSystemic 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 NetworkAnalyst 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 GSE61635 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.https://www.frontiersin.org/articles/10.3389/fmed.2025.1557307/fullbioinformaticshub genesimmune cellmachine learningsystemic lupus erythematosus |
| spellingShingle | Su Zhang Weitao Hu Yuchao Tang Hongjie Lin Xiaoqing Chen Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning Frontiers in Medicine bioinformatics hub genes immune cell machine learning systemic lupus erythematosus |
| title | Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning |
| title_full | Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning |
| title_fullStr | Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning |
| title_full_unstemmed | Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning |
| title_short | Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning |
| title_sort | identification of hub immune related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning |
| topic | bioinformatics hub genes immune cell machine learning systemic lupus erythematosus |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1557307/full |
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