Chinese Journal of Tissue Engineering Research ›› 2024, Vol. 28 ›› Issue (27): 4312-4318.doi: 10.12307/2024.571

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Expression of immune-related genes in rheumatoid arthritis and a two-sample Mendelian randomization study of immune cells

Fan Yidong1, Qin Gang2, He Kaiyi2, Gong Yufang3, Li Weicai1, Wu Guangtao1   

  1. 1Guangxi University of Chinese Medicine, Nanning 530222, Guangxi Zhuang Autonomous Region, China; 2The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi Zhuang Autonomous Region, China; 3The People’s Hospital of Guigang, Guigang 537199, Guangxi Zhuang Autonomous Region, China
  • Received:2023-10-08 Accepted:2023-11-25 Online:2024-09-28 Published:2024-01-26
  • Contact: Qin Gang, MD, Chief physician, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi Zhuang Autonomous Region, China
  • About author:Fan Yidong, Master candidate, Guangxi University of Chinese Medicine, Nanning 530222, Guangxi Zhuang Autonomous Region, China
  • Supported by:
    National Natural Science Foundation of China, Nos. 81860793 and 82360939 (both to QG); Natural Science Foundation of Guangxi Zhuang Autonomous Region, No. 2020GXNSFAA297140 (to QG); Postgraduate Education Innovation Program of Guangxi University of Chinese Medicine in 2022, No. YCSY2022028 (to FYD)

Abstract: BACKGROUND: Rheumatoid arthritis is a chronic systemic autoimmune disease. It is important to study the immunological changes involved in it for diagnosis and treatment.
OBJECTIVE: To identify immune-related biomarkers associated with rheumatoid arthritis utilizing bioinformatics techniques and examine alterations in immune cell infiltration as well as the relationship between immune cells and biomarkers.
METHODS: Differential expression analysis was used to identify the immune-related genes that were up-regulated in rheumatoid arthritis based on the GEO and Immport databases. Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) enrichment analyses were used to investigate the possible function of these elevated genes. The immunological characteristic genes associated with rheumatoid arthritis were screened using least absolute shrinkage and selection operator (Lasso) and support vector machine recursive feature elimination (SVM-RFE). Independent datasets were used for difference validation, and the diagnostic performance was evaluated by plotting receiver operating characteristic curves for feature genes. Immune cell infiltration was used to analyze the differential profile of immune cells in rheumatoid arthritis and the correlation between the characterized genes and immune cells. In order to ascertain the causal relationship between monocytes and rheumatoid arthritis in immune cells, Mendelian randomization analysis was ultimately employed.
RESULTS AND CONCLUSION: There were 39 upregulated differentially expressed genes in rheumatoid arthritis. The genes were primarily enriched in chemotaxis, cytokine activity, and immune receptor activity, according to GO enrichment analysis, while kEGG enrichment analysis revealed that the genes were considerably enriched in the tumor necrosis factor signaling pathway and peripheral leukocyte migration. Lasso and SVM-RFE identified five feature genes: CXCL13, SDC1, IGLC1, PLXNC1, and SLC29A3. Independent dataset validation of the feature genes found them to be similarly highly expressed in rheumatoid arthritis samples, with area under the curve values greater than 0.8 for all five feature genes in both datasets. Immune cell infiltration indicated that most immune cells, including natural killer cells and monocytes, exhibited increased levels of infiltration in rheumatoid arthritis samples. The correlation analysis revealed a significant positive correlation between memory B cells and immature B cells and these five feature genes. Correlation analysis showed that the five feature genes were positively correlated with memory B cells and immature B cells. The inverse variance weighting method revealed that monocytes were associated with the risk of developing rheumatoid arthritis.

Key words: rheumatoid arthritis, machine learning, immune infiltration, genome-wide association studies, Mendelian randomization

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