机构：[1]Department of Pain Medicine, The Affiliated Anning First People’s Hospital of Kunming University of Science and Technology, Kunming 650302, Yunnan, China.[2]School of Basic Medical Sciences, Kunming University of Science and Technology, Kunming 650500, Yunnan, China.[3]Faculty of Life Science and Technology & The Affiliated Anning First People’s Hospital, Kunming University of Science and Technology, Kunming 650500, Yunnan, China.[4]Department of Orthopedics, 920 Hospital of the Joint Logistic Support Force, People’s Liberation Army of China, Kunming 650032, Yunnan, China.
IntroductionLyme disease (LD), caused by the spirochete Borrelia burgdorferi (Bb), is a multisystem disorder with early symptoms such as erythema migrans and late manifestations including arthritis and neuroborreliosis. The molecular mechanisms driving tissue damage and inflammatory dysregulation in LD remain incompletely characterized. Given the central role of peripheral blood mononuclear cells (PBMCs) in orchestrating immune responses, we aimed to identify optimal feature genes (OFGs) within PBMCs associated with LD pathogenesis and delineate their immune infiltration patterns using integrated bioinformatics.MethodsTranscriptomic datasets (GSE42606, GSE68765, GSE103481) were retrieved from GEO. Differential expression analysis identified LD-related genes. Weighted Gene Co-expression Network Analysis (WGCNA) screened disease-associated modules. Feature selection was performed via SVM-Recursive Feature Elimination (SVM-RFE), Least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) to pinpoint OFGs. Immune cell infiltration was quantified using CIBERSORT, followed by correlation analysis between OFGs and immune subsets. The Single-gene gene set enrichment analysis (GSEA) was performed to explore the functional associations of OFGs. Biological pathways linked to OFGs were inferred by single-sample GSEA (ssGSEA). Diagnostic utility was assessed via ROC curves and nomogram modeling. Finally, we used RT-qPCR to confirm the bioinformatics results.ResultsOur study identified 174 DEGs among the LD patients, with 156 genes located within the "turquoise" module by WGCNA, exhibiting the most robust correlation with clinical characteristics. Among these, KIAA1199 turned out to be the unique OFG, selected via three distinct machine learning methodologies, possessing exceptional diagnostic potential. The Single-gene gene set enrichment analysis showed KIAA1199 was strongly correlated with multiple immune-related pathways. Furthermore, RT-qPCR validated candidate gene expression within a THP-1 cellular model.ConclusionIn conclusion, this study integrated WGCNA and machine learning methodologies to identify one core gene associated with LD from PBMC gene expression data: KIAA1199. The predictive model constructed using these genes demonstrated robust diagnostic accuracy, providing a basis for further research on host immune responses and the development of new diagnostic methods.
基金：
the internal projects of the First People's Hospital of Anning, affiliated with Kunming University of Science and Technology; Research Center for Clinical Medicine within the First Affiliated Hospital of Kunming Medical University
第一作者机构：[1]Department of Pain Medicine, The Affiliated Anning First People’s Hospital of Kunming University of Science and Technology, Kunming 650302, Yunnan, China.[2]School of Basic Medical Sciences, Kunming University of Science and Technology, Kunming 650500, Yunnan, China.
通讯作者：
通讯机构：[1]Department of Pain Medicine, The Affiliated Anning First People’s Hospital of Kunming University of Science and Technology, Kunming 650302, Yunnan, China.[2]School of Basic Medical Sciences, Kunming University of Science and Technology, Kunming 650500, Yunnan, China.
推荐引用方式(GB/T 7714)：
Dong Yan,Liu Meng,Luo Yanshuang,et al.Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning[J].EUROPEAN JOURNAL OF MEDICAL RESEARCH.2025,30(1):doi:10.1186/s40001-025-03108-y.
APA：
Dong, Yan,Liu, Meng,Luo, Yanshuang,Chen, Yantong,Chen, Xuesong...&Zhou, Guozhong.(2025).Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning.EUROPEAN JOURNAL OF MEDICAL RESEARCH,30,(1)
MLA：
Dong, Yan,et al."Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning".EUROPEAN JOURNAL OF MEDICAL RESEARCH 30..1(2025)
