VSports - Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The . gov means it’s official. Federal government websites often end in VSports app下载. gov or . mil. Before sharing sensitive information, make sure you’re on a federal government site. .

Https

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. V体育官网.

. 2025 Sep 26;30(1):860.
doi: 10.1186/s40001-025-03108-y.

Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning (V体育官网入口)

Yan Dong  1   2 , Meng Liu  3 , Yanshuang Luo  3 , Yantong Chen  3 , Xuesong Chen  1 , Xiaorong Liu  1 , Xingbo Cai  4 , Fusong Yang  1 , Chao Song  5 , Guozhong Zhou  6   7
Affiliations

Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning

Yan Dong et al. Eur J Med Res. .

"VSports在线直播" Abstract

Introduction: Lyme 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 VSports手机版. .

Methods: Transcriptomic 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 V体育安卓版. .

Results: Our 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 V体育ios版. Furthermore, RT-qPCR validated candidate gene expression within a THP-1 cellular model. .

Conclusion: In conclusion, this study integrated WGCNA and machine learning methodologies to identify one core gene associated with LD from PBMC gene expression data: KIAA1199 VSports最新版本. 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. .

Keywords: Borrelia burgdorferi; Bioinformatics analysis; Immune infiltration; Machine learning (ML); Weighted gene co-expression network analysis (WGCNA) V体育平台登录. .

PubMed Disclaimer

Conflict of interest statement (VSports在线直播)

Declarations VSports注册入口. Ethics approval and consent to participate: Not required. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A detailed flowchart about the study
Fig. 2
Fig. 2
Differentially expressed genes (DEGs) between Lyme disease and normal controls. A PCA plot before batch correction. B PCA plot after batch correction. C Volcano plot of DEGs. Data points in red are upregulated genes, and in green are downregulated genes. The top upregulated and downregulated genes are shown. D A Heatmap of the top 50 upregulated DEGs and top 50 downregulated DEGs are shown
Fig. 3
Fig. 3
The GSEA enrichment analysis of DEGs. A GSEA results of the top 5 upregulated pathways. B GSEA results of the top 5 downregulated pathways. On each graph is a line graph, and on the bottom is a GSEA enrichment map. C Ridgeplot visualizing the enrichment patterns across multiple gene sets
Fig. 4
Fig. 4
Identification of module genes via WGCNA. A Eigengene dendrogram and heatmap to illustrate the meta-modules of correlated eigengenes for the LD. B Clustering dendrogram and merging of the gene co-expression modules represented by different colors in LD. C Heatmap of the module–trait relationship in LD. Red indicates a positively correlation with phenotypic traits; blue indicates a negative correlation. The number in each cell represents the correlation coefficient
Fig. 5
Fig. 5
Determine the intersection 156 genes of WGCNA and DEGs and Function enrichment analysis. A Venn diagram demonstrated the intersection set of WGCNA and EDGs. B Disease Ontology (DO) enrichment analysis. C Gene Ontology (GO) enrichment analysis. BP, biological processes; CC, cellular components; MF, molecular functions. D KEGG enrichment analysis of intersection genes. The bubble shows significant items according to the P-value (The enrichment cutoff was set to p < 0.05)
Fig. 6
Fig. 6
Screening for candidate diagnostic biomarkers for Lyme disease using three machine learnings. A, B The optimal feature genes obtained from LASSO, (C) SVM-RFE, and (D, E) random forest. The final selection of shared gene was determined through the LASSO, SVM-RFE algorithm, and random forest. Finally, shared gene KIAA1199 was identified as the hub gene (OFG)
Fig. 7
Fig. 7
Single-gene GSEA-KEGG pathway analysis of KIAA1199. A Single-gene GSEA results of the top five upregulated pathways. B Single-gene GSEA results of the top five downregulated pathways. On each graph is a line graph, and on the bottom is a GSEA enrichment map
Fig. 8
Fig. 8
The results of single-sample GSEA for Lyme disease and the hub gene (OFG) KIAA1199. A The relative immune infiltration score between Lyme disease and healthy group. B The relative immune infiltration with the hub gene (OFG) KIAA1199. Red represents a positive correlation, whereas green represents a negative correlation. The darker the color, the higher the correlation. (*p < 0.05, **p < 0.01, ***p < 0.001,#p < 0.02,NS: p ≥ 0.05)
Fig. 9
Fig. 9
CIBERSORT algorithm analysis of immune microenvironment characteristics. A Summary of immune infiltration of 22 immune cell subpopulations from 83 samples, and heatmap of 22 immune-infiltrating cell populations between the two groups. B Violin plot of differential expression of 22 immune-infiltrating cell populations. C Correlation matrix of 22 immune-infiltrating cell populations in 83 samples. Green indicates control group, red indicates Lyme disease group. D Correlation analysis between hub gene KIAA1199 and immune cells. p < 0.05 (red) indicates a significant correlation between gene expression and immune cell infiltration
Fig. 10
Fig. 10
ROC curve and box-plot of the expression levels of the hub gene (OFG) KIAA1199 and validation of the biomarkers by RT-qPCR in THP-1 cells. A Box-plot of the expression level of KIAA1199 in the integrated 83 samples dataset. B ROC curve of KIAA1199 in the integrated 83 samples dataset. C Box-plot of the expression level of KIAA1199 in the external validation dataset GSE103481. D ROC curve of KIAA1199 in the external validation dataset GSE103481. E Relative expression levels of qRT-PCR of KIAA1199 in THP-1 cells (*p < 0.05, **p < 0.01, ***p < 0.001)
See this image and copyright information in PMC

VSports app下载 - References

    1. Skare JT, Garcia BL. Complement evasion by Lyme disease spirochetes. Trends Microbiol. 2020;28:889–99. 10.1016/j.tim.2020.05.004. - PMC - PubMed
    1. Mead P. Epidemiology of lyme disease. Infect Dis Clin North Am. 2022;36:495–521. 10.1016/j.idc.2022.03.004. - PubMed (V体育ios版)
    1. Kugeler KJ, Earley A, Mead PS, Hinckley AF. Surveillance for Lyme disease after implementation of a revised case definition - United States, 2022. MMWR Morb Mortal Wkly Rep. 2024;73:118–23. 10.15585/mmwr.mm7306a1. - "VSports最新版本" PMC - PubMed
    1. Wong KH, Shapiro ED, Soffer GK. A review of post-treatment Lyme disease syndrome and chronic Lyme disease for the practicing immunologist. Clin Rev Allergy Immunol. 2022;62:264–71. 10.1007/s12016-021-08906-w. - PubMed
    1. Miklossy J. Alzheimer’s disease - a neurospirochetosis. Analysis of the evidence following Koch’s and Hill’s criteria. J Neuroinflammation. 2011;8:90. 10.1186/1742-2094-8-90. - PMC (VSports在线直播) - PubMed

LinkOut - more resources