Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning
In 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.
Authors
- Yanshuang Luo (ORCID: https://orcid.org/0009-0008-8091-4836)
- Guozhong Zhou (ORCID: https://orcid.org/0009-0001-9112-6631)
- Yantong Chen (ORCID: https://orcid.org/0000-0002-7564-7226)
- Xingbo Cai
- Chao Song (ORCID: https://orcid.org/0000-0003-0936-7725)
- Xiaorong Liu (ORCID: https://orcid.org/0000-0001-9724-6493)
- Yan Dong (ORCID: https://orcid.org/0000-0002-4018-3839)
- Xuesong Chen (ORCID: https://orcid.org/0009-0009-6011-6738)
- Feikun Yang (ORCID: https://orcid.org/0000-0002-7295-0553)
- Boping Liu (ORCID: https://orcid.org/0000-0001-6791-8922)
Institutions
- Kunming University of Science and Technology (CN)
- Kunming Medical University (CN)
- First People's Hospital of Yunnan Province (CN)
- The First Hospital of Kunming (CN)
- First Affiliated Hospital of Kunming Medical University (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1186/s40001-025-03108-y
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00