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<p>Lyme disease (LD) presents significant diagnostic challenges due to the absence of a reliable screening method for initial detection. This study aimed to identify potential biomarkers using bioinformatics and machine learning algorithms, which may contribute to future biomarker-based research for Lyme disease diagnostics. The gene expression profile datasets GSE145974 and GSE63085 were analyzed using machine learning to identify hub genes among differentially expressed genes. High-throughput data and receiver operating characteristic curves were used to validate these genes. The molecular mechanisms underlying LD were explored using functional enrichment analysis. The correlation between immune cell counts and insomnia in LD was further validated using clinical data from the GEO database. Gene set enrichment analysis indicated that hub genes were enriched in circadian rhythms. The integration of machine learning revealed <i>FCGR1B</i>, <i>MPP1</i>, and <i>HSPA6</i> as potential central genes involved in immune response and diagnostic biomarkers for Lyme disease. Immune infiltration analysis showed that LD is frequently associated with the monocyte–macrophage system and humoral immunity. This study provides novel insights into the targeted treatment of LD by revealing novel diagnostic biomarkers.</p>

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Identification of potential biomarkers for Lyme disease using bioinformatics and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Qi-Wen Lan,
  • Yao-Zhu Wu,
  • Zhi-Shan Wang,
  • Xin-Lei Hu,
  • Yi-Lin Huang,
  • Xun-Jie Cao,
  • Yi-Fei Li,
  • Xu-Guang Guo,
  • Jun-Jie Wang

摘要

Lyme disease (LD) presents significant diagnostic challenges due to the absence of a reliable screening method for initial detection. This study aimed to identify potential biomarkers using bioinformatics and machine learning algorithms, which may contribute to future biomarker-based research for Lyme disease diagnostics. The gene expression profile datasets GSE145974 and GSE63085 were analyzed using machine learning to identify hub genes among differentially expressed genes. High-throughput data and receiver operating characteristic curves were used to validate these genes. The molecular mechanisms underlying LD were explored using functional enrichment analysis. The correlation between immune cell counts and insomnia in LD was further validated using clinical data from the GEO database. Gene set enrichment analysis indicated that hub genes were enriched in circadian rhythms. The integration of machine learning revealed FCGR1B, MPP1, and HSPA6 as potential central genes involved in immune response and diagnostic biomarkers for Lyme disease. Immune infiltration analysis showed that LD is frequently associated with the monocyte–macrophage system and humoral immunity. This study provides novel insights into the targeted treatment of LD by revealing novel diagnostic biomarkers.