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<p>This study employed bioinformatics to identify immune-related diagnostic and therapeutic target genes in thymoma, offering a theoretical foundation for clinical diagnosis and treatment of the disease. In this study, machine learning techniques were employed to identify and validate potential biomarkers, leading to the construction of a diagnostic nomogram. Immune infiltration analysis was subsequently conducted to explore the relationships between these biomarkers and immune cell populations. Finally, the findings were validated through RT-qPCR and Western blotting experiments, ensuring the robustness of the results. Three critical biomarkers were selected for thymoma, with CCR9 and CCL13 confirmed as the final biomarkers through ROC analysis, both demonstrating AUC values exceeding 0.7. Immune infiltration analysis revealed significant differences in 14 types of immune cells between the thymoma and control groups, highlighting a strong association between macrophages and the biomarkers. A nomogram for thymoma was constructed using these 2 biomarkers, exhibiting robust performance. Ultimately, validation experiments using RT-qPCR and Western blotting confirmed the consistency of CCR9 and CCLI3 expression with the bioinformatics results. CCR9 and CCL13 were identified in this study as immune-related biomarkers associated with thymoma, which might provide a theoretical foundation for the development of targeted gene therapies.</p>

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Unveiling CCR9 and CCL13 as immune biomarkers and therapeutic targets in thymoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月6日

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  • Peng Lu,
  • Lixun Chai

摘要

This study employed bioinformatics to identify immune-related diagnostic and therapeutic target genes in thymoma, offering a theoretical foundation for clinical diagnosis and treatment of the disease. In this study, machine learning techniques were employed to identify and validate potential biomarkers, leading to the construction of a diagnostic nomogram. Immune infiltration analysis was subsequently conducted to explore the relationships between these biomarkers and immune cell populations. Finally, the findings were validated through RT-qPCR and Western blotting experiments, ensuring the robustness of the results. Three critical biomarkers were selected for thymoma, with CCR9 and CCL13 confirmed as the final biomarkers through ROC analysis, both demonstrating AUC values exceeding 0.7. Immune infiltration analysis revealed significant differences in 14 types of immune cells between the thymoma and control groups, highlighting a strong association between macrophages and the biomarkers. A nomogram for thymoma was constructed using these 2 biomarkers, exhibiting robust performance. Ultimately, validation experiments using RT-qPCR and Western blotting confirmed the consistency of CCR9 and CCLI3 expression with the bioinformatics results. CCR9 and CCL13 were identified in this study as immune-related biomarkers associated with thymoma, which might provide a theoretical foundation for the development of targeted gene therapies.