哮喘是一种常见的慢性炎症性疾病，是由遗传易感性和环境暴露之间的多方面相互作用造成的。尽管在破译其病理生理学方面取得了长足的进步，但哮喘复杂的分子基础仍然难以捉摸。人们的关注焦点越来越多地转向伴随哮喘的代谢异常，特别是在嘧啶代谢（PyM）领域——核苷酸合成和降解的关键途径。虽然 PyM 的治疗相关性已在多种疾病中得到认可，但其对哮喘病理学的具体贡献尚未得到充分探索。本研究采用复杂的生物信息学方法来描述和确认 PyM 基因 (PyMGs) 在哮喘中的参与，旨在弥合这一重大知识空白。本研究采用尖端生物信息学技术，旨在阐明 PyMGs 在哮喘中的作用。我们对 31 种 PyMG 进行了详细检查，以评估它们的差异表达。通过基因集富集分析（GSEA）和基因集变异分析（GSVA），我们探索了与这些基因相关的生物学功能和途径。我们利用 Lasso 回归和支持向量机递归特征消除 (SVM-RFE) 来查明关键中心基因，并确定八个 PyMG 在区分哮喘方面的诊断准确性，并辅之以与疾病临床特征的广泛相关性研究。使用数据集 GSE76262 和 GSE147878 进行基因表达验证。我们的分析显示，11 个 PyMGs-DHODH、UMPS、NME7、NME1、POLR2B、POLR3B、POLR1C、POLE、ENPP3、RRM2B、TK2 与哮喘显着相关。这些基因在 RNA 剪接、解剖结构维护和涉及嘌呤化合物的代谢过程等重要生物过程中发挥着至关重要的作用。这项研究确定了哮喘发病机制的核心 11 种 PyMG，将它们确立为该疾病的潜在生物标志物。我们的研究结果增强了对哮喘分子机制的理解，并为改进诊断、监测和进展评估开辟了新途径。通过提供对非癌症病理学的新见解，我们的工作引入了新颖的视角，并为该领域的进一步研究奠定了基础。© 2024。作者。

Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)-a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge.Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878.Our analyses revealed that eleven PyMGs-DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2-are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds.This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma's molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field.© 2024. The Author(s).