Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes

Abstract

Abstract Background 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. Methods 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. Results 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. Conclusions 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.

Keywords

• Asthma
• Pyrimidine metabolism-related genes (PyMGs)
• Lasso regression
• SVM-RFE
• Bioinformatics