Utilizing an integrated bioinformatics and machine learning approach to uncover biomarkers linking ulcerative colitis to purine metabolism-related genes.

Background: Ulcerative colitis (UC) is an increasing incidence of inflammatory disorder in the colon mucosa. One of the current research focuses is the alteration of metabolic networks in UC. One of the important aspects of this metabolic shift is the expression of purine metabolism genes (PMGs) vital for nucleic acid synthesis. Nevertheless, the precise function of PMGs in the pathophysiology of UC is not yet fully known. Methods: To this end, this study used state-of-the-art bioinformatics tools and approaches to discover and confirm the PMGs involved in UC. All the 114 candidate PMGs were compared for their expression levels. GSEA and GSVA were applied to define the functional and pathway implications of these PMGs. Lasso regression and SVM-RFE approaches were used for the identification of hub genes and to assess the diagnostic potential of eight PMGs in UC classification. The relationship between these critical PMGs and clinical features was also systematically evaluated as well. The expression levels of these eight PMGs were validated using datasets GSE206285 and GSE179285. Results: Using bioinformatics and machine learning, this work seeks to establish the involvement of PMGs in UC. From the LASSO and SVM models, 114 DE PMGs were selected and investigated to build a stable predictive model. Based on these studies, the following genes: IMPDH1, GUK1, POLE3, ADCY3, ADCY4, PDE6B, PNPT1 and PDE4D were suggested as potential biomarkers of UC. Gene ontology enrichment analysis revealed that these genes are implicated in the biological processes of particular relevance to immune and inflammatory responses. The study also provided a lot of information on the interaction between immune cells and PMGs indicating that these genes may control some immune-related pathways in UC. Moreover, drug-gene interaction analysis presents potential therapeutic opportunities for potential drug targets which were further confirmed through molecular docking. Mendelian randomization analysis revealed that ADCY4 and PDAZN are involved in PMG-related processes, thus opening new possibilities for treatment. Conclusions: This work reveals eight PMGs closely related to UC and provides new perspectives on possible markers of this inflammatory disease. These findings not only increase the understanding of the pathogenesis of UC but also offer potential for improving the surveillance of disease and its progression.