Investigating the relevance of nucleotide metabolism in the prognosis of glioblastoma through bioinformatics models.

Nucleotide metabolism (NM) is a fundamental process that enables the rapid growth of tumors. Glioblastoma (GBM) primarily relies on NM for its invasion, leading to severe clinical outcomes. This study focuses on NM to identify potential biomarkers associated with GBM. Publicly available databases were used as the primary data source for this study, excluding biological tissue samples. We identified and evaluated key genes involved in NM, followed by developing and validating a prognostic model. Patients were classified into high- and low-risk groups based on this model, and the two groups were compared with respect to cellular immunity and mutation profiles. The biomarkers were confirmed using real-time reverse-transcriptase polymerase chain reaction. Our study identified UPP1, CDA, NUDT1, and ADSL as significant biomarkers associated with prognosis, all of which were upregulated in patients with GBM. The risk score and clinical factors such as age, sex, GBM stage, MGMT promoter status, and IDH mutation status were found to be independent prognostic factors. Patients with glioblastoma showed a higher overall mutation burden. Using bioinformatics, this study identifies key factors associated with NM in GBM that may influence patient prognosis. This study enhances our understanding of GBM, provides valuable insights for further research, and serves as a reference for evaluating patient outcomes.