Background: Among primary intracranial neoplasms in adults, glioblastoma multiforme stands out for both its prevalence and its exceptionally invasive character. Uric acid-related genes (UARGs) may enhance tumor cell invasiveness and drug resistance by promoting oxidative stress responses. This study aimed to elucidate uric acid-driven mechanisms in glioblastoma, focusing on risk stratification and therapeutic vulnerability. Methods: Transcriptomic profiles of GBM were retrieved from TCGA and GEO repositories, followed by performing differentially expressed analysis, univariate Cox and LASSO regression, in order to screen prognostic UARGs and construct a risk model. Then, prognostic analyses were expanded by performing immune microenvironment analysis, drug sensitivity analysis, tumor mutation analysis, independent prognostic analysis, and nomogram construction. Additionally, dataset GSE162631 was interrogated to pinpoint pivotal cell subsets and to map intercellular communication as well as pseudo-time analysis. Results: A risk model incorporating six prognostic UARGs (TIMP1, PLAUR, CTSB, KLF10, RARRES2, and PTPRN) was constructed and identified as a favorable prognostic signature. Resting dendritic cells and drugs (including acetalax and trametinib) were found to be associated with GBM patients’ risk stratification. Low-risk patients showed relatively higher mutation rates of PTEN and TP53. A nomogram was developed based on RARRES2 and PTPRN, which exhibited favorable predictive performance for GBM prognosis. Furthermore, scRNA-seq profiling identified dendritic cells (DCs), macrophages, and T cells as key populations in the tumor microenvironment. Intercellular communication inference indicated relatively strong DCs-macrophage crosstalk, and pseudo-time analysis linked prognostic UARG expression to the differentiation trajectory of critical cell subsets. Conclusions: This study identified uric acid-related genes as potential independent indicators of clinical outcomes in glioblastoma progression. A novel prognostic UARG-associated signature was developed and validated, which showed potential in predicting GBM patient outcomes.
Sun et al. (Mon,) studied this question.