Determination of Nitrogen Metabolism-Related Prognostic Signatures for Forecasting Bladder Cancer Prognosis.

Abstract

Background: Bladder cancer is one of the major health threats worldwide, and aberrant regulation of nitrogen metabolism is closely related to its development. Understanding the role of nitrogen metabolism-related genes in BC is pivotal for the development of new therapeutic strategies and prognostic assessment.

Aim and objectives: This study aimed to explore the prognostic factors associated with nitrogen metabolism in bladder cancer (BC) and to construct a prognostic model.

Methods: Differential expression gene analysis was performed to identify genes associated with nitrogen metabolism by analyzing mRNA expression data from BC patients. The prognostic relationship between these genes and BC patients was analyzed using univariate Cox regression. One hundred one combinatorial machine learning methods were applied for feature selection, and key prognostic genes were identified based on the method with the highest combined score. Immunocyte infiltration analysis was carried out to assess the tumor microenvironmental characteristics of patients in different risk groups.

Results: Twenty-five genes significantly associated with prognosis were identified from nitrogen metabolism-related genes. Twenty-three most prognostically predictive signature genes were screened under feature screening with multiple machine-learning models. Immune cell infiltration analysis showed that patients in the high-risk group had significantly different immune cell infiltration, suggesting that these genes may influence BC progression by regulating immune escape mechanisms. These results provide new biomarkers and potential therapeutic targets for precision treatment and prognostic assessment of BC.

Conclusion: The expression patterns of nitrogen metabolism-related genes identified can be used as effective biomarkers for bladder cancer prognosis, providing a scientific basis for personalized treatment. Future studies can further explore the specific biological functions and mechanisms of action of these genes to promote more effective clinical applications.