Background: Bladder cancer is the most common malignancy of the urinary system. However, patient prognosis and treatment outcomes in bladder cancer are difficult to predict owing to high tumor heterogeneity. Given that abnormal glutamine metabolism has been identified as a key factor driving the progression of bladder cancer, it is necessary to assess the prognosis and therapeutic efficacy of bladder cancer treatments based on an analysis of glutamine metabolism-related genes.
Methods: We used bladder cancer sample data downloaded from The Cancer Genome Atlas to identify glutamine metabolism-related genes as prognostic markers, and established a novel Glutamine Metabolism Immunity Index (GMII) based on univariate and multivariate COX regression analyses. On the basis of GMII values, bladder cancer patients were divided into high- and low-risk groups, and systematic analysis was conducted for clinical features, somatic mutations, immune cell infiltration, chemotherapeutic response, and immunotherapeutic efficacy. Candidate small-molecule drugs targeting the GMII core target proteins were identified based on molecular docking analysis.
Results: The GMII consisting of eight independent prognostic genes was established to be an excellent tool for predicting the survival in patients with bladder cancer and was validated using multiple datasets. Compared with patients in the high-risk group, those in the low-risk group had significantly better responses to gemcitabine and immune checkpoint blockade. In addition, we predicted 12 potential small-molecule drugs that could bind to three of the GMII core target proteins.
Conclusions: The GMII can be used to accurately predict the prognosis and immunotherapeutic response of bladder cancer patients, as well as candidate small-molecule drugs. Furthermore, the novel "Glutamine Metabolism-related Gene"-guided strategy for predicting survival and chemo-immunotherapeutic efficacy may also be applicable for cancers other than bladder cancer.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9995899 | PMC |
| http://dx.doi.org/10.3389/fimmu.2023.1111319 | DOI Listing |
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