In silico analysis of the immune microenvironment in bladder cancer.

BACKGROUND: Infiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to perform cell phenotyping and transcriptional profiling of the tumour immune microenvironment and analyse the association of distinct cell subsets and genes with BC prognosis. METHODS: Clinical data of 412 patients with BC and 433 transcription files for normal and cancer tissues were downloaded from The Cancer Genome Atlas. The CIBERSORT algorithm was used to determine the relative abundance of 22 immune cell types in each sample and the ESTIMATE algorithm was used to identify differentially expressed genes within the tumour microenvironment of BC, which were subjected to functional enrichment and protein-protein interaction (PPI) analyses. The association of cell subsets and differentially expressed genes with patient survival and clinical parameters was examined by Cox regression analysis and the Kaplan-Meier method. RESULTS: Resting natural killer cells and activated memory CD4+ and CD8+ T cells were associated with favourable patient outcome, whereas resting memory CD4+ T cells were associated with poor outcome. Differential expression analysis revealed 1334 genes influencing both immune and stromal cell scores; of them, 97 were predictive of overall survival in patients with BC. Among the top 10 statistically significant hub genes in the PPI network, CXCL12, FN1, LCK, and CXCR4 were found to be associated with BC prognosis. CONCLUSION: Tumour-infiltrating immune cells and cancer microenvironment-related genes can affect the outcomes of patients and are likely to be important determinants of both prognosis and response to immunotherapy in BC.

Bladder cancer survival nomogram: Development and validation of a prediction tool, using the SEER and TCGA databases.

Bladder cancer (BC) is a common malignancy associated with high morbidity and mortality, however, accurate and convenient risk assessment tools applicable to BC patients are currently lacking. Previous studies using nomograms to evaluate bladder cancer (BC) survival have been based on small samples. Using a large dataset, this study aimed to construct more precise clinical nomograms to effectively predict bladder cancer survival.Data on patients with pathologically-confirmed bladder cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Additional BC patient data for an external validation cohort were extracted from the Cancer Genome Atlas (TCGA) database. Clinical parameters that constituted potential risk factors were reviewed and analyzed using univariate and multivariate Cox proportional hazards regression. A nomogram was constructed with parameters that significantly correlated with the overall survival (OS). Prognostic performance of a nomogram was assessed using the concordance index (c-index), area under the receiver operating characteristic curve (AUC), and a calibration curve. The model was then tested with data from an internal and external validation cohort. Patients' survival was analyzed and compared with the Kaplan-Meier (KM) method.Multivariate Cox regression showed that age, sex, race, stage_T1, stage_T2a, stage_T2b, stage_T3a, stage_Ta, stage_Tis, stage_N, stage_M were independent predictors of BC survival. A nomogram was constructed based on these factors. The c-index of the nomogram was 0.7916 (95% confidence interval CI, 0.79-0.80). The calibration curve showed excellent agreement between the predicted and observed values. The c-index for the internal validation cohort was 0.7917 (95% CI 0.79-0.80), which was higher than for the training cohort, suggesting robustness of the model. For the training cohort, the AUC for the 3- and the 5-year survival was 0.82 and 0.813, respectively. The c-index for the TNM-based model was superior to that for the AJCC-TNM classification.The models presented in this study might be suitable for clinical use, supporting clinicians in their individualized assessment of expected survival in BC patients. They might also be used as a layered tool for clinical research.