Clinical Outcomes and Prognosis Analysis of Younger Bladder Cancer Patients

The Role of AHCY Expression in Bladder Urothelial Carcinoma: A Bioinformatics and Experimental Analysis

Background

Although adenosylhomocysteinase (AHCY) is crucial to the oncogenesis and growth of some cancers, it is unknown how this affects bladder urothelial carcinoma (BLCA). Investigating the variations in AHCY expression in BLCA and examining the relationship between AHCY expression and BLCA patient prognosis were the goals of this investigation.

Methods

By leveraging The Cancer Genome Atlas (TCGA) database, we undertook a meticulous examination of AHCY expression levels, juxtaposing them between BLCA and normal tissues. Subsequently, Kaplan-Meier analysis and COX regression and nomogram was used to assess the effect of AHCY on the survival of BLCA patients. We further elaborated on the possible enriched pathways of AHCY and its immune relevance. In addition, we employed si-RNA technology to downregulate the AHCY gene expression and subsequently utilized quantitative real-time PCR (qRT-PCR), CCK-8, cell scratch assays, and Transwell migration assays to validate the pivotal role of AHCY in BLCA.

Results

The expression of AHCY was associated with various types of malignancies (including BCLA). In BLCA cancer tissues, there was an observed upregulation of AHCY expression in comparison to paracancerous tissues. Increased expression of AHCY was linked to decreased overall survival (OS), clinical stage, N stage, and T stage in individuals with BLCA. The functional enrichment of AHCY related genes mainly involves biological processes such as rRNA metabolic processes, proteasome activity, and cell cycle regulation, etc. Furthermore, AHCY showed significant associations with m6A related genes and infiltration of immune cells (Especially for Th2 cells and T-gd lymphocytes). In vitro functional experiments substantiated that the inhibition of AHCY effectively suppresses the growth, migration, and invasion of bladder cancer cells.

Conclusion

This study provides novel insights into the role of AHCY in BLCA, which holds significant potential to contribute towards advancing the diagnosis and treatment of BLCA in the future.

The Added-value of Staging 18F-FDG PET/CT in the Prediction of Overall Survival in the Patients with Bladder Cancer

Objectives

This retrospective study aimed to evaluate the prognostic importance of 18F-fluorodeoxyglucose (18F-FDG)-positive pelvic lymph nodes (LNs) and extra-pelvic disease on staging 18F-FDG positron emission tomography/computed tomography (PET/CT) in patients with bladder cancer.

Methods

Bladder cancer patients who underwent staging 18F-FDG PET/CT were included in the study. Histopathologic features of tumors, therapy histories, presence of distinguishable tumors on CT and PET images, sizes and maximum standardized uptake value (SUVmax) of primary tumors, total numbers, sizes, and SUVmax of 18F-FDG-positive pelvic and extra-pelvic LNs, and total numbers and SUVmax of distant metastases (M1a/1b) were recorded. Patients were followed up until death or the last medical visit. Factors predicting overall survival were determined using Cox regression analysis.

Results

Fifty-five patients [median age: 70 (53-84), 48 (87.3%) male, 7 (12.7%) female] with bladder cancer were included in this study. Twenty-nine (52.7%) patients had 18F-FDG positive pelvic LNs, while 24 (43.7%) patients had 18F-FDG positive extra-pelvic disease. Patients with 18F-FDGpositive pelvic LNs had a higher rate of extra-pelvic disease (p=0.003). The median follow-up duration was 13.5 months. The median overall survival was 16.3 months [95% confidence interval (CI) 8.9-23.7]. The primary tumor distinguishability on PET (p=0.011) and CT (p=0.009) images, the presence of 18F-FDG-positive pelvic LNs (p<0.001) and 18F-FDG-positive extra-pelvic disease/distant metastases (M1a/M1b) (p<0.001), and the number of distant metastases (p=0.034) were associated with mortality. The 18F-FDG-positive extra-pelvic disease/distant metastases [p=0.029, odds ratio: 4.15 (95% CI 1.16-14.86)] was found to be an independent predictor of mortality in patients with bladder cancer.

Conclusion

The presence of 18F-FDG-positive extra-pelvic disease in pretreatment 18F-FDG PET/CT is an important prognostic factor in bladder cancer patients.

Development and validation of a nomogram for predicting suicide risk and prognostic factors in bladder cancer patients following diagnosis: A population-based retrospective study

This study sought to identify independent risk factors associated with suicide following a diagnosis of bladder cancer and to develop a predictive model with the potential to contribute to suicide rate reduction. Harnessing data from the Surveillance, Epidemiology, and End Results (SEER) database, we identified bladder cancer patients diagnosed between 2004 and 2015, randomly assigning them to training and validation cohorts. The Cox proportional hazard model was employed to identify relevant predictors, leading to the construction of prediction nomogram models. Validation of prognostic nomograms involved assessing the consistency index (C-index), receiver operating characteristic (ROC) curve, and calibration curve. A total of 109,961 eligible bladder cancer patients were enrolled, randomly divided into training and validation sets. Multivariate Cox regression analysis revealed that sex, marital status, tumor local status (T Stage), and lymph node metastatic conditions (N Stage) were independent predictors for suicide in bladder cancer patients. Evaluation of the nomogram's accuracy through the C-index and ROC curve demonstrated acceptable performance in both training and validation sets. Moreover, the calibration plot indicated moderate accuracy of the nomogram in both datasets. Overall, this study successfully identified risk factors for suicide among bladder cancer patients and developed a nomogram, offering individualized diagnosis, intervention, and risk assessment to mitigate the risk of suicide in this patient population.

Competitive Risk Model for Specific Mortality Prediction in Patients with Bladder Cancer: A Population-Based Cohort Study with Machine Learning

Background

Noncancer death accounts for a high proportion of all patients with bladder cancer, while these patients are often excluded from the survival analysis, which increases the selection bias of the study subjects in the prediction model.

Methods

Clinicopathological information of bladder cancer patients was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, and the patients were categorized at random into the training and validation cohorts. The random forest method was used to calculate the importance of clinical variables in the training cohort. Multivariate and univariate analyses were undertaken to assess the risk indicators, and the prediction nomogram based on the competitive risk model was constructed. The model's performance was evaluated utilizing the calibration curve, consistency index (C index), and the area under the receiver operator characteristic curve (AUC).

Results

In total, we enrolled 39285 bladder cancer patients in the study (27500 patients were allotted to the training cohort, whereas 11785 were allotted to the validation cohort). A competitive risk model was constructed to predict bladder cancer-specific mortality. The overall C index of patients in the training cohort was 0.876, and the AUC values were 0.891, 0.871, and 0.853, correspondingly, for 1-, 3-, and 5-year cancer-specific mortality. On the other hand, the overall C index of patients in the validation cohort was 0.877, and the AUC values were 0.894, 0.870, and 0.847 for 1-, 3-, and 5-year correspondingly, suggesting a remarkable predictive performance of the model.

Conclusions

The competitive risk model proved to be of great accuracy and reliability and could help clinical decision-makers improve their management and approaches for managing bladder cancer patients.