Background: Bladder cancer is one of the most common malignancies of the urinary system, and its screening relies heavily on invasive cystoscopy, which increases the risk of urethral injury and infection. This study aims to use nontargeted metabolomics approaches to screen the metabolites that are expressed signi cantly differently from the urine of bladder cancer patients and the cancer-free controls.Methods and Results: This study performed non-targeted metabolomic analysis and metabolite identi cation on the urine of bladder cancer patients (n=57) and the cancer-free controls (n=38) using a liquid chromatography-mass spectrometry analyzer. The results showed that there were signi cant differences in the expression of 27 metabolites between bladder cancer patients and the cancer-free controls.Conclusions: In the multivariate statistical analysis of this study, the urinary metabolic pro le data of bladder cancer patients are analyzed, and the receiver operating characteristic curve analysis shows that it is possible to perform non-invasive clinical diagnoses of bladder cancer through these candidate biomarkers.

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.

Background: Traditional clinicopathological features (TNM, pathology grade) are often insufficient in predictive prognosis accuracy of clear cell renal cell carcinoma (ccRCC). The IL6-JAK-STAT3 pathway is aberrantly hyperactivated in many cancer types, and such hyperactivation is generally associated with a poor clinical prognosis implying that it can be used as a promising prognosis indicator. The relation between the IL6-JAK-STAT3 pathway and ccRCC remains unknown.Methods: We evaluated the levels of various cancer hallmarks and filtered out the promising risk hallmarks in ccRCC. Subsequently, a prognosis model based on these hallmark-related genes was established via weighted correlation network analysis and Cox regression analysis. Besides, we constructed a nomogram based on the previous model with traditional clinicopathological features to improve the predictive power and accuracy.Results: The IL6-JAK-STAT3 pathway was identified as the promising risk hallmarks in ccRCC, and the pathway-related prognosis model based on five genes was built. Also, the nomogram we developed demonstrated the strongest and most stable survival predictive ability.Conclusion: Our study would provide new insights for guiding individualized treatment of ccRCC patients.

Background: Bladder cancer is one of the most common malignancies of the urinary system, and its screening relies heavily on invasive cystoscopy, which increases the risk of urethral injury and infection. This study aims to use non-targeted metabolomics approaches to screen the metabolites that are expressed significantly differently from the urine of bladder cancer patients and the cancer-free controls. Methods and Results: This study performed non-targeted metabolomic analysis and metabolite identification on the urine of bladder cancer patients (n=57) and the cancer-free controls (n=38) using a liquid chromatography-mass spectrometry analyzer. The results showed that there were significant differences in the expression of 27 metabolites between bladder cancer patients and the cancer-free controls. Conclusions: In the multivariate statistical analysis of this study, the urinary metabolic profile data of bladder cancer patients are analyzed, and the receiver operating characteristic curve analysis shows that it is possible to perform non-invasive clinical diagnoses of bladder cancer through these candidate biomarkers.

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