Quantitative Analysis of Quality of Life and Exploration of Influencing Factors in Patients Undergoing Radical Prostatectomy.

OBJECTIVE: To quantify and evaluate the quality of life of patients undergoing radical prostatectomy using the FACT-P scoring system, and to explore the predictive factors for postoperative quality of life. METHODS: Clinical data of 249 patients who underwent radical prostatectomy in our hospital from January 2021 to October 2022 were analyzed. According to the surgical method and whether the subjective quality of life of the patient decreased significantly, the patients were divided into groups, and the predictive factors for changes in subjective quality of life after surgery were analyzed. RESULTS: A total of 192 cases were finally obtained (45 cases of fascia internal approach, 147 cases of traditional radical prostatectomy), and patients who underwent fascia internal approach (FACT-P: 110.15 ± 10.55) had better postoperative quality of life than those who underwent extra-fascial radical prostatectomy (FACT-P: 102.30 ± 6.75) (P < .01). One hundred fourteen patients reported a decrease in subjective quality of life, while 78 did not. The preoperative FACT-P score was an independent predictive factor (OR=0.719, P < .01), and when the preoperative score was <116 points, the possibility of no decrease in quality of life after surgery was higher. CONCLUSION: Fascia internal approach should be performed as much as possible for suitable surgical patients, and for patients with a preoperative FACT-P score ≥116 points, the possibility of a decrease in quality of life after surgery should be fully communicated.

Bladder cancer biomarker screening based on non-targeted urine metabolomics.

PURPOSE: 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 methods to screen for metabolites that are significantly different between the urine of bladder cancer patients and cancer-free controls. METHODS: In this study, liquid chromatography-mass spectrometry was used to analyze the urine of bladder cancer patients (n = 57) and the cancer-free controls (n = 38) by non-targeted metabolomic analysis and metabolite identification. RESULTS: The results showed that there were significant differences in the expression of 27 metabolites between bladder cancer patients and the cancer-free controls. CONCLUSION: In the multivariate statistical analysis of this study, the urinary metabolic profile data of bladder cancer patients were analyzed, and the receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of bladder cancer through these candidate biomarkers.

Development and Validation of an IL6/JAK/STAT3-Related Gene Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma.

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.

Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma.

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.