Risk factor analysis and optimal cutoff value selection of PSAD for diagnosing clinically significant prostate cancer in patients with negative mpMRI: results from a high-volume center in Southeast China.

BACKGROUND: Multi-parametric magnetic resonance imaging (mpMRI) is a diagnostic tool used for screening, localizing, and staging prostate cancer. Patients with Prostate Imaging Reporting and Data System (PI-RADS) score of 1 and 2 are considered negative mpMRI, with a lower likelihood of detecting clinically significant prostate cancer (csPCa). However, relying solely on mpMRI is insufficient to completely exclude csPCa, necessitating further stratification of csPCa patients using biomarkers. METHODS: A retrospective study was conducted on mpMRI-negative patients who underwent prostate biopsy at the First Affiliated Hospital of Zhejiang University from January 2022 to June 2023. A total of 607 patients were included based on inclusion and exclusion criteria. Univariate and multivariate logistic regression analysis were performed to identify risk factors for diagnosing csPCa in patients with negative mpMRI. Receiver Operating Characteristic (ROC) curves were plotted to compare the discriminatory ability of different Prostate-Specific Antigen Density (PSAD) cutoff values for csPCa. RESULTS: Among the 607 patients with negative mpMRI, 73 patients were diagnosed with csPCa. In univariate logistic regression analysis, age, PSA, f/tPSA, prostate volume, and PSAD were all associated with diagnosing csPCa in patients with negative mpMRI (P < 0.05), with PSAD being the most accurate predictor. In multivariate logistic regression analysis, f/tPSA, age, and PSAD were independent predictors of csPCa (P < 0.05). PSAD cutoff value of 0.20 ng/ml/ml has better discriminatory ability for predicting csPCa and is a significant risk factor for csPCa in multivariate analysis. CONCLUSION: Age, f/tPSA, and PSAD are independent predictors of diagnosing csPCa in patients with negative mpMRI. It is suggested that patients with negative mpMRI and PSAD less than 0.20 ng/ml/ml could avoid prostate biopsy, as a PSAD cutoff value of 0.20 ng/ml/ml has better diagnostic performance than the traditional cutoff value of 0.15 ng/ml/ml.

Assessing the diagnostic accuracy of the Xpert MTB/RIF assay in detecting epididymal tuberculosis.

To assess the diagnostic efficacy of Xpert MTB/RIF assay in detecting epididymal tuberculosis. METHODS: We analyzed 84 samples from patients with suspected epididymal TB or chronic epididymitis who received surgical treatment (epididymal resection or epididymis-testicular resection) at our hospital between July 1, 2016 and July 1, 2020. A composite reference standard (CRS) was used in this study. We determined the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) of the Xpert MTB/RIF assay for epididymal TB and compared its diagnostic accuracy with that of the Mycobacterium tuberculosis (MTB) culture test in detecting epididymal TB. RESULTS: Three patients were excluded because of incomplete data. Comparing with the composite reference standard, the sensitivity, specificity, PPV, NPV, and AUC of Xpert MTB/RIF assay for epididymal TB were 80.95% (69.09-89.75%); 94.44% (72.71-99.86%); 98.08% (89.74-99.95%); and 58.62% (38.94-76.48%), respectively, with an AUC of 0.88 (95% confidence interval: 0.79-0.94). The diagnostic sensitivity of the Xpert MTB/RIF assay was significantly higher than that of the MTB culture test for epididymal TB. CONCLUSIONS: We recommend the Xpert MTB/RIF assay as a diagnostic method for the detection of epididymal TB.

Comparison of the CapitalBio™Mycobacterium RT-PCR detection test and Xpert MTB/RIF assay for diagnosis of renal tuberculosis.

The purpose of this study is to compare the efficiency difference between CapitalBio™Mycobacterium real-time polymerase chain reaction (RT-PCR) detection test and Xpert MTB/RIF assay for the diagnosis of renal tuberculosis (TB). We analyzed 117 samples collected between July 1, 2018, and October 31, 2019, from patients with suspected renal TB to determine the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) of the CapitalBio™ Mycobacterium RT-PCR detection test for renal TB and to evaluate its diagnostic accuracy compared with Xpert MTB/RIF assay. Five cases were excluded from this study because of incomplete data. Taking clinical diagnosis as the gold standard, for the Xpert MTB/RIF assay, the sensitivity was 87.95% (78.96-94.07%), specificity 96.55% (82.24-99.91%), PPV 98.65% (92.70-99.97%), NPV 73.68% (56.90-86.60%), and AUC 0.92 (0.86-0.96). For the CapitalBio™Mycobacterium RT-PCR detection test, the overall sensitivity was 84.34% (74.71-91.39%), specificity 93.10% (77.23-99.15%), PPV 97.22% (90.32-99.66%), NPV 67.50% (50.87-81.43%), and AUC 0.89(0.81-0.94). The diagnostic efficiency of the CapitalBio™Mycobacterium RT-PCR detection test was similar to that of the Xpert MTB/RIF assay in patients with renal TB. Hence, the CapitalBio™Mycobacterium RT-PCR detection test presents a valuable alternative for the diagnosis of renal TB.

Knockdown of sterol O-acyltransferase 1 (SOAT1) suppresses SCD1-mediated lipogenesis and cancer procession in prostate cancer.

Prostate cancer (PCa) is one of the most fatal malignant tumors that occurs in the prostate epithelium, especially in older men, the mortality of which ranks sixth among all cancer-related deaths. It has been urgently needed to elucidate the pathogenesis of PCa and provide promising therapeutic targets for PCa treatment. The Sterol O-acyltransferase 1 (SOAT1), cholesterol metabolism enzyme, was widely expressed in various cancer tissues, resulting in cancer progression. SOAT1 has been demonstrated to be highly expressed in prostate cancer tissues, whereas the underlying mechanism has not been elucidated. Herein, we found the expression of SOAT1 was elevated in human PCa tissues, which demonstrated SOAT1 level was correlated with lymph node metastasis (p = 0.006), clinical stage (p = 0.032), grading (p = 0.036), and Gleason score (p = 0.030) of PCa patients. In addition, we revealed that SOAT1 promoted proliferation and liposynthesis of PCa cells by targeting Stearoyl-CoA Desaturase 1 (SCD1). Our data further confirmed that SCD1 overexpression reversed the proliferation and liposynthesis defects caused by SOAT1 depletion in PCa cells, however, SOAT1 depletion inhibited tumor growth of PCa cells in mice. We further found SOAT1 contributed to the progression of PCa via SREBF1 pathway. Taken together, our data revealed the mechanism underlying SOAT1 promoting PCa progression in vitro and in vivo.

Development of a prognostic model for muscle-invasive bladder cancer using glutamine metabolism.

BACKGROUND: Muscle-invasive bladder cancer (MIBC) is distinguished by its pronounced invasiveness and unfavorable prognosis. Immunotherapy and targeted therapy have emerged as key treatment options for various types of cancer. Altered metabolism is a defining characteristic of cancer cells, and there is mounting evidence suggesting the important role of glutamine metabolism (GM) in tumor metabolism. Nevertheless, the relationship between GM and clinical outcomes, immune microenvironment, and immunotherapy in MIBC remains unknown. METHODS: This study employed Mendelian randomization to explore the causal relationship between blood metabolites and bladder tumors. We systematically evaluated 373 glutamine metabolism-related genes and identified prognostic-related genes, leading to the construction of a glutamine-associated prognostic model. Further analysis confirmed the correlation between high and low-risk groups with the tumor microenvironment, immune cell infiltration, and tumor mutation burden. Subsequently, we assessed the relationship between the risk score and the sensitivity to various immunotherapies and anticancer drugs. RESULTS: We identified 14 blood metabolites at the molecular level that have a causal relationship with bladder tumors. At the gene level, the study discussed differentially expressed GM genes in MIBC. First, we established a risk model predicting overall survival (OS) based on GM genes, confirming its reliable predictive ability in MIBC patients and validated it in a GEO cohort. Additionally, a reliable column line chart was created. Secondly, two distinct molecular subtypes were identified, and the associations between different risk groups and tumor microenvironment and immune infiltration were observed. In addition, the predicted risk values correlated with responses to a broad range of pharmaceutical agents. CONCLUSION: In summary, we confirmed the causal relationship between blood metabolites and bladder tumors. Furthermore, a risk scoring model related to glutamine metabolism consisting of 9 genes was developed. This model could potentially serve as a useful tool for predicting prognosis and guiding the treatment of MIBC patients.