Effect of metabolic syndrome on testosterone levels in patients with metastatic prostate cancer: a real-world retrospective study.

Background: Metabolic syndrome (MetS) has been shown to have a negative impact on prostate cancer (PCa). However, there is limited research on the effects of MetS on testosterone levels in metastatic prostate cancer (mPCa). Objective: This study aims to investigate the influence of MetS, its individual components, and composite metabolic score on the prognosis of mPCa patients, as well as the impact on testosterone levels. Additionally, it seeks to identify MetS-related risk factors that could impact the time of decline in testosterone levels among mPCa patients. Methods: A total of 212 patients with mPCa were included in the study. The study included 94 patients in the Non-MetS group and 118 patients in the combined MetS group. To analyze the relationship between MetS and testosterone levels in patients with mPCa. Additionally, the study aimed to identify independent risk factors that affect the time for testosterone levels decline through multifactor logistic regression analysis. Survival curves were plotted by the Kaplan-Meier method. Results: Compared to the Non-MetS group, the combined MetS group had a higher proportion of patients with high tumor burden, T stage ≥ 4, and Gleason score ≥ 8 points (P < 0.05). Patients in the combined MetS group also had higher lowest testosterone values and it took longer for their testosterone to reach the lowest level (P < 0.05). The median progression-free survival (PFS) time for patients in the Non-MetS group was 21 months, while for those in the combined MetS group it was 18 months (P = 0.001). Additionally, the median overall survival (OS) time for the Non-MetS group was 62 months, whereas for the combined MetS group it was 38 months (P < 0.001). The median PFS for patients with a composite metabolic score of 0-2 points was 21 months, 3 points was 18 months, and 4-5 points was 15 months (P = 0.002). The median OS was 62 months, 42 months, and 29 months respectively (P < 0.001). MetS was found to be an independent risk factor for testosterone levels falling to the lowest value for more than 6 months. The risk of testosterone levels falling to the lowest value for more than 6 months in patients with MetS was 2.157 times higher than that of patients with Non-MetS group (P = 0.031). Patients with hyperglycemia had a significantly higher lowest values of testosterone (P = 0.015). Additionally, patients with a BMI ≥ 25 kg/m2 exhibited lower initial testosterone levels (P = 0.007). Furthermore, patients with TG ≥ 1.7 mmol/L experienced a longer time for testosterone levels to drop to the nadir (P = 0.023). The lowest value of testosterone in the group with a composite metabolic score of 3 or 4-5 was higher than that in the 0-2 group, and the time required for testosterone levels to decrease to the lowest value was also longer (P < 0.05). Conclusion: When monitoring testosterone levels in mPCa patients, it is important to consider the impact of MetS and its components, and make timely adjustments to individualized treatment strategies.

Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer.

Background: The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. Methods: This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. Results: MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and ß-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. Conclusion: The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Prostate cancer epidemiology and prognostic factors in the United States.

Objective: Using the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients. Methods: Patients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index. Results: There was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model. Conclusions: Although there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

Preliminary study on miRNA in prostate cancer.

OBJECTIVE: To screen for miRNAs differentially expressed in prostate cancer and prostate hyperplasia tissues and to validate their association with prostate cancer. METHODS: Patients diagnosed by pathology in the Department of Urology of the First Affiliated Hospital of Xinjiang Medical University from October 2021 to June 2022 were selected, and their general clinical information, blood samples, and prostate tissue samples were collected. miRNA microarray technology was performed to obtain differentially expressed miRNAs in prostate cancer and hyperplasia tissues, and miRNAs to be studied were screened by microarray results and review of relevant literature. The detection of miRNA expression in the patients' blood and prostate tissue samples was measured. The miRNA-222-mimics were transfected into PC3 cells, and cell biology experiments such as CCK8, scratch, Transwell, and flow cytometry were performed to detect the effects of overexpressed miRNA-222 on the growth and proliferation, invasive ability, apoptotic ability, and metastatic ability of prostate cancer cells. RESULTS: The results of the miRNA microarray showed that there were many differentially expressed miRNAs in prostate cancer and hyperplasia tissues, and four miRNAs, miRNA-144, miRNA-222, miRNA-1248, and miRNA-3651 were finally selected as the subjects by reviewing relevant literature. The results showed that the expression of miRNA-222 in prostate cancer tissues was lower than that in prostate hyperplasia tissues (P < 0.05). The expression of miRNA-222, miRNA-1248, and miRNA-3651 in blood samples of prostate cancer patients was lower than that in prostate hyperplasia patients (P < 0.05). The analysis results indicated that the f/t ratio and the relative expression of miRNA-222 and miRNA-1248 were independent influences of prostate cancer (P < 0.05), in which overexpression of miRNA-222 decreased the proliferative, invasive, and metastatic abilities of PC3 cells and enhanced the level of apoptosis of cancer cells. CONCLUSIONS: Although there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

Metabolic syndrome and metastatic prostate cancer correlation study, a real-world study in a prostate cancer clinical research center, Xinjiang, China.

Objective: The aim of this study was to investigate the relevance of metabolic syndrome (MetS) and metabolic scores to the occurrence, progression and prognosis of metastatic prostate cancer (mPCA), assessing the definition of the variables of metabolic syndrome, and the potential mechanisms of MetS and mPCA. Methods: Data were obtained from the database of prostate cancer follow-up at the Urology Centre of the First Affiliated Hospital of Xinjiang Medical University (N=1303). After screening by inclusion and exclusion criteria, clinical data of 190 patients diagnosed with mPCA by pathology and imaging from January 2010 to August 2021 were finally included, including 111 cases in the MetS group and 79 cases in the Non-MetS group. Results: The MetS group was higher than the Non-MetS group: T stage, Gleasson score, initial PSA, tumor load, PSA after 7 months of ADT (P<0.05),with a shorter time to progression to CRPC stage(P<0.05)[where the time to progression to CRPC was relatively shorter in the high metabolic score subgroup of the MetS group than in the low subgroup (P<0.05)].Median survival time was significantly shorter in the MetS group than in the Non-MetS group (P<0.05),and there was a correlation with metabolic score, with the higher metabolic score subgroup having a lower survival time than the lower metabolic score subgroup (P<0.05). Conclusion: Those with mPCA combined with MetS had lower PSA remission rates, more aggressive tumors, shorter time to progression to CRPC and shorter median survival times than those with mPCA without MetS.Tumour progression and metabolic score showed a positive correlation, predicting that MetS may promote the progression of mPCA, suggesting that MetS may be a risk factor affecting the prognosis of mPCA.