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.

Association between metabolic obesity phenotypes and the risk of developing prostate cancer: a propensity score matching study based on Xinjiang.

Background: Obesity-induced metabolic dysfunction increases the risk of developing tumors, however, the relationship between metabolic obesity phenotypes and prostate cancer (PCa) remains unclear. Methods: The term metabolic obesity phenotypes was introduced based on metabolic status and BMI categories. Participants were categorized into four groups: metabolically healthy nonobesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy nonobesity (MUNO), and metabolically unhealthy obesity (MUO). Propensity score matching was conducted based on age, ethnicity, marriage, etc. Univariate and multivariate conditional logistic regression analyses were used to assess the relationship between metabolic obesity phenotypes, metabolic risk factors, and PCa. Sensitivity analysis was performed to verify the robustness of the results. Results: After propensity score matching among 564 PCa patients and 1418 healthy individuals, 209 were selected for each of the case and control groups. There were no statistically significant differences in the basic characteristics between the two groups. Univariate and multivariate conditional logistic regression suggested that the risk of developing PCa in both MHO and MUO individuals was higher than in MHNO individuals. Specifically, the risk of developing PCa in MHO individuals was 2.166 times higher than in MHNO individuals (OR=2.166, 95%CI: 1.133-4.139), and the risk in MUO individuals was is 2.398 times higher than in MHNO individuals(OR=2.398, 95%CI:1.271-4.523). Individuals with hyperglycemia and elevated triglycerides also had a higher risk of developing PCa (hyperglycemia:OR=1.488, 95%CI: 1.001-2.210; elevated triglycerides: OR=2.292, 95%CI: 1.419-3.702). Those with more than or equal to three metabolic risk factors had an increased risk of PCa (OR=1.990, 95%CI: 1.166-3.396). Sensitivity analysis indicated an increased risk of PCa in MUO individuals compared to MHNO individuals. Conclusion: In this retrospective study, individuals with MHO and MUO had a higher risk of developing PCa.

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.

Association between the metabolic score for insulin resistance and prostate cancer: a cross-sectional study in Xinjiang.

Background: Insulin resistance is associated with the development and progression of various cancers. However, the epidemiological evidence for the association between insulin resistance and prostate cancer is still limited. Objectives: To investigate the associations between insulin resistance and prostate cancer prevalence. Methods: A total of 451 patients who were pathologically diagnosed with prostate cancer in the First Affiliated Hospital of Xinjiang Medical University were selected as the case population; 1,863 participants who conducted physical examinations during the same period were selected as the control population. The metabolic score for insulin resistance (METS-IR) was calculated as a substitute indicator for evaluating insulin resistance. The Chi-square test and Mann-Whitney U test were performed to compare the basic information of the case population and control population. Univariate and multivariate logistic regression analyses to define factors that may influence prostate cancer prevalence. The generalized additive model (GAM) was applied to fit the relationship between METS-IR and prostate cancer. Interaction tests based on generalized additive model (GAM) and contour plots were also carried out to analyze the interaction effect of each factor with METS-IR on prostate cancer. Results: METS-IR as both a continuous and categorical variable suggested that METS-IR was negatively associated with prostate cancer prevalence. Smoothed curves fitted by generalized additive model (GAM) displayed a nonlinear correlation between METS-IR and prostate cancer prevalence (P < 0.001), and presented that METS-IR was negatively associated with the odds ratio (OR) of prostate cancer. The interaction based on the generalized additive model (GAM) revealed that METS-IR interacted with low-density lipoprotein cholesterol (LDL-c) to influence the prostate cancer prevalence (P = 0.004). Contour plots showed that the highest prevalence probability of prostate cancer was achieved when METS-IR was minimal and low-density lipoprotein cholesterol (LDL-c) or total cholesterol (TC) was maximal. Conclusions: METS-IR is nonlinearly and negatively associated with the prevalence of prostate cancer. The interaction between METS-IR and low-density lipoprotein cholesterol (LDL-c) has an impact on the prevalence of prostate cancer. The study suggests that the causal relationship between insulin resistance and prostate cancer still needs more research to confirm.

Triglyceride-glucose index is a predictor of the risk of prostate cancer: a retrospective study based on a transprostatic aspiration biopsy population.

Background: Current research suggests that prostate cancer (PCa), one of the most common cancers in men, may be linked to insulin resistance (IR).Triglyceride-glucose index (TyG index) was made for a marker of insulin resistance. We investigated the relationship between the TyG index and the risk of PCa. Objective: To assess the correlation and dose-response relationship between TyG index and prostate cancer. Method: Retrospectively, 316 patients who required prostate biopsy puncture in the First Affiliated Hospital of Xinjiang Medical University from March 2017 to July 2021 were collected, and the relationship between factors such as the TyG index and prostate cancer was analyzed by Logistic regression model combined with a restricted cubic spline. Results: (1) The differences in age, initial PSA and TyG index between the two groups were statistically significant; (2) Logistic regression results showed that the risk of prostate cancer in the highest quartile of the TyG index (Q4) was 3.387 times higher than that in the lowest quartile (Q1) (OR=3.387,95% CI [1.511,7.593], P=0.003); (3) The interaction results showed a significant interaction between the TyG index Q4 group and age with the risk of developing prostate cancer (P for interaction<0.001). (4) The results of the restricted cubic spline showed a linear dose-response relationship between the TyG index and the risk of prostate cancer; (5) The Receiver operating characteristic (ROC) curve results showed that the area under the curve (AUC) of the TyG index combined with initial PSA and age was 0.840, with a sensitivity and specificity of 62.5% and 93.3%, respectively. Conclusion: TyG index and age are risk factors for prostate cancer, and the interaction between the TyG index and different risk factors may increase the risk of prostate cancer. TyG index has some predictive value for the risk of prostate cancer, and the risk of prostate cancer can be reduced by controlling the levels of blood lipids and blood glucose.