Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.