Urologic oncology · 2025
Urine metabolic analysis as a noninvasive method to diagnose prostate cancer.
СтатьяНазвание и аннотация: Перевод в очереди
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Panach-Navarrete Jorge, González-Marrachelli Vannina, Morales-Tatay José Manuel, García-Morata Francisco, Sales-Maicas María Ángeles, Monleón-Salvado Daniel, Martínez-Jabaloyas José María
Аннотация
OBJECTIVES: This study used urine NMR spectroscopy to define a potential metabolic profile indicating presence of prostate cancer, which could be a useful noninvasive method for diagnosis of this neoplasia.
METHODS: Urine samples were obtained from patients undergoing transrectal prostate biopsy after prostate massage. Patients were classified as diseased if cancerous tissue was obtained from biopsy histology, and all spectra were acquired using a Bruker Avance III DRX 600 spectrometer. Univariate and multivariate analyses were performed with metabolites and clinical variables with the objective of predicting tumor presence.
RESULTS: A total of 201 patients were included in the study, with a mean age of 67.20 ± 7.90 years. Prostate cancer was diagnosed in 107 (53.2%) cases, with a negative result for malignancy in the other 94 (46.8%).Metabolic analysis revealed metabolic pathways such as glycolysis, Krebs cycle, and the metabolism of different amino acids as involved in the presence of prostate cancer. The 28 metabolites detected in urine, together with prostate volume and ultrasound suspicion for tumor, formed a predictive model of prostate cancer in tissue, with an area under the curve (AUC) of 0.89, a sensitivity of 89%, a positive predictive value (PPV) of 82% and a negative predictive value (NPV) of 83%.
CONCLUSIONS: Metabolomics can be used to build a useful predictive model for diagnosing prostate cancer from the metabolic profile in urine, using a total of 28 metabolites. Combining metabolites, particularly molecules of glycerophospholipid metabolism, glycolysis, and amino acid metabolism, with clinical variables provides an effective strategy.
METHODS: Urine samples were obtained from patients undergoing transrectal prostate biopsy after prostate massage. Patients were classified as diseased if cancerous tissue was obtained from biopsy histology, and all spectra were acquired using a Bruker Avance III DRX 600 spectrometer. Univariate and multivariate analyses were performed with metabolites and clinical variables with the objective of predicting tumor presence.
RESULTS: A total of 201 patients were included in the study, with a mean age of 67.20 ± 7.90 years. Prostate cancer was diagnosed in 107 (53.2%) cases, with a negative result for malignancy in the other 94 (46.8%).Metabolic analysis revealed metabolic pathways such as glycolysis, Krebs cycle, and the metabolism of different amino acids as involved in the presence of prostate cancer. The 28 metabolites detected in urine, together with prostate volume and ultrasound suspicion for tumor, formed a predictive model of prostate cancer in tissue, with an area under the curve (AUC) of 0.89, a sensitivity of 89%, a positive predictive value (PPV) of 82% and a negative predictive value (NPV) of 83%.
CONCLUSIONS: Metabolomics can be used to build a useful predictive model for diagnosing prostate cancer from the metabolic profile in urine, using a total of 28 metabolites. Combining metabolites, particularly molecules of glycerophospholipid metabolism, glycolysis, and amino acid metabolism, with clinical variables provides an effective strategy.