Metabolic analysis using HR-MAS in prostate tissue for prostate cancer diagnosis.

INTRODUCTION: In this study we used nuclear magnetic resonance spectroscopy in prostate tissue to provide new data on potential biomarkers of prostate cancer in patients eligible for prostate biopsy. MATERIAL AND METHODS: Core needle prostate tissue samples were obtained. After acquiring all the spectra using a Bruker Avance III DRX 600 spectrometer, tissue samples were subjected to routine histology to confirm presence or absence of prostate cancer. Univariate and multivariate analyses with metabolic and clinical variables were performed to predict the occurrence of prostate cancer. RESULTS: A total of 201 patients, were included in the study. Of all cores subjected to high-resolution magic angle spinning (HR-MAS) followed by standard histological study, 56 (27.8%) tested positive for carcinoma. According to HR-MAS probe analysis, metabolic pathways such as glycolysis, the Krebs cycle, and the metabolism of different amino acids were associated with presence of prostate cancer. Metabolites detected in tissue such as citrate or glycerol-3-phosphocholine, together with prostate volume and suspicious rectal examination, formed a predictive model for prostate cancer in tissue with an area under the curve of 0.87, a specificity of 94%, a positive predictive value of 80% and a negative predictive value of 84%. CONCLUSIONS: Metabolomics using HR-MAS analysis can uncover a specific metabolic fingerprint of prostate cancer in prostate tissue, using a tissue core obtained by transrectal biopsy. This specific fingerprint is based on levels of citrate, glycerol-3-phosphocholine, glycine, carnitine, and 0-phosphocholine. Several clinical variables, such as suspicious digital rectal examination and prostate volume, combined with these metabolites, form a predictive model to diagnose prostate cancer that has shown encouraging results.

Is there a relationship between varicocele and testosterone levels?

OBJECTIVES: The goal of this work was to study the relationship between presence of varicocele and testosterone serum levels in adulthood. METHODS: A comparative, cross-sectional study of 387 men who consulted for erectile dysfunction. Age, body mass index (BMI), diabetes (DM), and presence of varicocele were related to testosterone levels through uni- and multi-variate analysis. RESULTS: A total of 248 cases (70.8%) had no varicocele, 46 (13.1%) had grade I varicocele, 36 (10.3%) grade II, and 20 (5.7%) grade III. The mean total testosterone levels were 4.77 ng/mL in the non-varicocele group and 4.34 ng/mL in the varicocele group (p = .91), while free testosterone levels were 69.81 and 73.24 pg/mL (p = .18), respectively. In the multivariate analysis, BMI> = 30 was related to low total testosterone levels (OR: 2.94, p < .001) and low free testosterone (OR: 2.01, p = .01), while advanced age associated with low levels of free testosterone (OR: 1.04, p < .001). CONCLUSIONS: We were not able to establish a relationship between the presence of varicocele and decreased serum testosterone levels. Other factors already described, such as obesity and age, were related to low levels of total and free testosterone.

Prostate cancer detection rate at second and third biopsy. Predictive factors and risk groups for cancer diagnosis.

OBJECTIVES: The diagnosis of prostate cancer is obtained with the performance of a prostate biopsy. Repetition of biopsies is required in patients with negative biopsies when there is high suspicion for cancer. The objective of this study is to know the prostate cancer detection rate in second and third prostatic biopsies and to identify the clinical factors with predictive value for positivity. We also want to establish risk groups for cancer diagnosis after one or two previous negative biopsies. METHODS: Retrospective study of patients undergoing a second or third prostatic biopsy. We determined the rate of cancer diagnosis for both. We performed univariate and multivariate analysis (multiple logistic regression) to analyse any relationship between clinical variables (PSA, PSA density, PSA F/T ratio, PSA velocity, digital rectal examination, transrectal ultrasonography, prostate volume, time between biopsies, pathological result and number of cores obtained in the first biopsy) and positivity in the second and third biopsies. Logistic regression analysis was performed to know which factors are predictors for positivity in 2nd and 3 th biopsies. According to the probabilities obtained, different risk groups were established. RESULTS: 4.532 patients underwent prostate biopsy between 1999 and 2010. 663 patients were included for second biopsy and 191 for third biopsy. Detection rates for prostate cancer were 24,3% and 17.8% respectively. According to the multivariate analysis, the probability for positivity on second biopsy increases when first biopsy was sextant (p=0,049), patients were >65 years old (p=0,005) and PSA density was >0,15 (p=0,000). Four risk groups were established with a range of probability for prostate cancer between 7 and 37%. For third biopsy, predictive variables were: suspicious digital rectal examination (p=0,007), age >64 years (p=0,009), and PSA density >0,20 (p=0,001). Also risk groups were established with probabilities between 1,6 and 61%. CONCLUSIONS: Detection rate for prostatic cancer in second and third biopsy is high. According to risk factors we can establish different risk groups.