BACKGROUND: RNA integrity is the essential factor that determines the accuracy of mRNA transcript measurements obtained with quantitative real-time reversetranscription PCR (RT-qPCR), but evidence is clearly lacking on whether this conclusion also applies to microRNAs (miRNAs). We evaluated this issue by comparative analysis of the dependence of miRNA and mRNA measurements on RNA integrity in renal and prostate samples, under both model and clinical conditions.

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Ga-labeled prostate-specific membrane antigen (Ga-PSMA) PET/CT has a proven role in staging and restaging of prostate cancer (PCA). The aims of this study were to evaluate the association of intraprostatic Ga-PSMA PET/CT findings and PSMA expression in immunohistochemical staining and generate a cutoff value for differentiation between normal prostate (PN) and PCA. The data of 31 patients (mean age, 67.2 y) who underwent prostatectomy and preoperative PET were retrospectively analyzed. On PET, focally increased uptake in the prostate was suggestive of tumor. A region of interest was placed on the suggestive area to generate an SUV; a similar region of interest was placed on adjacent visually PN. Both PCA and PN were stained with monoclonal anti-PSMA antibody (clone 3E6, 1:100, M3620). All intraprostatic PCA lesions on PET could be confirmed histopathologically. In PN sections ( = 31), median staining intensity was mild, median percentage of stained cells was 20% ± 14.24%, and median immunoreactive score (IRS) was 1. In PCA sections ( = 31), median IRS was 3, median staining intensity was strong, and median percentage of stained cells was 80% ± 16.46%. The mean SUV (±SD) of PCA (14.06 ± 15.56) was significantly higher than that of PN (2.43 ± 0.63; < 0.001). Receiver-operating-characteristic curve analyses of the SUV of PCA, validated by immunohistochemical staining in 62 tissue samples, showed the best cutoff to be 3.15 (sensitivity, 97%; specificity, 90%; area under curve, 0.987). Applied to multifocal PCA, it resulted in sensitivity and specificity of 87% and 97% respectively. The mean SUV of PCA and PN for an IRS of less than 2 ( = 26; 2.52 ± 0.64) was significantly lower than the mean SUV for an IRS of 2 or more ( = 36; 12.38 ± 15.02; < 0.001). The mean SUV was significantly lower in PCA samples with fewer than 50% stained cells ( = 30; 2.81 ± 2.35) than in samples with 50% or more ( = 32; 13.34 ± 15.55; < 0.001). There was no correlation between the SUV of PCA and Gleason score ( = 0.54). This study showed that SUV on Ga-PSMA PET/CT correlates significantly with PSMA expression in primary PCA, enabling the detection of PCA with a high sensitivity and specificity.

Real-time MRI/US fusion-guided TB combined with RB improved PCa detection in patients with primary and repeat biopsies. The CDR was strongly correlated with a rising PI-RADS score, values of 4 and 5 increasing the detection of clinically significant tumors and leading to a higher histological stage after RP.

Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.