Cooperative Effect of miR-141-3p and miR-145-5p in the Regulation of Targets in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Due to the poor prognosis for advanced renal cell carcinoma (RCC), there is an urgent need for new therapeutic targets and for prognostic markers to identify high risk tumors. MicroRNAs (miRNAs) are frequently dysregulated in tumors, play a crucial role during carcinogenesis and therefore might be promising new biomarkers. In previous studies, we identified miR-141-3p and miR-145-5p to be downregulated in clear cell RCC (ccRCC). Our objective was to investigate the functional association of these miRNAs, focusing on the cooperative regulation of new specific targets and their role in ccRCC progression. METHODS: The effect of miR-141-3p and miR-145-5p on cell migration was examined by overexpression in 786-O cells. New targets of both miRNAs were identified by miRWalk, validated in 786-O and ACHN cells and additionally characterized in ccRCC tissue on mRNA and protein level. RESULTS: In functional analysis, a tumor suppressive effect of miR-141-3p and miR-145-5p by decreasing migration and invasion of RCC cells could be shown. Furthermore, co-overexpression of the miRNAs seemed to result in an increased inhibition of cell migration. Both miRNAs were recognized as post-transcriptional regulators of the targets EAPP, HS6ST2, LOX, TGFB2 and VRK2. Additionally, they showed a cooperative effect again as demonstrated by a significantly increased inhibition of HS6ST2 and LOX expression after simultaneous overexpression of both miRNAs. In ccRCC tissue, LOX mRNA expression was strongly increased compared to normal tissue, allowing also to distinguish between non-metastatic and already metastasized primary tumors. Finally, in subsequent tissue microarray analysis LOX protein expression showed a prognostic relevance for the overall survival of ccRCC patients. CONCLUSION: These results illustrate a jointly strengthening effect of the dysregulated miR-141-3p and miR-145-5p in various tumor associated processes. Focusing on the cooperative effect of miRNAs provides new opportunities for the development of therapeutic strategies and offers novel prognostic and diagnostic capabilities.

Integration of tissue metabolomics, transcriptomics and immunohistochemistry reveals ERG- and gleason score-specific metabolomic alterations in prostate cancer.

Integrated analysis of metabolomics, transcriptomics and immunohistochemistry can contribute to a deeper understanding of biological processes altered in cancer and possibly enable improved diagnostic or prognostic tests. In this study, a set of 254 metabolites was determined by gas-chromatography/liquid chromatography-mass spectrometry in matched malignant and non-malignant prostatectomy samples of 106 prostate cancer (PCa) patients. Transcription analysis of matched samples was performed on a set of 15 PCa patients using Affymetrix U133 Plus 2.0 arrays. Expression of several proteins was immunohistochemically determined in 41 matched patient samples and the association with clinico-pathological parameters was analyzed by an integrated data analysis. These results further outline the highly deregulated metabolism of fatty acids, sphingolipids and polyamines in PCa. For the first time, the impact of the ERG translocation on the metabolome was demonstrated, highlighting an altered fatty acid oxidation in TMPRSS2-ERG translocation positive PCa specimens. Furthermore, alterations in cholesterol metabolism were found preferentially in high grade tumors, enabling the cells to create energy storage. With this integrated analysis we could not only confirm several findings from previous metabolomic studies, but also contradict others and finally expand our concepts of deregulated biological pathways in PCa.

Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score.

Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR). However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP) were used to develop an artificial neural network (ANN) to predict BCR and to compare it with a logistic regression (LR) model using clinical and pathologic parameters, prostate-specific antigen (PSA), margin status (R0/1), pathological stage (pT), and Gleason Score (GS). For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC) curve (AUC) for the ANN (0.754) and LR models (0.755) calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001), pT or PSA (AUC: 0.619; P always <0.0001) alone. The GS predicted the BCR better than PSA (P = 0.0001), but there was no difference between the ANN and LR models (P = 0.39). Our ANN and LR models predicted individual BCR risk from radical prostatectomy for up to 10 years postoperative. ANN and LR models equally and significantly improved the prediction of BCR compared with PSA and GS alone. When the GS and ANN output values are combined, a more accurate BCR prediction is possible, especially in high-risk patients with GS ≥7.

A new algorithm for integrated analysis of miRNA-mRNA interactions based on individual classification reveals insights into bladder cancer.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression. It has been proposed that miRNAs play an important role in cancer development and progression. Their ability to affect multiple gene pathways by targeting various mRNAs makes them an interesting class of regulators. METHODOLOGY/PRINCIPAL FINDINGS: We have developed an algorithm, Classification based Analysis of Paired Expression data of RNA (CAPE RNA), which is capable of identifying altered miRNA-mRNA regulation between tissues samples that assigns interaction states to each sample without preexisting stratification of groups. The distribution of the assigned interaction states compared to given experimental groups is used to assess the quality of a predicted interaction. We demonstrate the applicability of our approach by analyzing urothelial carcinoma and normal bladder tissue samples derived from 24 patients. Using our approach, normal and tumor tissue samples as well as different stages of tumor progression were successfully stratified. Also, our results suggest interesting differentially regulated miRNA-mRNA interactions associated with bladder tumor progression. CONCLUSIONS/SIGNIFICANCE: The need for tools that allow an integrative analysis of microRNA and mRNA expression data has been addressed. With this study, we provide an algorithm that emphasizes on the distribution of samples to rank differentially regulated miRNA-mRNA interactions. This is a new point of view compared to current approaches. From bootstrapping analysis, our ranking yields features that build strong classifiers. Further analysis reveals genes identified as differentially regulated by miRNAs to be enriched in cancer pathways, thus suggesting biologically interesting interactions.

Artificial neural networks and prostate cancer--tools for diagnosis and management.

Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

Comparative assessment of urinary prostate cancer antigen 3 and TMPRSS2:ERG gene fusion with the serum [-2]proprostate-specific antigen-based prostate health index for detection of prostate cancer.

BACKGROUND: We compared urinary prostate cancer antigen 3 (PCA3), transmembrane protease, serine 2 (TMPRSS2):v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene fusion (T2:ERG), and the serum [-2]proprostate-specific antigen ([-2]proPSA)-based prostate health index (Phi) for predicting biopsy outcome. METHODS: Serum samples and first-catch urine samples were collected after digital rectal examination (DRE) from consented outpatients with PSA 0.5-20 µg/L who were scheduled for prostate biopsy. The PCA3 score (PROGENSA PCA3, Hologic Gen-Probe) and T2:ERG score (Hologic Gen-Probe) were determined. Measurements of serum PSA, free PSA, and [-2]proPSA (Beckman Coulter) were performed, and the percentages of free PSA (%fPSA) and Phi ([-2]proPSA/fPSA × âˆšPSA) were determined. RESULTS: Of 246 enrolled men, prostate cancer (PCa) was diagnosed in 110 (45%) and there was no evidence of malignancy (NEM) in 136 (55%). A first set of biopsies was performed in 136 (55%) of all men, and 110 (45%) had ≥1 repeat biopsies. PCA3, Phi, and T2:ERG differed significantly between men with PCa and NEM, and these markers showed the largest areas under the ROC curve (AUCs) (0.74, 0.68, and 0.63, respectively). PCA3 had the largest AUC of all parameters, albeit not statistically different from Phi. Phi showed somewhat lower specificities than PCA3 at 90% sensitivity. Combination of both markers enhanced diagnostic power with modest AUC gains of 0.01-0.04. Although PCA3 had the highest AUC in the repeat-biopsy cohort, the highest AUC for Phi was observed in DRE-negative patients with PSA in the 2-10 µg/L range. CONCLUSIONS: PCA3 and Phi were superior to the other evaluated parameters but their combination gave only moderate enhancements in diagnostic accuracy for PCa at first or repeat prostate biopsy.

Selenoprotein P status correlates to cancer-specific mortality in renal cancer patients.

Selenium (Se) is an essential trace element for selenoprotein biosynthesis. Selenoproteins have been implicated in cancer risk and tumor development. Selenoprotein P (SePP) serves as the major Se transport protein in blood and as reliable biomarker of Se status in marginally supplied individuals. Among the different malignancies, renal cancer is characterized by a high mortality rate. In this study, we aimed to analyze the Se status in renal cell cancer (RCC) patients and whether it correlates to cancer-specific mortality. To this end, serum samples of RCC patients (n = 41) and controls (n = 21) were retrospectively analyzed. Serum Se and SePP concentrations were measured by X-ray fluorescence and an immunoassay, respectively. Clinical and survival data were compared to serum Se and SePP concentrations as markers of Se status by receiver operating characteristic (ROC) curve and Kaplan-Meier and Cox regression analyses. In our patients, higher tumor grade and tumor stage at diagnosis correlated to lower SePP and Se concentrations. Kaplan-Meier analyses indicated that low Se status at diagnosis (SePP<2.4 mg/l, bottom tertile of patient group) was associated with a poor 5-year survival rate of 20% only. We conclude that SePP and Se concentrations are of prognostic value in RCC and may serve as additional diagnostic biomarkers identifying a Se deficit in kidney cancer patients potentially affecting therapy regimen. As poor Se status was indicative of high mortality odds, we speculate that an adjuvant Se supplementation of Se-deficient RCC patients might be beneficial in order to stabilize their selenoprotein expression hopefully prolonging their survival. However, this assumption needs to be rigorously tested in prospective clinical trials.

Avoiding pitfalls in applying prediction models, as illustrated by the example of prostate cancer diagnosis.

BACKGROUND: The use of different mathematical models to support medical decisions is accompanied by increasing uncertainties when they are applied in practice. Using prostate cancer (PCa) risk models as an example, we recommend requirements for model development and draw attention to possible pitfalls so as to avoid the uncritical use of these models. CONTENT: We conducted MEDLINE searches for applications of multivariate models supporting the prediction of PCa risk. We critically reviewed the methodological aspects of model development and the biological and analytical variability of the parameters used for model development. In addition, we reviewed the role of prostate biopsy as the gold standard for confirming diagnoses. In addition, we analyzed different methods of model evaluation with respect to their application to different populations. When using models in clinical practice, one must validate the results with a population from the application field. Typical model characteristics (such as discrimination performance and calibration) and methods for assessing the risk of a decision should be used when evaluating a model's output. The choice of a model should be based on these results and on the practicality of its use. SUMMARY: To avoid possible errors in applying prediction models (the risk of PCa, for example) requires examining the possible pitfalls of the underlying mathematical models in the context of the individual case. The main tools for this purpose are discrimination, calibration, and decision curve analysis.

Effect of mutations of the human serpin protein corticosteroid-binding globulin on cortisol-binding, thermal and protease sensitivity.

Corticosteroid-binding globulin (CBG, transcortin) belongs to the serpin family of serine protease inhibitors (SERPINA6) and is mainly secreted by the liver. The negative acute phase protein CBG regulates free cortisol levels in the blood and distributes cortisol to its target tissues. So far no CBG serpin partner protease has been identified. However, its cleavage by human neutrophil elastase destroys ligand binding capacity and supposedly liberates cortisol at sites of inflammation. Here we report on the recombinant expression and secretion of human wild-type CBG and several novel mutants by human 293-EBNA cells. Functional characterization of wild-type and mutant CBG revealed distinct differences in ligand binding sensitivity to heat or elastase. Certain mutants are almost devoid of cortisol binding activity (Q232R and CBG Lyon), some display higher sensitivity for heat inactivation (G335V, Q232R and CBG Lyon) or for elastase cleavage (G335V). CBG mutant T342A is more resistant to elastase cleavage. Our data support the validity of the serpin structural concept. The expression system used provides functionally active human recombinant transcortin for further functional characterization of wild-type and human CBG mutant variants, which have been associated with altered serum free cortisol levels or pathophysiological constellations such as increased body weight, fatigue or hypotension.

Internal validation of an artificial neural network for prostate biopsy outcome.

OBJECTIVES: To carry out an internal validation of the retrospectively trained artificial neural network (ANN) 'ProstataClass'. METHODS: A prospectively collected database of 393 patients undergoing 8-12 core prostate biopsy was analyzed. Data of these patients were applied to the online available ANN 'ProstataClass' using the Elecsys total prostate-specific antigen (tPSA) and free PSA (fPSA) assays. Beside the internal validation of the ANN 'ProstataClass' an additional ANN (named as ANN internal validation: ANNiv) only using the 393 prospective patient data was evaluated. The new ANN model was constructed with the MATLAB Neural Network Toolbox. Diagnostic accuracy was evaluated by receiver operator characteristic (ROC) curves comparing the areas under the ROC curves (AUC) and specificities at 90% and 95% sensitivity. RESULTS: Within a tPSA range of 1.0-22.8 ng/mL, 229 men (58.3%) had prostate cancer (PCa). tPSA, %fPSA and the number of positive digital rectal examinations (DRE) differed significantly from the cohort of patients of the ANN 'ProstataClass', whereas age and prostate volume were comparable. AUCs for tPSA, %fPSA and the ANN 'ProstataClass' were 0.527, 0.726 and 0.747 (P = 0.085 between %fPSA and ANN). The AUC of the ANNiv (0.754) was significantly better compared with %fPSA (P = 0.021), whereas the AUC of two ANN models built on external cohorts (0.726 and 0.729) showed no differences to %fPSA and the other ANN models. CONCLUSIONS: Significant differences of DRE status and %fPSA medians decrease the power of the 'ProstataClass' ANN in the internal validation cohort. The effect of retrospective data evaluation the 'ProstataClass' cohort and prospective fPSA measurement may be responsible for %fPSA differences. All ANN models built with different PSA and fPSA assays performed equally if applied to the two cohorts.

Benign prostatic hyperplasia-associated free prostate-specific antigen improves detection of prostate cancer in an artificial neural network.

OBJECTIVES: To show discriminative power between patients with prostate cancer (PCa) and those with "no evidence of malignancy" using "benign" prostate-specific antigen (bPSA) and the new automated Access benign prostatic hyperplasia-associated (BPHA) research assay within a percent free PSA (%fPSA)-based artificial neural network (ANN) model. METHODS: The sera from 287 patients with PCa and 254 patients with no evidence of malignancy were measured using the BPHA, total PSA (tPSA), and fPSA assays with Access immunoassay technology, with a 0-10 ng/mL tPSA range. Two ANN models with Bayesian regularization and leave-one-out validation using the 4 input parameters of tPSA, %fPSA, age, and prostate volume and 1 containing BPHA/tPSA were constructed and compared by receiver operating characteristic curve analysis. RESULTS: The BPHA/tPSA-based ANN reached the significant greatest area under the receiver operating characteristic curve (AUC 0.81; P = .0004 and P = .0024) and best specificity (53.9% and 44.5%) compared with the ANN without BPHA/tPSA (AUC 0.77; specificity 50% and 40.6%) and %fPSA (AUC 0.77; specificity 40.9% and 27.2%) at 90% and 95% sensitivity, respectively. The AUCs for tPSA (0.58), BPHA (0.55), BPHA/fPSA (0.51), prostate volume (0.69), and BPHA/tPSA (0.69) were significantly lower. CONCLUSIONS: Although BPHA as single marker or ratio to tPSA did not improve the diagnostic performance of %fPSA or tPSA, the incorporation of BPHA/tPSA into an ANN model increased the specificity compared with %fPSA by 13% and 17% at 90% and 95% sensitivity, respectively. Thus, the automated BPHA research assay might improve PCa detection when incorporating this new marker into an ANN.

Identification of stanniocalcin 2 as prognostic marker in renal cell carcinoma.

BACKGROUND: For an individualized therapy in renal cell carcinoma (RCC), there is a clear need for novel prognostic biomarkers to ensure adequate risk stratification and help with the choice of therapy options. OBJECTIVE: To identify new secreted biomarkers for diagnosis and estimation of prognosis in RCC. DESIGN, SETTING, AND PARTICIPANTS: A meta-analysis of published microarray data was performed. Stanniocalcin 2 (STC2), a glycoprotein hormone that is involved in regulatory effects on calcium and phosphate transport in the kidney, was found overexpressed in tumors and hence analyzed in detail. Kidney tissue samples derived from 108 patients with RCC undergoing radical nephrectomy between July 2003 and January 2006 were used to validate and estimate the potential of STC2 as a biomarker for RCC. MEASUREMENTS: STC2, found upregulated in clear cell RCC, was analyzed in detail using real-time reverse transcription-polymerase chain reaction (RT-PCR), western blotting, and immunohistochemistry. Furthermore, STC2 protein expression determined on a tissue microarray was correlated to clinical pathologic parameters, including patient survival. RESULTS AND LIMITATIONS: STC2 was upregulated at the mRNA and protein levels in RCC. In normal renal tissue, STC2 expression was limited to distal tubuli and glomeruli, whereas in tumor a strong cytoplasmic and also membranous staining was detected. STC2 expression was found in clear cell, chromophobe, and papillary RCC. Strong cytoplasmic STC2 expression was significantly associated with shorter patient survival in Kaplan-Meier analyses. In the group of patients without metastases, cytoplasmic STC2 expression was also found as a significant independent risk factor in multivariate analysis. A limitation of the study is the small number of patients. CONCLUSIONS: Increased cytoplasmic STC2 expression correlated with conventional indicators of aggressiveness of RCC and shorter overall patient survival times. STC2 could become an adjunct tissue biomarker that may be useful in the postoperative risk stratification of RCC patients.

Elevated plasma osteopontin as marker for distant metastases and poor survival in patients with renal cell carcinoma.

PURPOSE: To evaluate diagnostic and prognostic significance of plasma osteopontin (OPN) in patients with renal cell carcinoma (RCC). METHODS: The retrospective study included 80 patients with RCC (pN0M0, n = 32; pN1M0, n = 11; M1, and n = 37), and 52 healthy controls (27 females and 25 males). OPN, the bone marker bone-specific alkaline phosphatase (bALP) and carboxyterminal telopetide of type-I collagen (ICTP), and the enzymes alanine aminotransferase (ALAT), and gamma-glutamyltransferase (GGT) were evaluated together with Memorial Sloan-Kettering Cancer Center (MSKCC) laboratory parameters. Data were analyzed by receiver-operating characteristics (ROC), survival analysis, and Cox proportional hazards regression model. RESULTS: OPN and ICTP levels in RCC patients with distant metastases were significantly elevated (medians 115 and 4.7 microg/l, P < 0.001) compared to those without metastases (31.1 and 2.5 microg/l) and controls (28.9 and 2.1 microg/l) but did not differ between patients with bone or non-bone metastases. Both bALP and ALAT were not different between all study groups, while GGT was only increased in patients with non-bone metastases. In ROC analysis, OPN showed the best discrimination between patients with and without metastases (area under the curve: 0.888). High OPN values were associated with poor survival (Kaplan-Meier analysis, log-rank test, P = 0.002). Multivariate Cox regression with forward and backward stepwise elimination confirmed plasma OPN as independent predictive survival factor in RCC patients. CONCLUSIONS: Our results show that high plasma OPN levels are associated with distant metastases and poor survival in RCC patients. The use of OPN as potential marker to monitor new treatment strategies in patients with advanced RCC should be evaluated in prospective studies.

PSA and new biomarkers within multivariate models to improve early detection of prostate cancer.

This review gives an overview of the use of prostate-specific antigen (PSA) and percent free-PSA (%fPSA)-based artificial neural networks (ANNs) and logistic regression models (LR) to reduce unnecessary prostate biopsies. There is a clear advantage in including clinical data such as age, digital rectal examination and transrectal ultrasound (TRUS) variables like prostate volume and PSA density as additional factors to tPSA and %fPSA within ANNs and LR models. There is also positive impact of tPSA and fPSA assays on the outcome of ANNs. New markers provide additional value within ANNs but to prove their clinical usefulness further testing is necessary.