Prostate cancer (PCa) diagnosis with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment, highlighting the need for novel biomarkers. The aim of this review is to provide a summary of available metabolomics PCa biomarkers, particularly for clinically significant disease. A systematic search was conducted on PubMed for publications from July 2008 to July 2018 in accordance with PRISMA guidelines to report biomarkers with respect to their application in PCa diagnosis, progression, aggressiveness, recurrence, and treatment response. The vast majority of studies report biomarkers with the ability to distinguish malignant from benign prostate tissue with a few studies investigating biomarkers associated with disease progression, treatment response or tumour recurrence. In general, these studies report high dimensional datasets and the number of analysed metabolites often significantly exceeded the number of available samples. Hence, observed multivariate differences between case and control samples in the datasets might potentially also be associated with pre-analytical, technical, statistical and confounding factors. Giving the technical and methodological hurdles, there are nevertheless a number of metabolites and pathways repeatedly reported across various technical approaches, cohorts and sample types that appear to play a predominant role in PCa tumour biology, progression and recurrence.

Introduction Allograft rejection is still an important complication after kidney transplantation. Currently, monitoring of these patients mostly relies on the measurement of serum creatinine and clinical evaluation. The gold standard for diagnosing allograft rejection, i.e. performing a renal biopsy is invasive and expensive. So far no adequate biomarkers are available for routine use. Objectives We aimed to develop a urine metabolite constellation that is characteristic for acute renal allograft rejection. Methods NMR-Spectroscopy was applied to a training cohort of transplant recipients with and without acute rejection. Results We obtained a metabolite constellation of four metabolites that shows promising performance to detect renal allograft rejection in the cohorts used (AUC of 0.72 and 0.74, respectively). Conclusion A metabolite constellation was defined with the potential for further development of an in-vitro diagnostic test that can support physicians in their clinical assessment of a kidney transplant patient.

Background: This exploratory study aimed to evaluate lipidomic and metabolomic profiles in patients with early and advanced HCCs and to investigate whether certain metabolic parameters may predict the overall survival in these patients. Methods: A total of 60 patients from the prospective, randomized-controlled, multicenter phase II SORAMIC trial were included in this substudy; among them were 30 patients with an early HCC who underwent radiofrequency ablation combined with sorafenib or a placebo and 30 patients with an advanced HCC who were treated with a selective internal radiation therapy (SIRT) plus sorafenib vs. sorafenib alone. The blood serum of these patients was analyzed using a standardized nuclear magnetic resonance (NMR) platform. All tested metabolites were correlated with the overall survival. Results: The overall survival (OS) was significantly higher in patients with an early HCC (median OS: 34.0 months) compared with patients with an advanced HCC (median OS: 12.0 months) (p < 0.0001). Patients with high serum concentrations of myo-inositol (MI) had a higher overall survival compared with patients with low concentrations (21.6 vs. 13.8 months) with a Pearson correlation coefficient of 0.331 (p = 0.011). Patients with high serum concentrations of dimethylamine had a higher overall survival compared with patients with low concentrations (25.1 vs. 19.7 months) with a Pearson correlation coefficient of 0.279 (p = 0.034). High concentrations of total cholesterol, LDL-cholesterol and LDL particles (LDL-P) were associated with a decreased overall survival. Conclusions: NMR-based lipidomic and metabolomic profiling has the potential to identify individual metabolite biomarkers that predict the outcome of patients with an HCC exposed to non-invasive therapeutic management.

Evaluation of renal dysfunction includes estimation of glomerular filtration rate (eGFR) as the initial step and subsequent laboratory testing. We hypothesized that combined analysis of serum creatinine, myo-inositol, dimethyl sulfone, and valine would allow both assessment of renal dysfunction and precise GFR estimation. Bio-banked sera were analyzed using nuclear magnetic resonance spectroscopy (NMR). The metabolites were combined into a metabolite constellation (GFR_NMR) using n = 95 training samples and tested in n = 189 independent samples. Tracer-measured GFR (mGFR) served as a reference. GFR_NMR was compared to eGFR based on serum creatinine (eGFR_Crea and eGFR_EKFC), cystatin C (eGFR_Cys-C), and their combination (eGFR_Crea-Cys-C) when available. The renal biomarkers provided insights into individual renal and metabolic dysfunction profiles in selected mGFR-matched patients with otherwise homogenous clinical etiology. GFR_NMR correlated better with mGFR (Pearson correlation coefficient r = 0.84 vs. 0.79 and 0.80). Overall percentages of eGFR values within 30% of mGFR for GFR_NMR matched or exceeded those for eGFR_Crea and eGFR_EKFC (81% vs. 64% and 74%), eGFR_Cys-C (81% vs. 72%), and eGFR_Crea-Cys-C (81% vs. 81%). GFR_NMR was independent of patients’ age and sex. The metabolite-based NMR approach combined metabolic characterization of renal dysfunction with precise GFR estimation in pediatric and adult patients in a single analytical step.

Assessment of renal function relies on the estimation of the glomerular filtration rate (eGFR). Existing eGFR equations, usually based on serum levels of creatinine and/or cystatin C, are not uniformly accurate across patient populations. In the present study, we expanded a recent proof-of-concept approach to optimize an eGFR equation targeting the adult population with and without chronic kidney disease (CKD), based on a nuclear magnetic resonance spectroscopy (NMR) derived ‘metabolite constellation’ (GFR_NMR). A total of 1855 serum samples were partitioned into development, internal validation and external validation datasets. The new GFR_NMR equation used serum myo-inositol, valine, creatinine and cystatin C plus age and sex. GFR_NMR had a lower bias to tracer measured GFR (mGFR) than existing eGFR equations, with a median bias (95% confidence interval [CI]) of 0.0 (−1.0; 1.0) mL/min/1.73 m² for GFR_NMR vs. −6.0 (−7.0; −5.0) mL/min/1.73 m² for the Chronic Kidney Disease Epidemiology Collaboration equation that combines creatinine and cystatin C (CKD-EPI₂₀₁₂) (p < 0.0001). Accuracy (95% CI) within 15% of mGFR (1-P15) was 38.8% (34.3; 42.5) for GFR_NMR vs. 47.3% (43.2; 51.5) for CKD-EPI₂₀₁₂ (p < 0.010). Thus, GFR_NMR holds promise as an alternative way to assess eGFR with superior accuracy in adult patients with and without CKD.

Previously, we described prostate cancer (PCa) detection (83% sensitivity; 67% specificity) in seminal plasma by CE-MS/MS. Moreover, advanced disease was distinguished from organ-confined tumors with 80% sensitivity and 82% specificity. The discovered biomarkers were naturally occurring fragments of larger seminal proteins, predominantly semenogelin 1 and 2, representing endpoints of the ejaculate liquefaction. Here we identified proteases putatively involved in PCa specific protein cleavage, and examined gene expression and tissue protein levels, jointly with cell localization in normal prostate (nP), benign prostate hyperplasia (BPH), seminal vesicles and PCa using qPCR, Western blotting and confocal laser scanning microscopy. We found differential gene expression of chymase (CMA1), matrix metalloproteinases (MMP3, MMP7), and upregulation of MMP14 and tissue inhibitors (TIMP1 and TIMP2) in BPH. In contrast tissue protein levels of MMP14 were downregulated in PCa. MMP3/TIMP1 and MMP7/TIMP1 ratios were decreased in BPH. In seminal vesicles, we found low-level expression of most proteases and, interestingly, we also detected TIMP1 and low levels of TIMP2. We conclude that MMP3 and MMP7 activity is different in PCa compared to BPH due to fine regulation by their inhibitor TIMP1. Our findings support the concept of seminal plasma biomarkers as non-invasive tool for PCa detection and risk stratification.