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Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. For its detection, serum prostate-specific antigen (PSA) screening is commonly used, despite its lack of specificity, high false positive rate, and inability to discriminate indolent from aggressive PCa. Following increases in serum PSA levels, clinicians often conduct prostate biopsies with or without advanced imaging. Nuclear magnetic resonance (NMR)-based metabolomics has proven to be promising for advancing early-detection and elucidation of disease progression, through the discovery and characterization of novel biomarkers. This retrospective study of urine-NMR samples, from prostate biopsy patients with and without PCa, identified several metabolites involved in energy metabolism, amino acid metabolism, and the hippuric acid pathway. Of note, lactate and hippurate-key metabolites involved in cellular proliferation and microbiome effects, respectively-were significantly altered, unveiling widespread metabolomic modifications associated with PCa development. These findings support urine metabolomics profiling as a promising strategy to identify new clinical biomarkers for PCa detection and diagnosis., (© 2023 John Wiley & Sons Ltd.)

Currently, many prostate cancer patients, detected through the prostate specific antigen test, harbor organ-confined indolent disease that cannot be differentiated from aggressive cancer according to clinically and pathologically known measures. Spermine has been considered as an endogenous inhibitor for prostate-confined cancer growth and its expression has shown correlation with prostate cancer growth rates. If established clinically, measurements of spermine bio-synthesis rates in prostates may predict prostate cancer growth and patient outcomes. Using rat models, we tested the feasibility of quantifying spermine bio-synthesis rates with 13 C NMR. Male Copenhagen rats (10 weeks, n = 6) were injected with uniformly 13 C-labeled L-ornithine HCl, and were sacrificed in pairs at 10, 30, and 60 min after injection. Another two rats were injected with saline and sacrificed at 30 min as controls. Prostates were harvested and extracted with perchloric acid and the neutralized solutions were examined by 13 C NMR at 600 MHz. 13 C NMR revealed measurable ornithine, as well as putrescine-spermidine-spermine syntheses in rat prostates, allowing polyamine bio-synthetic and ornithine bio-catabolic rates to be calculated. Our study demonstrated the feasibility of 13 C NMR for measuring bio-synthesis rates of ornithine to spermine enzymatic reactions in rat prostates. The current study established a foundation upon which future investigations of protocols that differentiate prostate cancer growth rates according to the measure of ornithine to spermine bio-synthetic rates may be developed., (© 2023 John Wiley & Sons Ltd.)

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication., (© 2019 John Wiley & Sons, Ltd.)

Background: Prostate cancer (PCa), the most common cancer and second leading cause of cancer death in American men, presents the clinical challenge of distinguishing between indolent and aggressive tumors for proper treatment. PCa presents significant alterations in metabolic pathways that can potentially be measured using techniques like mass spectrometry (MS) or MS imaging (MSI) and used to characterize PCa aggressiveness. MS quantifies metabolomic, proteomic, and lipidomic profiles of biological systems that can be further visualized for their spatial distributions through MSI., Methods: PubMed was queried for all publications relating to MS and MSI in human PCa from April 2007 to April 2017. With the goal of reviewing the utility of MSI in diagnosis and prognostication of human PCa, MSI articles that reported investigations of PCa-specific metabolites or metabolites indicating PCa aggressiveness were selected for inclusion. Articles were included that covered MS and MSI principles, limitations, and applications in PCa., Results: We identified nine key studies on MSI in intact human prostate tissue specimens that determined metabolites which could either differentiate between benign and malignant prostate tissue or indicate PCa aggressiveness. These MSI-detected biomarkers show promise in reliably identifying PCa and determining disease aggressiveness., Conclusions: MSI represents an innovative technique with the ability to interrogate cancer biomarkers in relation to tissue pathologies and investigate tumor aggressiveness. We propose MSI as a powerful adjuvant histopathology imaging tool for prostate tissue evaluations, where clinical translation of this ex vivo technique could make possible the use of MSI for personalized medicine in diagnosis and prognosis of PCa. Moreover, the knowledge provided from this technique can majorly contribute to the understanding of molecular pathogenesis of PCa and other malignant diseases.

Statistical data from prostate cancer (PCa) clinics indicates that a large patient population discovered by annual prostate specific antigen (PSA) screening may have a latent form of the disease. However, current medical tests cannot differentiate slow from fast growing PCa, resulting in many unnecessary radical treatments and morbidities. It is thus necessary to find new screening tests that enable us to differentiate between fast- and slow-growing tumors. Inspired by the reported functions of spermine in carcinogenesis, we analyzed spermine and mRNA expression levels of rate-limiting enzymes in the spermine metabolic pathway for nine cases of PCa with accurately defined PSA velocity (Vpsa). Using MR spectroscopy, histopathology, laser-capture microdissection and real-time PCR techniques, we analyzed relationships between changes in spermine levels, mRNA expression levels of spermine anabolic and catabolic enzymes and human prostate cancer growth rates represented by serum Vpsa. The expression levels of spermine anabolic enzymes: ornithine decarboxylase (ODC1) and S-adenosylmethionine decarboxylase (AMD1) in benign epithelia surrounding cancer glands was logarithmically reduced with the increase of Vpsa (ODC1, p < 0.016; AMD1, p < 0.048), and antizyme (OAZ1) expression in cancer cells was increased with the increase of Vpsa (p < 0.001). Finally, we observed an inverse correlation between ODC1 and OAZ1 (p < 0.019) measured in cancer cells. These correlations may function to evaluate the aggressiveness of human prostate cancer, and assist patients and clinicians to select appropriate treatment strategies based on biological activities of individual tumors.