Comprehensive plasma steroidomics reveals subtle alterations of systemic steroid profile in patients at different stages of prostate cancer disease.

The steroid submetabolome, or steroidome, is of particular interest in prostate cancer (PCa) as the dependence of PCa growth on androgens is well known and has been routinely exploited in treatment for decades. Nevertheless, the community is still far from a comprehensive understanding of steroid involvement in PCa both at the tissue and at systemic level. In this study we used liquid chromatography/high resolution mass spectrometry (LC/HRMS) backed by a dynamic retention time database DynaSTI to obtain a readout on circulating steroids in a cohort reflecting a progression of the PCa. Hence, 60 relevant compounds were annotated in the resulting LC/HRMS data, including 22 unknown steroid isomers therein. Principal component analysis revealed only subtle alterations of the systemic steroidome in the study groups. Next, a supervised approach allowed for a differentiation between the healthy state and any of the stages of the disease. Subsequent clustering of steroid metabolites revealed two groups responsible for this outcome: one consisted primarily of the androgens, whereas another contained corticosterone and its metabolites. The androgen data supported the currently established involvement of a hypothalamic-pituitary-gonadal axis in the development of PCa, whereas biological role of corticosterone remained elusive. On top of that, current results suggested a need for improvement in the dynamic range of the analytical methods to better understand the role of low abundant steroids, as the analysis revealed an involvement of estrogen metabolites. In particular, 2-hydroxyestradiol-3-methylether, one of the compounds present in the disease phenotype, was annotated and reported for the first time in men.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review.

INTRODUCTION: Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC. OBJECTIVES: To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine. METHODS: A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine). RESULTS: Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented. CONCLUSION: The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer.

Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.

In Vivo Targeted Metabolomic Profiling of Prostanit, a Novel Anti-PAD NO-Donating Alprostadil-Based Drug.

Prostanit is a novel drug developed for the treatment of peripheral arterial diseases. It consists of a prostaglandin E1 (PGE1) moiety with two nitric oxide (NO) donor fragments, which provide a combined vasodilation effect on smooth muscles and vascular spastic reaction. Prostanit pharmacokinetics, however, remains poorly investigated. Thus, the object of this study was to investigate the pharmacokinetics of Prostanit-related and -affected metabolites in rabbit plasma using the liquid chromatography-mass spectrometry (LC-MS) approach. Besides, NO generation from Prostanit in isolated rat aorta and human smooth muscle cells was studied using the Griess method. In plasma, Prostanit was rapidly metabolized to 1,3-dinitroglycerol (1,3-DNG), PGE1, and 13,14-dihydro-15-keto-PGE1. Simultaneously, the constant growth of amino acid (proline, 4-hydroxyproline, alanine, phenylalanine, etc.), steroid (androsterone and corticosterone), and purine (adenosine, adenosine-5 monophosphate, and guanosine) levels was observed. Glycine, aspartate, cortisol, and testosterone levels were decreased. Ex vivo Prostanit induced both NO synthase-dependent and -independent NO generation. The observed pharmacokinetic properties suggested some novel beneficial activities (i.e., effect prolongation and anti-inflammation). These properties may provide a basis for future research of the effectiveness and safety of Prostanit, as well as for its characterization from a clinical perspective.

Plasma metabolomic profile in prostatic intraepithelial neoplasia and prostate cancer and associations with the prostate-specific antigen and the Gleason score.

INTRODUCTION: The metabolic alterations reflecting the influence of prostate cancer cells can be captured through metabolomic profiling. OBJECTIVE: To characterize the plasma metabolomic profile in prostatic intraepithelial neoplasia (PIN) and prostate cancer (PCa). METHODS: Metabolomics analyses were performed in plasma samples from individuals classified as non-cancerous control (n = 36), with PIN (n = 16), or PCa (n = 27). Untargeted [26 moieties identified after pre-processing by gas chromatography/mass spectrometry (GC/MS)] and targeted [46 amino acids, carbohydrates, organic acids and fatty acids by GC/MS, and 16 nucleosides and amino acids by ultra performance liquid chromatography-triple quadrupole/mass spectrometry (UPLC-TQ/MS)] analyses were performed. Prostate specific antigen (PSA) concentrations were measured in all samples. In PCa patients, the Gleason scores were determined. RESULTS: The metabolites that were best discriminated (p < 0.05, FDR < 0.2) for the Kruskal-Wallis test with Dunn's post-hoc comparing the control versus the PIN and PCa groups included isoleucine, serine, threonine, cysteine, sarcosine, glyceric acid, among several others. PIN was mainly characterized by alterations on steroidogenesis, glycine and serine metabolism, methionine metabolism and arachidonic acid metabolism, among others. In the case of PCa, the most predominant metabolic alterations were ubiquinone biosynthesis, catecholamine biosynthesis, thyroid hormone synthesis, porphyrin and purine metabolism. In addition, we identified metabolites that were correlated to the PSA [i.e. hypoxanthine (r = - 0.60, p < 0.05; r = - 0.54, p < 0.01) and uridine (r = - 0.58, p < 0.05; r = - 0.50, p < 0.01) in PIN and PCa groups, respectively] and metabolites that were significantly different in PCa patients with Gleason score < 7 and ≥ 7 [i.e. arachidonic acid, median (P25-P75) = 883.0 (619.8-956.4) versus 570.8 (505.6-651.8), respectively (p < 0.01)]. CONCLUSIONS: This human plasma metabolomic assessment contributes to the understanding of the unique metabolic features exhibited in PIN and PCa and provides a list of metabolites that can have the potential to be used as biomarkers for early detection of disease progression and management.

Plasma Sarcosine Measured by Gas Chromatography-Mass Spectrometry Distinguishes Prostatic Intraepithelial Neoplasia and Prostate Cancer from Benign Prostate Hyperplasia.

OBJECTIVE: Sarcosine was postulated in 2009 as a biomarker for prostate cancer (PCa). Here, we assess plasma sarcosine as a biomarker that is complementary to prostate-specific antigen (PSA). METHODS: Plasma sarcosine was measured using gas chromatography-mass spectrometry (GC-MS) in adults classified as noncancerous controls (with benign prostate hyperplasia [BPH], n = 36), with prostatic intraepithelial neoplasia (PIN, n = 16), or with PCa (n = 27). Diagnostic accuracy was assessed using receiver operating characteristic curve analysis. RESULTS: Plasma sarcosine levels were higher in the PCa (2.0 µM [1.3-3.3 µM], P <.01) and the PIN (1.9 µM [1.2-6.5 µM], P <.001) groups than in the BPH (0.9 µM [0.6-1.4 µM]) group. Plasma sarcosine had "good" and "very good" discriminative capability to detect PIN (area under the curve [AUC], 0.734) and PCa (AUC, 0.833) versus BPH, respectively. The use of PSA and sarcosine together improved the overall diagnostic accuracy to detect PIN and PCa versus BPH. CONCLUSION: Plasma sarcosine measured by GC-MS had "good" and "very good" classification performance for distinguishing PIN and PCa, respectively, relative to noncancerous patients diagnosed with BPH.