Inflammatory metabolic profile of South African patients with prostate cancer

Nonsevere Burn Induces a Prolonged Systemic Metabolic Phenotype Indicative of a Persistent Inflammatory Response Postinjury

Globally, burns are a significant cause of injury that can cause substantial acute trauma as well as lead to increased incidence of chronic comorbidity and disease. To date, research has primarily focused on the systemic response to severe injury, with little in the literature reported on the impact of nonsevere injuries (<15% total burn surface area; TBSA). To elucidate the metabolic consequences of a nonsevere burn injury, longitudinal plasma was collected from adults (n = 35) who presented at hospital with a nonsevere burn injury at admission, and at 6 week follow up. A cross-sectional baseline sample was also collected from nonburn control participants (n = 14). Samples underwent multiplatform metabolic phenotyping using 1H nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry to quantify 112 lipoprotein and glycoprotein signatures and 852 lipid species from across 20 subclasses. Multivariate data modeling (orthogonal projections to latent structures-discriminate analysis; OPLS-DA) revealed alterations in lipoprotein and lipid metabolism when comparing the baseline control to hospital admission samples, with the phenotypic signature found to be sustained at follow up. Univariate (Mann-Whitney U) testing and OPLS-DA indicated specific increases in GlycB (p-value < 1.0e-4), low density lipoprotein-2 subfractions (variable importance in projection score; VIP > 6.83e-1) and monoacyglyceride (20:4) (p-value < 1.0e-4) and decreases in circulating anti-inflammatory high-density lipoprotein-4 subfractions (VIP > 7.75e-1), phosphatidylcholines, phosphatidylglycerols, phosphatidylinositols, and phosphatidylserines. The results indicate a persistent systemic metabolic phenotype that occurs even in cases of a nonsevere burn injury. The phenotype is indicative of an acute inflammatory profile that continues to be sustained postinjury, suggesting an impact on systems health beyond the site of injury. The phenotypes contained metabolic signatures consistent with chronic inflammatory states reported to have an elevated incidence postburn injury. Such phenotypic signatures may provide patient stratification opportunities, to identify individual responses to injury, personalize intervention strategies, and improve acute care, reducing the risk of chronic comorbidity.

Detection of Cancer-Associated Gene Mutations in Urinary Cell-Free DNA among Prostate Cancer Patients in South Africa

Prostate cancer (PCa) is the most common cause of cancer death among African men. The presence of tumor-specific variations in cell-free DNA (cfDNA), such as mutations, microsatellite instability, and DNA methylation, has been explored as a source of biomarkers for cancer diagnosis. In this study, we investigated the diagnostic role of cfDNA among South African PCa patients. We performed whole exome sequencing (WES) of urinary cfDNA. We identified a novel panel of 31 significantly deregulated somatic mutated genes between PCa and benign prostatic hyperplasia (BPH). Additionally, we performed whole-genome sequencing (WGS) on matching PCa and normal prostate tissue in an independent PCa cohort from South Africa. Our results suggest that the mutations are of germline origin as they were also found in the normal prostate tissue. In conclusion, our study contributes to the knowledge of cfDNA as a biomarker for diagnosing PCa in the South African population.

Potential of miRNAs in Plasma Extracellular Vesicle for the Stratification of Prostate Cancer in a South African Population

Prostate cancer (PCa) is the most common cause of cancer death among African men. The analysis of microRNAs (miRNAs) in plasma extracellular vesicles (EVs) can be utilized as a non-invasive tool for the diagnosis of PCa. In this study, we used small RNA sequencing to profile miRNAs cargo in plasma EVs from South African PCa patients. We evaluated the differential expression of miRNAs between low and high Gleason scores in the plasma EVs of South African patients and in the prostatic tissue from data available in the Cancer Genome Atlas (TCGA) Data Portal. We identified 7 miRNAs differently expressed in both EVs and prostatic tissues. We evaluated their expression using qPCR in a larger cohort of 10 patients with benign prostatic hyperplasia (BPH) and 24 patients with PCa. Here, we reported that the ratio between two of these miRNAs (i.e., miR-194-5p/miR-16-5p) showed a higher concentration in PCa compared to BPH and in metastatic PCa compared to localized PCa. We explored for the first time the profiling of miRNAs cargo in plasma EVs as a tool for the identification of putative markers in the South African population. Our finding indicated the ratio miR-194-5p/miR-16-5p as a non-invasive marker for the evaluation of PCa aggressiveness in this population.

MetChem: a new pipeline to explore structural similarity across metabolite modules

Summary

Computational analysis and interpretation of metabolomic profiling data remains a major challenge in translational research. Exploring metabolic biomarkers and dysregulated metabolic pathways associated with a patient phenotype could offer new opportunities for targeted therapeutic intervention. Metabolite clustering based on structural similarity has the potential to uncover common underpinnings of biological processes. To address this need, we have developed the MetChem package. MetChem is a quick and simple tool that allows to classify metabolites in structurally related modules, thus revealing their functional information.

Availabilityand implementation

MetChem is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later).

Metabolite Analyses Using Nuclear Magnetic Resonance (NMR) Spectroscopy in Plasma of Patients with Prostate Cancer

Nuclear magnetic resonance (NMR) spectroscopy enables the detection and the quantification of a large range of molecules, including low-molecular-weight metabolites and lipids. NMR spectroscopy is a powerful approach when applied to the high-throughput analysis of plasma or serum samples allowing, in addition, the detection of total proteins, lipoproteins, and signals arising from the glycosylation of circulating acute-phase proteins. Here, we describe the usage of NMR spectroscopy for profiling the plasma or serum of patients with prostate cancer.

Can Diet Prevent Urological Cancers? An Update on Carotenoids as Chemopreventive Agents

Urological cancers, namely prostate, bladder, kidney, testicular, and penile cancers, are common conditions that constitute almost one-quarter of all malignant diseases in men. Urological cancers tend to affect older individuals, and their development is influenced by modifiable metabolic, behavioral, and environmental risk factors. Phytochemicals may have cancer-fighting properties and protect against cancer development, slow its spread, and reduce the risk of cancer deaths in humans. This paper aims to review the current literature in regard to the effects of carotenoids in reducing urological cancer risk.

Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer

The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

KODAMA exploratory analysis in metabolic phenotyping

KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research.

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

Fatty Acid Metabolism Reprogramming in Advanced Prostate Cancer

Prostate cancer (PCa) is a carcinoma in which fatty acids are abundant. Fatty acid metabolism is rewired during PCa development. Although PCa can be treated with hormone therapy, after prolonged treatment, castration-resistant prostate cancer can develop and can lead to increased mortality. Changes to fatty acid metabolism occur systemically and locally in prostate cancer patients, and understanding these changes may lead to individualized treatments, especially in advanced, castration-resistant prostate cancers. The fatty acid metabolic changes are not merely reflective of oncogenic activity, but in many cases, these represent a critical factor in cancer initiation and development. In this review, we analyzed the literature regarding systemic changes to fatty acid metabolism in PCa patients and how these changes relate to obesity, diet, circulating metabolites, and peri-prostatic adipose tissue. We also analyzed cellular fatty acid metabolism in prostate cancer, including fatty acid uptake, de novo lipogenesis, fatty acid elongation, and oxidation. This review broadens our view of fatty acid switches in PCa and presents potential candidates for PCa treatment and diagnosis.

Serum Metabolomic and Lipoprotein Profiling of Pancreatic Ductal Adenocarcinoma Patients of African Ancestry

Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with a characteristic dysregulated metabolism. Abnormal clinicopathological features linked to defective metabolic and inflammatory response pathways can induce PDAC development and progression. In this study, we investigated the metabolites and lipoproteins profiles of PDAC patients of African ancestry. Nuclear Magnetic Resonance (NMR) spectroscopy was conducted on serum obtained from consenting individuals (34 PDAC, 6 Chronic Pancreatitis, and 6 healthy participants). Seventy-five signals were quantified from each NMR spectrum. The Liposcale test was used for lipoprotein characterization. Spearman's correlation and Kapan Meier tests were conducted for correlation and survival analyses, respectively. In our patient cohort, the results demonstrated that levels of metabolites involved in the glycolytic pathway increased with the tumour stage. Raised ethanol and 3-hydroxybutyrate were independently correlated with a shorter patient survival time, irrespective of tumour stage. Furthermore, increased levels of bilirubin resulted in an abnormal lipoprotein profile in PDAC patients. Additionally, we observed that the levels of a panel of metabolites (such as glucose and lactate) and lipoproteins correlated with those of inflammatory markers. Taken together, the metabolic phenotype can help distinguish PDAC severity and be used to predict patient survival and inform treatment intervention.