Metabolomics Contributions to the Discovery of Prostate Cancer Biomarkers

Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis

Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.

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.

Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models

Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa.

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

INTRODUCTION

In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue.

OBJECTIVES

Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis.

METHODS

We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled.

RESULTS

Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy.

CONCLUSION

The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BACKGROUND

Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

METHODS

A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

RESULTS

Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

CONCLUSIONS

The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Plasma metabolomics profiling of 580 patients from an Early Detection Research Network prostate cancer cohort

Prostate cancer is the second most common cancer in men and affects 1 in 9 men in the United States. Early screening for prostate cancer often involves monitoring levels of prostate-specific antigen (PSA) and performing digital rectal exams. However, a prostate biopsy is always required for definitive cancer diagnosis. The Early Detection Research Network (EDRN) is a consortium within the National Cancer Institute aimed at improving screening approaches and early detection of cancers. As part of this effort, the Weill Cornell EDRN Prostate Cancer has collected and biobanked specimens from men undergoing a prostate biopsy between 2008 and 2017. In this report, we describe blood metabolomics measurements for a subset of this population. The dataset includes detailed clinical and prospective records for 580 patients who underwent prostate biopsy, 287 of which were subsequentially diagnosed with prostate cancer, combined with profiling of 1,482 metabolites from plasma samples collected at the time of biopsy. We expect this dataset to provide a valuable resource for scientists investigating prostate cancer metabolism.

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLine^TM and Ulib^MS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

High resolution magic angle spinning MRS in prostate cancer

INTRODUCTION

Prostate cancer (PCa) is one of the leading causes of death among men worldwide. The current methods utilized to screen for prostate cancer may not have sufficient sensitivity in distinguishing aggressive from indolent diseases, which affect the quality of life of patients in the short and long term. The overdiagnosis of cases and overtreatment are prevalent due to the heterogeneity of the disease in terms of latent and progressive variants, as well as in the tissue types present in biopsy samples.

METHODS

The purpose of this review is to discuss the potential clinical benefits of incorporating high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS) modalities to overcome the current challenges in the diagnosis, prognostication, and monitoring of PCa.

Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy

Background

The incidence of esophageal squamous cell carcinoma in China ranks first in the world. The early diagnosis technology is underdeveloped, and the prognosis is poor, which seriously threatens the quality of life of the Chinese people. Epidemiological findings are related to factors such as diet, living habits, and age. The specific mechanism is not clear yet. Metabolomics is a kind of omics that simultaneously and quantitatively analyzes the comprehensive profile of metabolites in living systems. It has unique advantages in the study of the diagnosis and pathogenesis of tumor-related diseases, especially in the search for biomarkers. Therefore, it is desirable to perform metabolic profiling analysis of cancer tissues through metabolomics to find potential biomarkers for the diagnosis and treatment of esophageal squamous cell carcinoma.

Methods

HPLC-TOF-MS/MS technology and Illumina Hiseq Xten Sequencing was used for the analysis of 210 pairs of matched esophageal squamous cell carcinoma tissues and normal tissues in Zhenjiang City, Jiangsu Province, a high-incidence area of esophageal cancer in China. Bioinformatics analysis was also performed.

Results

Through metabolomic and transcriptomic analysis, this study found that a total of 269 differential metabolites were obtained in esophageal squamous cell carcinoma and normal tissues, and 48 differential metabolic pathways were obtained through KEGG enrichment analysis. After further screening and identification, 12 metabolites with potential biomarkers to differentiate esophageal squamous cell carcinoma from normal tissues were obtained.

Conclusions

From the metabolomic data, 4 unknown compounds were found to be abnormally expressed in esophageal squamous cell carcinoma for the first time, such as 9,10-epoxy-12,15-octadecadienoate; 3 metabolites were found in multiple abnormal expression in another tumor, but upregulation or downregulation was found for the first time in esophageal cancer, such as oleoyl glycine; at the same time, it was further confirmed that five metabolites were abnormally expressed in esophageal squamous cell carcinoma, which was similar to the results of other studies, such as PE.

Identification of characteristic metabolic panels for different stages of prostate cancer by 1H NMR-based metabolomics analysis

Background

Prostate cancer (PCa) is the second most prevalent cancer in males worldwide, yet detecting PCa and its metastases remains a major challenging task in clinical research setups. The present study aimed to characterize the metabolic changes underlying the PCa progression and investigate the efficacy of related metabolic panels for an accurate PCa assessment.

Methods

In the present study, 75 PCa subjects, 62 PCa patients with bone metastasis (PCaB), and 50 benign prostatic hyperplasia (BPH) patients were enrolled, and we performed a cross-sectional metabolomics analysis of serum samples collected from these subjects using a ¹H nuclear magnetic resonance (NMR)-based metabolomics approach.

Results

Multivariate analysis revealed that BPH, PCa, and PCaB groups showed distinct metabolic divisions, while univariate statistics integrated with variable importance in the projection (VIP) scores identified a differential metabolite series, which included energy, amino acid, and ketone body metabolism. Herein, we identified a series of characteristic serum metabolic changes, including decreased trends of 3-HB and acetone as well as elevated trends of alanine in PCa patients compared with BPH subjects, while increased levels of 3-HB and acetone as well as decreased levels of alanine in PCaB patients compared with PCa. Additionally, our results also revealed the metabolic panels of discriminant metabolites coupled with the clinical parameters (age and body mass index) for discrimination between PCa and BPH, PCaB and BPH, PCaB and PCa achieved the AUC values of 0.828, 0.917, and 0.872, respectively.

Conclusions

Overall, our study gave successful discrimination of BPH, PCa and PCaB, and we characterized the potential metabolic alterations involved in the PCa progression and its metastases, including 3-HB, acetone and alanine. The defined biomarker panels could be employed to aid in the diagnosis and classification of PCa in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03478-5.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

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.

Aqueous metabolome of tissue-specific conditional Pten-knockout mouse prostate cancer and TRAMP neuroendocrine carcinoma

BACKGROUND

Metabolic reprograming is now a recognized hallmark of cancer. The prostate-specific phosphatase and tensin homolog deleted on chromosome 10 (Pten) gene-conditional knockout (KO) mouse carcinogenesis model is highly desirable for studying prostate cancer biology and prevention due to its close resemblance of primary molecular defects and histopathological features of human prostate cancer. We have recently published macromolecular profiling of this model by proteomics and transcriptomics, denoting a preeminence of inflammation and myeloid suppressive immune cell features. Here, we performed metabolomic analyses of Pten-KO prostate versus wild type (WT) counterpart for discernable changes in the aqueous metabolites and contrasted to those in the TRAMP neuroendocrine carcinoma (NECa).

METHODS

Three matched pairs of tissue-specific conditional Pten-KO mouse prostate and WT prostate of litter/cage-mates at 20-22 weeks of age and three pairs of TRAMP NECa versus WT (28-31 weeks) were profiled for their global aqueous metabolite changes, using hydrophilic interaction liquid chromatography-tandem mass spectrometry.

RESULTS

The Pten-KO prostate increased purine nucleotide pools, cystathionine, and both reduced and oxidized glutathione (GSH, GSSG), and gluconate/glucuronate species in addition to cholesteryl sulfate and polyamine precursor ornithine. On the contrary, Pten-KO prostate contained diminished pools of glycolytic intermediates and phosphorylcholine derivatives, select amino acids, and their metabolites. Bioinformatic integration revealed a significant shunting of glucose away from glycolysis-citrate cycle and glycerol-lipid genesis to pentose phosphate cycle for NADPH/GSH/GSSG redox and pentose moieties for purine and pyrimidine nucleotides, and glycosylation/glucuronidation. Implicit arginine catabolism to ornithine was consistent with immunosuppression in Pten-KO model. While also increased in cystathionine-GSH/GSSG, purine, and pyrimidine nucleotide pools and glucuronidation at the expense of glycolysis-citrate cycle, the TRAMP NECa increased abundance of many amino acids, methyl donor S-adenosyl-methionine, and intermediates for phospholipids without increasing cholesteryl sulfate or ornithine.

CONCLUSIONS

The aqueous metabolomic patterns in Pten-KO prostate and TRAMP NECa shared similarities in the greater pools of cystathionine, GSH/GSSG redox pair, and nucleotides and shunting away from glycolysis-citrate cycle in both models. Remarkable metabolic distinctions between them included metabolisms of many amino acids (protein synthesis; arginine-ornithine/immune suppression) and cholesteryl sulfate and methylation donor for epigenetic regulations.

Identification of the metabolic signatures of prostate cancer by mass spectrometry-based plasma and urine metabolomics analysis

OBJECTIVE

Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men which is associated with profound metabolic changes. Systematic analysis of the metabolic alterations and identification of new biomarkers may benefit PCa diagnosis and a deep understanding of the pathological mechanism. The purpose of this study was to determine the metabolic features of PCa.

METHODS

Plasma and urine metabolites from 89 prostate cancer (PCa) patients, 84 benign prostatic hyperplasia (BPH) patients, and 70 healthy males were analyzed using LC-MS/MS and GC-MS. The Orthogonalised Partial Least Squares Discriminant Analysis (OPLS-DA) was used to find the significantly changed metabolites. The clinical value of the candidate markers was examined by receiver operating characteristic curve analysis and compared with prostate-specific antigen (PSA).

RESULTS

Multivariate statistical analyses found a series of altered metabolites, which related to the urea cycle, tricarboxylic acid cycle (TCA), fatty acid metabolism, and the glycine cleavage system. Plasma Glu/Gln showed the highest predictive value (AUC = 0.984) when differentiating PCa patients from healthy controls, with a higher sensitivity than PSA (96.6% vs. 94.4%). Both Glu/Gln and PSA displayed a low specificity when differentiating PCa patients from BPH patients (<53.2%), while the combination of Glu/Gln and PSA can further increase the diagnostic specificity to 66.9%.

CONCLUSIONS

The present study showed the metabolic features of PCa, provided strong evidence that the amide nitrogen and the energy metabolic pathways could be a valuable source of markers for PCa. Several candidate markers identified in this study were clinically valuable for further assessment.

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Objective

Prostate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.

Methods

A total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.

Results

Several differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).

Conclusion

The findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

Mechanisms Governing Metabolic Heterogeneity in Breast Cancer and Other Tumors

Reprogramming of metabolic priorities promotes tumor progression. Our understanding of the Warburg effect, based on studies of cultured cancer cells, has evolved to a more complex understanding of tumor metabolism within an ecosystem that provides and catabolizes diverse nutrients provided by the local tumor microenvironment. Recent studies have illustrated that heterogeneous metabolic changes occur at the level of tumor type, tumor subtype, within the tumor itself, and within the tumor microenvironment. Thus, altered metabolism occurs in cancer cells and in the tumor microenvironment (fibroblasts, immune cells and fat cells). Herein we describe how these growth advantages are obtained through either “convergent” genetic changes, in which common metabolic properties are induced as a final common pathway induced by diverse oncogene factors, or “divergent” genetic changes, in which distinct factors lead to subtype-selective phenotypes and thereby tumor heterogeneity. Metabolic heterogeneity allows subtyping of cancers and further metabolic heterogeneity occurs within the same tumor mass thought of as “microenvironmental metabolic nesting”. Furthermore, recent findings show that mutations of metabolic genes arise in the majority of tumors providing an opportunity for the development of more robust metabolic models of an individual patient’s tumor. The focus of this review is on the mechanisms governing this metabolic heterogeneity in breast cancer.

Prostate cancer cells survive anti-androgen and mitochondrial metabolic inhibitors by modulating glycolysis and mitochondrial metabolic activities

BACKGROUND

Most cancer cells are more glycolytic even under aerobic conditions compared with their normal counterparts. Recent evidence of tumor cell metabolism, however, shows that some tumors also increase mitochondrial oxidative phosphorylation (ox-phos) at some disease states during progression and/or development of drug resistance. Our data show that anti-androgen enzalutamide (ENZA) resistant prostate cancer (PCa) cells use more mitochondrial metabolism leading to higher ox-phos as compared to the ENZA-sensitive cells and can become vulnerable to mitochondrial metabolism targeted therapies.

METHODS

Seahorse assay, mass spectrometry and high resolution fluorescence confocal microscopy coupled with image analysis has been used to compare mitochondrial metabolism in ENZA-treated and -untreated anti-androgen-sensitive LNCaP and -resistant C4-2, CWR22ν1, and PCa2b cells. Ex vivo fluorescence microscopy and image analysis has been standardized to monitor mitochondrial electron transport (ETS) activity that likely increases ox-phos in circulating tumor cells (CTCs) isolated fom patients undergoing AR-targeted therapies.

RESULTS

Our data show that PCa cells that are resistant to anti-androgen ENZA switch from glycolysis to ox-phos leading to an increased ETS activity. ENZA pretreated cells are more vulnerable to ETS component complex I inhibitor IACS-010759 (IACS) and mitochondrial glutaminase inhibitor CB-839 that reduces glutamate supply to tricarboxylic acid cycle. CTCs isolated from 6 of 20 patient blood samples showed relatively higher ETS activity than the rest of the patients. All six patients have developed ENZA resistance within less than 6 months of the sample collection.

CONCLUSION

The enhanced growth inhibitory effects of mitochondrial metabolic inhibitors IACS and CB-839 in ENZA pretreated PCa cells provides a rationale for designing a drug combination trial. Patients can be selected for such trials by monitoring the mitochondrial ETS activities in their CTCs to maximize success.

Comparison of the metabolome in urine prior and eight weeks after radical prostatectomy uncovers pathologic and molecular features of prostate cancer

Prostate cancer (PCa) is associated with cellular metabolism alterations leading to changes of the metabolome. So far, studies investigating these alterations mainly focused on comparisons of metabolite profiles of PCa patients and healthy controls. In the present study we compared for the first time metabolite profiles in a significant number of paired urine samples collected before and eight weeks after radical prostatectomy (rPX) in 34 patients with PCa. Our comprehensive non-targeted liquid chromatographic-mass spectrometric metabolomics approach covered > 3000 metabolite ion masses. We annotated 23 metabolites showing significant changes eight weeks after rPX. While the levels of uridine and six acylcarnitines in urine were increased before surgery, lower levels were detected for 16 metabolites, like e.g. citrate, phenyl-lactic acid, choline, myo-inositol, emphasizing a relevant pathophysiological role of these biomarkers and the associated metabolic pathways. These results have important implications for potential use of metabolome analyses for detection of prostate cancer and related pathologic and molecular features.

Metabolic reprograming confers tamoxifen resistance in breast cancer

Breast cancer is the most common cancer among females and the leading cause of cancer-related deaths. Approximately 70 % of breast cancers are estrogen receptor (ER) positive. An ER antagonist such as tamoxifen is used as adjuvant therapy in ER-positive patients. The major problem with endocrine therapy is the emergence of acquired resistance in approximately 40 % of patients receiving tamoxifen. Metabolic alteration is one of the hallmarks of cancer cells. Rapidly proliferating cancer cells require increased nutritional support to fuel various functions such as proliferation, cell migration, and metastasis. Recent studies have established that the metabolic state of cancer cells influences their susceptibility to chemotherapeutic drugs and that cancer cells reprogram their metabolism to develop into resistant phenotypes. In this review, we discuss the major findings on metabolic pathway alterations in tamoxifen-resistant (TAMR) breast cancer and the molecular mechanisms known to regulate the expression and function of metabolic enzymes and the respective metabolite levels upon tamoxifen treatment. It is anticipated that this in-depth analysis of specific metabolic pathways in TAMR cancer might be exploited therapeutically.

Plasma Metabolomics for Discovery of Early Metabolic Markers of Prostate Cancer Based on Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry

BACKGROUND

The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa.

METHODS

Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS).

RESULTS

Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower.

CONCLUSION

Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-year-old men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate Curve-Resolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research.

A monolithic single-chip point-of-care platform for metabolomic prostate cancer detection

There is a global unmet need for rapid and cost-effective prognostic and diagnostic tools that can be used at the bedside or in the doctor's office to reduce the impact of serious disease. Many cancers are diagnosed late, leading to costly treatment and reduced life expectancy. With prostate cancer, the absence of a reliable test has inhibited the adoption of screening programs. We report a microelectronic point-of-care metabolite biomarker measurement platform and use it for prostate cancer detection. The platform, using an array of photodetectors configured to operate with targeted, multiplexed, colorimetric assays confined in monolithically integrated passive microfluidic channels, completes a combined assay of 4 metabolites in a drop of human plasma in under 2 min. A preliminary clinical study using l-amino acids, glutamate, choline, and sarcosine was used to train a cross-validated random forest algorithm. The system demonstrated sensitivity to prostate cancer of 94% with a specificity of 70% and an area under the curve of 0.78. The technology can implement many similar assay panels and hence has the potential to revolutionize low-cost, rapid, point-of-care testing.

Metabolic regulation of prostate cancer heterogeneity and plasticity

Metabolic reprogramming is one of the main hallmarks of cancer cells. It refers to the metabolic adaptations of tumor cells in response to nutrient deficiency, microenvironmental insults, and anti-cancer therapies. Metabolic transformation during tumor development plays a critical role in the continued tumor growth and progression and is driven by a complex interplay between the tumor mutational landscape, epigenetic modifications, and microenvironmental influences. Understanding the tumor metabolic vulnerabilities might open novel diagnostic and therapeutic approaches with the potential to improve the efficacy of current tumor treatments. Prostate cancer is a highly heterogeneous disease harboring different mutations and tumor cell phenotypes. While the increase of intra-tumor genetic and epigenetic heterogeneity is associated with tumor progression, less is known about metabolic regulation of prostate cancer cell heterogeneity and plasticity. This review summarizes the central metabolic adaptations in prostate tumors, state-of-the-art technologies for metabolic analysis, and the perspectives for metabolic targeting and diagnostic implications.

Active and prospective latent tuberculosis are associated with different metabolomic profiles: clinical potential for the identification of rapid and non-invasive biomarkers

Although 23% of world population is infected with Mycobacterium tuberculosis (M. tb), only 5-10% manifest the disease. Individuals surely exposed to M. tb that remain asymptomatic are considered potential latent TB (LTB) cases. Such asymptomatic M. tb.-exposed individuals represent a reservoir for active TB cases. Although accurate discrimination and early treatment of patients with active TB and asymptomatic M. tb.-exposed individuals are necessary to control TB, identifying those individuals at risk of developing active TB still remains a tremendous clinical challenge. This study aimed to characterize the differences in the serum metabolic profile specifically associated to active TB infected individuals or to asymptomatic M. tb.-exposed population. Interestingly, significant changes in a specific set of metabolites were shared when comparing either asymptomatic house-hold contacts of active TB patients (HHC-TB) or active TB patients (A-TB) to clinically healthy controls (HC). Furthermore, this analysis revealed statistically significant lower serum levels of aminoacids such as alanine, lysine, glutamate and glutamine, and citrate and choline in patients with A-TB, when compared to HHC-TB. The predictive ability of these metabolic changes was also evaluated. Although further validation in independent cohorts and comparison with other pulmonary infectious diseases will be necessary to assess the clinical potential, this analysis enabled the discrimination between HHC-TB and A-TB patients with an AUC value of 0.904 (confidence interval 0.81-1.00, p-value < 0.0001). Overall, the strategy described in this work could provide a sensitive, specific, and minimally invasive method that could eventually be translated into a clinical tool for TB control.

Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status

Prostate cancer (PCa) is the most common male cancer and the second leading cause of cancer death in United States men. Controversy continues over the effectiveness of prostate-specific antigen (PSA) for distinguishing aggressive from indolent PCa. There is a critical need for more specific and sensitive biomarkers to detect and distinguish low- versus high-risk PCa cases. Discovery metabolomics were performed utilizing ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) on plasma samples from 159 men with treatment naïve prostate cancer participating in the North Carolina-Louisiana PCa Project to determine if there were metabolites associated with aggressive PCa. Thirty-five identifiable plasma small molecules were associated with PCa aggressiveness, 15 of which were sphingolipids; nine common molecules were present in both African-American and European-American men. The molecules most associated with PCa aggressiveness were glycosphingolipids; levels of trihexosylceramide and tetrahexosylceramide were most closely associated with high-aggressive PCa. The Cancer Genome Atlas was queried to determine gene alterations within glycosphingolipid metabolism that are associated with PCa and other cancers. Genes that encode enzymes associated with the metabolism of glycosphingolipids were altered in 12% of PCa and >30% of lung, uterine, and ovarian cancers. These data suggest that the identified plasma (glyco)sphingolipids should be further validated for their association with aggressive PCa, suggesting that specific sphingolipids may be included in a diagnostic signature for PCa.

Targeted Metabolomics Analyses Reveal Specific Metabolic Alterations in High-Grade Prostate Cancer Patients

Prostate cancer (PCa) is a hormone-dependent tumor characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the nuclear magnetic resonance-based metabolomic profile of PCa patients with different tumor grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason score (GS) into low-GS (GS < 7) and high-GS PCa (GS ≥ 7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.

Metabolism of prostate cancer by magnetic resonance spectroscopy (MRS)

Understanding the metabolism of prostate cancer (PCa) is important for developing better diagnostic approaches and also for exploring new therapeutic targets. Magnetic resonance spectroscopy (MRS) techniques have been shown to be useful in the detection and quantification of metabolites. PCa illustrates metabolic phenotype, showing lower levels of citrate (Cit), a key metabolite of oxidative phosphorylation and alteration in several metabolic pathways to sustain tumor growth. Recently, dynamic nuclear polarization (DNP) studies have documented high rates of glycolysis (Warburg phenomenon) in PCa. High-throughput metabolic profiling strategies using MRS on variety of samples including intact tissues, biofluids like prostatic fluid, seminal fluid, blood plasma/sera, and urine have also played a vital role in understanding the abnormal metabolic activity of PCa patients. The enhanced analytical potential of these techniques in the detection and quantification of a large number of metabolites provides an in-depth understanding of metabolic rewiring associated with the tumorigenesis. Metabolomics analysis offers dual advantages of identification of diagnostic and predictive biomarkers as well as in understanding the altered metabolic pathways which can be targeted for inhibiting the cancer progression. This review briefly describes the potential applications of in vivo ¹H MRS, high-resolution magic angle spinning spectroscopy (HRMAS) and in vitro MRS methods in understanding the metabolic changes of PCa and its usefulness in the management of PCa patients.

Distinct lipid signatures are identified in the plasma of rats with chronic inflammation induced by estradiol benzoate and sex hormones

INTRODUCTION

Prostatitis is likely to occur in younger or middle-aged men, while prostate cancer is likely to occur in older men. Although amino acids and lipids as biomarkers of prostate cancer have been examined using prostate cancer cell lines/tissues, no previous studies have evaluated amino acids or lipids as potential chronic prostatitis biomarkers.

OBJECTIVES

The study's aim was to identify amino acids and lipids that could serve as potential biomarkers of chronic prostatitis.

METHODS

We profiled the amino acids and lipids found in plasma from rats collected in a previous study. In brief, a total of 148 Sprague-Dawley rats (offspring) were dosed with estradiol benzoate (EB) on postnatal days (PNDs) 1, 3 and 5, and subsequently dosed with testosterone (T)/estradiol (E) tubes via subcutaneous implants from PND 90 to 200. Plasma was collected on PNDs 30, 90, 100, 145 and 200. Analysis was conducted with a Xevo TQ-S triple-quadrupole mass spectrometer using a Biocrates AbsoluteIDQ p180 kit.

RESULTS

Plasma acylcarnitines [(C2, C16:1, C18, C18:1, C18:1-OH, and C18:2)], glycerophospholipids (lysophosphatidylcholine-acyl, -di-acyl, and -di-acyl acyl-alkyl) and sphingomyelins [SM (OH) C16:1, SM C18:0, SM C18:1, and SM C20:2] significantly increased on PND 145, when chronic inflammation was observed in the dorsolateral prostate of rats dosed with EB, T, and E. No statistical significances of amino acid levels were observed in the EB + T + E group on PND 145.

CONCLUSION

Exposure to EB, T, and E altered lipid levels in rat plasma with chronic prostate inflammation. These findings suggest that the identified lipids may be predictive chronic prostatitis biomarkers. The results require confirmation through additional nonclinical and human studies.

Targeting CPT1B as a potential therapeutic strategy in castration-resistant and enzalutamide-resistant prostate cancer

BACKGROUND

Prostate cancer is characterized by aberrant lipid metabolism, including elevated fatty acid oxidation. Carnitine palmitoyltransferase 1B (CPT1B) catalyzes the rate-limiting step of fatty acid oxidation. This study aimed to determine if CPT1B has a critical role in prostate cancer progression and to identify its regulatory mechanism.

METHODS

CPT1B expression data from The Cancer Genome Atlas and Gene Expression Omnibus databases was compared with patient survival data. A tissue microarray was constructed with 60 samples of prostate cancer and immunohistochemically stained for CPT1B. Castration-resistant prostate cancer (CRPC) cell lines 22RV1 and C4-2 in which CPT1B expression had been stably knocked down were established; and cell proliferation, cell cycle distribution, and invasion were investigated by Cell Counting Kit-8 (CCK-8) and colony formation assays, flow cytometry, and Transwell assays, respectively. To examine the impact of androgen receptor (AR) inhibition on CPT1B expression, JASPAR CORE was searched to identify AR-binding sites in CPT1B. Dual luciferase and ChIP assays were performed to confirm CPT1B activity and AR binding, respectively. Differentially expressed genes (DEGs) in prostate cancer underwent gene set enrichment analysis (GSEA). Enzalutamide-resistant C4-2 cells were generated and the mechanism of enzalutamide resistance and downstream signaling pathway changes of CPT1B to C4-2 was explored through CCK-8 test.

RESULTS

CPT1B expression was upregulated in human prostate cancer compared with normal prostate tissue and was associated with poor disease-free survival and overall survival. Silencing of CPT1B resulted in downregulated cell proliferation, reduced S-phase distribution, and lower invasive ability, whereas the opposite was observed in CRPC cells overexpressing CPTB1. DEGS in prostate cancer were correlated with G-protein-coupled receptor signaling, molecular transducer activity, and calcium ion binding. AR may regulate CPT1B expression and activity via specific binding sites, as confirmed by dual luciferase and ChIP assays. The CCK-8 experiment demonstrated that CPT1B overexpression in C4-2 cells did not significantly increase the ability of enzalutamide resistance. However, overexpression of CPT1B in C4-2R cells significantly increased the enzalutamide resistance. Upregulation of CPT1B expression increased AKT expression and phosphorylation.

CONCLUSIONS

CPT1B is upregulated in prostate cancer and is correlated with poor prognosis, indicating its potential as a biomarker. AR inhibits the transcription of CPT1B. In the CRPC cell line, overexpression of CPT1B alone cannot promote enzalutamide resistance, but in the drug-resistant line C4-2R, overexpression of CPT1B can promote the resistance of C4-2R to enzalutamide.

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.