Metabolomics Biomarkers of Prostate Cancer: A Systematic Review

Current Issues in Molecular Biology

Prostate cancer (PCa) remains one of the leading causes of cancer mortality in men worldwide, currently lacking specific, early detection and staging biomarkers. In this regard, modern research focuses efforts on the discovery of novel molecules that could represent potential future non-invasive biomarkers for the diagnosis of PCa, as well as therapeutic targets. Mounting evidence shows that cancer cells express an altered metabolism in their early stages, making metabolomics a promising tool for the discovery of altered pathways and potential biomarker molecules. In this study, we first performed untargeted metabolomic profiling on 48 PCa plasma samples and 23 healthy controls using ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-[ESI+]-MS) for the discovery of metabolites with altered profiles. Secondly, we selected five molecules (L-proline, L-tryptophan, acetylcarnitine, lysophosphatidylcho...

Journal of Cancer Metastasis and Treatment , 2019

Aim: Prostate cancer (PCa) is the most commonly diagnosed non-skin cancer among men. Serum prostate-specific antigen level is used as a standard PCa biomarker for over 20 years. However, it has only 33% specificity and 86% sensitivity (for the cutoff value for prostate biopsy of > 4 ng/mL). This leads to overdiagnosis and overtreatment. In-depth insight into PCa metabolomics enables discovery of novel PCa biomarkers. Methods: Metabolomic alternation in PCa serum, urine and interstitial fluid was examined using gold-nanoparticle-based laser mass spectrometry imaging. This study included 5 patients who underwent prostate biopsy with positive result, 5 patients with negative result and 10 healthy controls. Results: Over two hundred differentiating metabolites (87 in urine, 54 in serum and 78 in interstitial fluid) were detected. Four, twenty two and ten metabolites from urine, serum and interstitial fluid respectively showed statistical significant differential abundance between cancer and control group. Conclusion: Comprehensive metabolomic profile of PCa has been identified. Out of 36 metabolites, 20 were identified and should be further evaluated in clinical trials as a potential PCa biomarker. Urine concentration of triglyceride (12:0/20:1) showed over 10 times higher abundance in PCa samples in comparison to healthy controls and is considered the most promising potential biomarker.

International journal of molecular sciences, 2024

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Journal of Proteome Research, 2014

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, overdiagnosis, and overtreatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lysophospholipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.

Cancer Biology & Medicine, 2021

Objective: Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH). Methods: We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic...

Revista Urología Colombiana / Colombian Urology Journal

Purpose To identify metabolites in humans that can be associated with the presence of malignant disturbances of the prostate. Methods In the present study, we selected male patients aged between 46 and 82 years who were considered at risk of prostate cancer due to elevated levels of prostate-specific antigen (PSA) or abnormal results on the digital rectal examination. All selected patients came from two university hospitals (Hospital Universitario del Valle and Clínica Rafael Uribe Uribe) and were divided into 2 groups: cancer (12 patients) and non-cancer (20 patients). Cancer was confirmed by histology, and none of the patients underwent any previous treatment. Standard protocols were applied to all the collected blood samples. The resulting plasma samples were kept at -80°C, and a profile of each one was acquired by nuclear magnetic resonance (NMR) using established experiments. Multivariate analyses were applied to this dataset, first to establish the quality of the data and iden...

The journals of gerontology. Series A, Biological sciences and medical sciences, 2018

Impaired metabolism may play a role in the development and lethality of prostate cancer, yet a comprehensive analysis of the interrelationships appears lacking. We measured 625 metabolites using ultrahigh performance LC/MS-GC/MS of prediagnostic serum from 197 prostate cancer cases in the ATBC Study (ages at diagnosis, 55-86 years). Cox proportional hazards models estimated associations between circulating metabolites and prostate cancer mortality for 1-standard deviation (SD) differences (log-metabolite scale), adjusted for age, year of diagnosis, and disease stage. Associations between metabolite chemical classes and survival were examined through pathway analysis, and Cox models assessed the relationship with a sterol/steroid metabolite principal component analysis factor score. Elevated serum N-oleoyl taurine was significantly associated with prostate cancer-specific mortality (HR=1.72 per 1-SD, p<0.00008, Bonferroni corrected threshold=0.05/625; HR=3.6 for highest vs lowest ...

Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies, 2015

Prostate cancer (PCa) is the second leading cause of mortality in men, the present diagnosis method being based on serum prostate-specific antigen (PSA) screening, with low specificity and overestimated values. A combined untargeted and targeted metabolomic study of metabolites from blood serum samples collected from healthy (n=11), hyperplasia (n=39) and prostate cancer (n=83) patients is presented, using the HPLC-(ESI+) QTOF-MS analysis. The profile of blood serum samples provided complementary information obtained from Base Peak Chromatograms and Dissect chromatograms. Based on dissect chromatograms, two different methods of statistical analysis were used, either based on the instrument software with automated alignments with/without normalization (Profile Analysis), or based on manual alignment followed by statistical analysis (Unscrambler10.X software). Both methods used the unsupervised Principal Component Analysis which discriminated between normal, hyperplasia and cancer patients. The second method allowed a better discrimination between groups, by qualitative and quantitative parameters (m/z values versus peak areas) and better possibilities to identify the molecules responsible for such discriminations. Considering the retention time interval (6-17 min), four molecules to be considered as putative biomarkers for hyperplasia or prostate cancer were identified: Prostaglandins E2/G2, Pregnenolone/ethyltestosterone, Lysophosphatidylcholine18:2/0:0, Galactosylceramide (18:1/24:1). By using larger patient cohorts and optimizing the data processing and chemometric analysis, more reliable biomarkers for prostate hyperplasia and cancer can be discovered and quantified. This preliminary study has had promising findings for the implementation and validation of metabolomic targeted analysis in clinical laboratories.

Future oncology (London, England), 2017

To assess the predictive value of metabolomic analysis for the presence of prostate cancer (PCa) at first systematic biopsy. Ninety serum samples from patients with suspicion for PCa were included. Targeted and nontargeted metabolomic analysis was performed. Six metabolites were combined into a predictive score. A cutoff value of 0.528 for the metabolomic score showed a good accuracy for the prediction of PCa at biopsy (Area under the curve (AUC): 0.779; p < 0.001). These results were validated in a subgroup of patients, showing similar accuracy (p = 0.1). For patients with prostate specific antigen (PSA) less than 10 ng/ml, the score showed a Se 80.95%, Sp 64.52% for the detection of PCa at biopsy. Metabolomic analysis can predict the outcome of the first systematic biopsy.

International Journal of Cancer

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR 1SD) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR 1SD = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR 1SD = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR 1SD = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR 1SD = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer. What's new? This prospective study is the largest investigation of metabolite profile and prostate cancer risk, to date. We found that patterns in plasma metabolite profile (characterized by higher concentrations of phosphatidylcholines and hydroxysphingomyelins; specific acylcarnitines, amino acids and a biogenic amine; and lysophosphatidylcholines, respectively) were associated with subsequent lower risk of more aggressive tumor subtypes and prostate cancer death. Moreover, the results suggest that metabolite profile may be relevant to the etiology of advanced stage disease.