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Relevance of emerging metabolomics-based biomarkers of prostate cancer: a systematic review

Published online by Cambridge University Press:  22 June 2022

Navneeta Bansal
Affiliation:
Department of Urology, King George's Medical University, Lucknow, India
Manoj Kumar*
Affiliation:
Department of Urology, King George's Medical University, Lucknow, India
S. N. Sankhwar
Affiliation:
Department of Urology, King George's Medical University, Lucknow, India
Ashish Gupta*
Affiliation:
Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India
*
Authors for correspondence: Manoj Kumar, E-mail: dr_manojait@yahoo.com; Ashish Gupta, E-mail: ashishg24@yahoo.co.in
Authors for correspondence: Manoj Kumar, E-mail: dr_manojait@yahoo.com; Ashish Gupta, E-mail: ashishg24@yahoo.co.in

Abstract

Prostate cancer (PC) presents great challenges in early diagnosis and often leads to unnecessary invasive procedures as well as over diagnosis and treatment, thus highlighting the need for promising early diagnostic biomarkers. The aim of this review is to provide an up-to-date summary of chronologically existing metabolomics PC biomarkers, their potential to improve clinical PC diagnosis and to reduce the proliferation and monitoring of PC. The systematic research was conducted on PubMed in accordance with PRISMA guidelines to report PC biomarkers. The majority of the studies distinguished malignant from benign prostate and few explored the biomarkers associated with the progression of PC. The present review summarises the primary outcomes of most significant studies to extend our knowledge of PC metabolomics biomarkers. We observed divergent inter-laboratory technical procedures employing different statistical approaches produced abundant information regarding PC metabolites perturbation. Since PC metabolomics is still in its early phase, it is vital that we dig out the most specific, sensitive and accurate metabolic signatures and conduct more studies with milestone findings with comparable sample sizes to validate and corroborate the findings.

Type
Review
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Siegel, RL et al. (2022) Cancer statistics 2022. CA: A Cancer Journal for Clinicians 72, 733.Google Scholar
Heidenreich, A et al. (2014) EAU Guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent-update 2013. European Urology 65, 124137.CrossRefGoogle ScholarPubMed
Ellinger, J et al. (2022) Prostate cancer treatment costs increase more rapidly than for any other cancer-how to reverse the trend? The EPMA Journal 13, 17.CrossRefGoogle ScholarPubMed
Silberstein, JL et al. (2013) Current clinical challenges in prostate cancer. Translational Andrology and Urology 2, 122.Google ScholarPubMed
Rawla, P (2019) Epidemiology of prostate cancer. World Journal of Oncology 10, 6389.CrossRefGoogle ScholarPubMed
Karantanos, T et al. (2013) Prostate cancer progression after androgen deprivation therapy: mechanisms of castrate resistance and novel therapeutic approaches. Oncogene 32, 5501.CrossRefGoogle ScholarPubMed
Pesapane, F et al. (2021) Comparison of sensitivity and specificity of biparametric versus multiparametric prostate MRI in the detection of prostate cancer in 431 men with elevated prostate-specific antigen levels. Diagnostics 11, 12231233.CrossRefGoogle ScholarPubMed
Harvey, CJ et al. (2012) Application of transrectal ultrasound in prostate cancer. The British Journal of Radiology 85, S3S17.CrossRefGoogle Scholar
Welty, CJ et al. (2014) The ongoing need for improved risk stratification and monitoring for those on active surveillance for early-stage prostate cancer. European Urology 65, 10321033.CrossRefGoogle ScholarPubMed
Wang, G et al. (2018) Genetics and biology of prostate cancer. Genes & Development 32, 11051140.CrossRefGoogle ScholarPubMed
Di Donato, M et al. (2015) Non-genomic androgen action regulates proliferative/migratory signaling in stromal cells. Frontiers in Endocrinology 5, 225.CrossRefGoogle ScholarPubMed
Rodriguez-Urrego, PA et al. (2011) Interobserver and intraobserver reproducibility in digital and routine microscopic assessment of prostate needle biopsies. Human Pathology 42, 6874.CrossRefGoogle ScholarPubMed
Taichman, RS et al. (2007) The evolving biology and treatment of prostate cancer. Journal of Clinical Investigation 117, 23512361.CrossRefGoogle ScholarPubMed
Cuzick, J et al. (2014) Prevention and early detection of prostate cancer. The Lancet. Oncology 15, e484e492.CrossRefGoogle ScholarPubMed
Prensner, JR et al. (2012) Beyond PSA: the next generation of prostate cancer biomarkers. Science Translational Medicine 4, 127rv3127rv3.CrossRefGoogle ScholarPubMed
Ganzer, R et al. (2013) Fourteen-year oncological and functional outcomes of high-intensity focused ultrasound in localized prostate cancer. BJU International 112, 322329.CrossRefGoogle ScholarPubMed
Vassilikos, EJK et al. (2000) Relapse and cure rates of prostate cancer patients after radical prostatectomy and 5 years of follow-up. Clinical Biochemistry 33, 115123.CrossRefGoogle ScholarPubMed
Tomita, M et al. (2012) Systems biology, metabolomics, and cancer metabolism. Science 336, 990991.CrossRefGoogle ScholarPubMed
Gupta, A et al. (2020) Role of metabolomics-derived biomarkers to identify renal cell carcinoma: a comprehensive perspective of the past ten years and advancements. Expert Review of Molecular Diagnostics 20, 518.CrossRefGoogle ScholarPubMed
Psychogios, M et al. (2011) The human serum metabolome. PLoS ONE 6, e16957.CrossRefGoogle ScholarPubMed
Osl, M et al. (2008) A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry. Bioinformatics 24, 29082914.CrossRefGoogle ScholarPubMed
Stabler, S et al. (2011) Serum methionine metabolites are risk factors for metastatic prostate cancer progression. PLoS ONE 6, e22486.CrossRefGoogle ScholarPubMed
Fan, Y et al. (2011) Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer. Journal of Proteome Research 10, 13611373.CrossRefGoogle ScholarPubMed
Zang, X et al. (2014) Feasibility of detecting prostate cancer by ultraperformance liquid chromatography–mass spectrometry serum metabolomics. Journal of Proteome Research 13, 34443454.CrossRefGoogle ScholarPubMed
Huang, G et al. (2014) Metabolomic evaluation of the response to endocrine therapy in patients with prostate cancer. European Journal of Pharmacology 729, 132137.CrossRefGoogle ScholarPubMed
De Vogel, S et al. (2014) Sarcosine and other metabolites along the choline oxidation pathway in relation to prostate cancer – a large nested case–control study within the JANUS cohort in Norway. International Journal of Cancer 134, 197206.CrossRefGoogle ScholarPubMed
Mondul, AM et al. (2014) 1-Stearoylglycerol is associated with risk of prostate cancer: results from a serum metabolomic profiling analysis. Metabolomics 10, 10361041.CrossRefGoogle Scholar
Mondul, AM et al. (2015) Metabolomic analysis of prostate cancer risk in a prospective cohort: the alpha-tocopherol, beta-carotene cancer prevention (ATBC) study. International Journal of Cancer 137, 21242132.CrossRefGoogle Scholar
Kumar, D et al. (2015) Metabolomics-derived prostate cancer biomarkers: fact or fiction? Journal of Proteome Research 14, 14551464.CrossRefGoogle ScholarPubMed
Giskeodegard, GF et al. (2015) Metabolic markers in blood can separate prostate cancer from benign prostatic hyperplasia. British Journal of Cancer 113, 1712.CrossRefGoogle ScholarPubMed
Ankerst, DP et al. (2015) A case control study of sarcosine as an early prostate cancer detection biomarker. BMC Urology 15, 14.CrossRefGoogle ScholarPubMed
Huang, J et al. (2016) Serum metabolomic profiling of prostate cancer risk in the prostate, lung, colorectal, and ovarian cancer screening trial. British Journal of Cancer 115, 10871095.CrossRefGoogle ScholarPubMed
Kumar, D et al. (2016) NMR spectroscopy of filtered serum of prostate cancer: a new frontier in metabolomics. Prostate 76, 11061119.CrossRefGoogle ScholarPubMed
Huang, J et al. (2017) Prospective serum metabolomic profile of prostate cancer by size and extent of primary tumor. Oncotarget 8, 45190.CrossRefGoogle ScholarPubMed
Schmidt, JA et al. (2017) Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European prospective investigation into cancer and nutrition. BMC Medicine 15, 122.CrossRefGoogle ScholarPubMed
Andras, I et al. (2017) Serum metabolomics can predict the outcome of first systematic trans-rectal prostate biopsy in patients with PSA<10 ng/ml. Future Oncology 13, 17931800.CrossRefGoogle Scholar
Derezinski, P et al. (2017) Amino acid profiles of serum and urine in search for prostate cancer biomarkers: a pilot study. International Journal of Medical Sciences 14, 112.CrossRefGoogle ScholarPubMed
Khan, A et al. (2019) Noninvasive serum metabolomic profiling reveals elevated kynurenine pathway's metabolites in humans with prostate cancer. Journal of Proteome Research 4, 15321541.CrossRefGoogle Scholar
Huang, J et al. (2019) Pre-diagnostic serum metabolomic profiling of prostate cancer survival. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 74, 853859.CrossRefGoogle ScholarPubMed
Zheng, H et al. (2020) NMR-based metabolomics analysis identifies discriminatory metabolic disturbances in tissue and biofluid samples for progressive prostate cancer. Clinica Chimica Acta 501, 241251.CrossRefGoogle ScholarPubMed
Schmidt, JA et al. (2020) Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: a prospective study of 3057 matched case–control sets from EPIC. International Journal of Cancer 146, 720730.CrossRefGoogle ScholarPubMed
Kiebish, MA et al. (2020) Multi-omic serum biomarkers for prognosis of disease progression in prostate cancer. Journal of Translational Medicine 7, 18, 10.Google Scholar
Cebrian, AG et al. (2020) Targeted metabolomics analyses reveal specific metabolic alterations in high-grade prostate cancer patients. Journal of Proteome Research 19, 40824092.CrossRefGoogle Scholar
Penny, KL et al. (2021) Metabolomics of prostate cancer Gleason score in tumor tissue and serum. Molecular Cancer Research 19, 475484.CrossRefGoogle Scholar
Xu, H et al. (2021) Serum metabolic profiling identifies a biomarker panel for improvement of prostate cancer diagnosis. Frontiers in Oncology 11, 666320.CrossRefGoogle ScholarPubMed
Kumar, D et al. (2021) Metabolomics of prostate cancer: knock-in versus knock-out prostate. Journal of Pharmaceutical and Biomedical Analysis 205, 114333.CrossRefGoogle ScholarPubMed
Xu, B et al. (2021) Metabolomics profiling discriminates prostate cancer from benign prostatic hyperplasia within the prostate-specific antigen gray zone. Frontiers in Oncology 11, 730638.CrossRefGoogle ScholarPubMed
Nomura, DK et al. (2010) Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell 140, 4961.CrossRefGoogle ScholarPubMed
Santos, CR et al. (2012) Lipid metabolism in cancer. FEBS Journal 279, 26102623.CrossRefGoogle ScholarPubMed
Cheng, M et al. (2016) Targeting phospholipid metabolism in cancer. Frontiers in Oncology 266, 117.Google Scholar
Carracedo, A et al. (2013) Cancer metabolism: fatty acid oxidation in the limelight. Nature Reviews Cancer 13, 227232.CrossRefGoogle ScholarPubMed
Huang, C et al. (2015) Lipid metabolism, apoptosis and cancer therapy. International Journal of Molecular Sciences 16, 924949.CrossRefGoogle ScholarPubMed
Mori, N et al. (2016) The tumor microenvironment modulates choline and lipid metabolism. Frontiers in Oncology 262, 110.Google Scholar
Console, L et al. (2020) Carnitine traffic in cells. Link with cancer. Frontiers in Cell and Developmental Biology 583850, 116.Google Scholar
Tripathi, P et al. (2013) HR-MAS NMR tissue metabolomic signatures cross-validated by mass spectrometry distinguish bladder cancer from benign disease. Journal of Proteome Research 12, 35193528.CrossRefGoogle ScholarPubMed
Marchand, CR et al. (2018) A framework for development of useful metabolomic biomarkers and their effective knowledge translation. Metabolites 8, 59.CrossRefGoogle ScholarPubMed
Sreekumar, A et al. (2009) Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910914.CrossRefGoogle ScholarPubMed
McDunn, JE et al. (2013) Metabolomic signatures of aggressive prostate cancer. Prostate 73, 15471560.CrossRefGoogle ScholarPubMed
Li, C et al. (2013) Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways. Nucleic Acids Research 41, e101.CrossRefGoogle ScholarPubMed
Thysell, E et al. (2010) Metabolomic characterization of human prostate cancer bone metastases reveal increased levels of cholesterol. PLoS ONE 5, e14175.CrossRefGoogle Scholar
Cernei, N et al. (2013) Sarcosine as a potential prostate cancer biomarker – a review. International Journal of Molecular Sciences 14, 1389313908.CrossRefGoogle ScholarPubMed