Metabolomics: A Tool to Envisage Biomarkers in Clinical Interpretation
of Cancer

Medha      Bhalla; Roopal      Mittal; Manish      Kumar; Rohit      Bhatia; Ajay   Singh   Kushwah
doi:10.2174/2589977516666230912120412
Abstract

Background: Cancer is amongst the most dreadful ailments of modern times, and its impact continuously worsens global health systems. Early diagnosis and suitable therapeutic agents are the prime keys to managing this disease. Metabolomics deals with the complete profiling of cells and physiological phenomena in their organelles, thus helping in keen knowledge of the pathological status of the disease. It has been proven to be one of the best strategies in the early screening of cancer.
Objective: This review has covered the recent updates on the promising role of metabolomics in the identification of significant biochemical markers in cancer-prone individuals that could lead to the identification of cancer in the early stages.
Methods: The literature was collected through various databases, like Scopus, PubMed, and Google Scholar, with stress laid on the last ten years' publications.
Conclusion: It was assessed in this review that early recognition of cancerous growth could be achieved via complete metabolic profiling in association with transcriptomics and proteomics. The outcomes are rooted in various clinical studies that anticipated various biomarkers like tryptophan, phenylalanine, lactates, and different metabolic pathways associated with the Warburg effect. This metabolite imaging has been a fundamental step for the target acquisition, evaluation of predictive cancer biomarkers for early detection, and outlooks into cancer therapy along with critical evaluation. Significant efforts should be made to make this technique most reliable and easy.
Keywords: Biofluids, biomarkers, cancer, clinical pharmacology, metabolomics, metabolic pathways, clinical trials.
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[1]
Woo JH, Shimoni Y, Yang WS, et al. Elucidating compound mechanism of action by network perturbation analysis. Cell  2015; 162(2): 441-51.
 [http://dx.doi.org/10.1016/j.cell.2015.05.056] [PMID:  26186195]

[2]
Vladimer GI, Snijder B, Krall N, et al. Global survey of the immunomodulatory potential of common drugs. Nat Chem Biol  2017; 13(6): 681-90.
 [http://dx.doi.org/10.1038/nchembio.2360] [PMID:  28437395]

[3]
Kang J, Hsu CH, Wu Q, et al. Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines. Nat Biotechnol  2016; 34(1): 70-7.
 [http://dx.doi.org/10.1038/nbt.3419] [PMID:  26655497]

[4]
Liu G, Wang X, Fan X, Luo X. Metabolomics profiles in acute-on-chronic liver failure: Unveiling pathogenesis and predicting progression. Front Pharmacol  2022; 13: 953297.
 [http://dx.doi.org/10.3389/fphar.2022.953297] [PMID:  36059949]

[5]
Cardenas LM, Deluce JE, Khan S, et al. Next wave of targets in the treatment of advanced renal cell carcinoma. Curr Oncol  2022; 29(8): 5426-41.
 [http://dx.doi.org/10.3390/curroncol29080429] [PMID:  36005167]

[6]
Gamazon ER, Skol AD, Perera MA. The limits of genome-wide methods for pharmacogenomic testing. Pharmacogenet Genomics  2012; 22(4): 261-72.
 [http://dx.doi.org/10.1097/FPC.0b013e328350ca5f] [PMID:  22344246]

[7]
Forbes SA, Beare D, Gunasekaran P, et al. COSMIC: Exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res  2015; 43(D1): D805-11.
 [http://dx.doi.org/10.1093/nar/gku1075] [PMID:  25355519]

[8]
Aboud OA, Weiss RH. New opportunities from the cancer metabolome. Clin Chem  2013; 59(1): 138-46.
 [http://dx.doi.org/10.1373/clinchem.2012.184598] [PMID:  23150057]

[9]
Al-Sahlany STG, Niamah AK. Bacterial viability, antioxidant stability, antimutagenicity and sensory properties of onion types fermentation by using probiotic starter during storage. Nutr Food Sci  2022; 52(6): 901-16.
 [http://dx.doi.org/10.1108/NFS-07-2021-0204]

[10]
Liu W, Le A, Hancock C, et al. Reprogramming of proline and glutamine metabolism contributes to the proliferative and metabolic responses regulated by oncogenic transcription factor c-MYC. Proc Natl Acad Sci  2012; 109(23): 8983-8.
 [http://dx.doi.org/10.1073/pnas.1203244109] [PMID:  22615405]

[11]
Mussai F, Egan S, Higginbotham-Jones J, et al. Arginine dependence of acute myeloid leukemia blast proliferation: A novel therapeutic target. Blood  2015; 125(15): 2386-96.
 [http://dx.doi.org/10.1182/blood-2014-09-600643] [PMID:  25710880]

[12]
Hipp SJ, Steffen-Smith EA, Patronas N, et al. Molecular imaging of pediatric brain tumors: comparison of tumor metabolism using 18F-FDG-PET and MRSI. J Neurooncol  2012; 109(3): 521-7.
 [http://dx.doi.org/10.1007/s11060-012-0918-0] [PMID:  22760419]

[13]
Zhan H, Ciano K, Dong K, Zucker S. Targeting glutamine metabolism in myeloproliferative neoplasms. Blood Cells Mol Dis  2015; 55(3): 241-7.
 [http://dx.doi.org/10.1016/j.bcmd.2015.07.007] [PMID:  26227854]

[14]
Giovacchini G, Guglielmo P, Mapelli P, et al. 11C-choline PET/CT predicts survival in prostate cancer patients with PSA < 1 NG/ml. Eur J Nucl Med Mol Imaging  2019; 46(4): 921-9.
 [http://dx.doi.org/10.1007/s00259-018-4253-3] [PMID:  30631911]

[15]
Goering AW, McClure RA, Doroghazi JR, et al. Metabologenomics: Correlation of microbial gene clusters with metabolites drives discovery of a nonribosomal peptide with an unusual amino acid monomer. ACS Cent Sci  2016; 2(2): 99-108.
 [http://dx.doi.org/10.1021/acscentsci.5b00331] [PMID:  27163034]

[16]
Liu X, Locasale JW. Metabolomics: A primer. Trends Biochem Sci  2017; 42(4): 274-84.
 [http://dx.doi.org/10.1016/j.tibs.2017.01.004] [PMID:  28196646]

[17]
Xu T, Feng G, Zhao B, et al. A non-target urinary and serum metabolomics strategy reveals therapeutical mechanism of radix astragali on adjuvant-induced arthritis rats. J Chromatogr B Analyt Technol Biomed Life Sci  2017; 1048: 94-101.
 [http://dx.doi.org/10.1016/j.jchromb.2017.01.040] [PMID:  28232289]

[18]
Chen WL, Wang YY, Zhao A, et al. Enhanced fructose utilization mediated by SLC2A5 Is a unique metabolic feature of acute myeloid leukemia with therapeutic potential. Cancer Cell  2016; 30(5): 779-91.
 [http://dx.doi.org/10.1016/j.ccell.2016.09.006] [PMID:  27746145]

[19]
West JA, Beqqali A, Ament Z, et al. A targeted metabolomics assay for cardiac metabolism and demonstration using a mouse model of dilated cardiomyopathy. Metabolomics  2016; 12(3): 59.
 [http://dx.doi.org/10.1007/s11306-016-0956-2] [PMID:  27069442]

[20]
Kohler I, Verhoeven A, Derks RJE, Giera M. Analytical pitfalls and challenges in clinical metabolomics. Bioanalysis  2016; 8(14): 1509-32.
 [http://dx.doi.org/10.4155/bio-2016-0090] [PMID:  27323646]

[21]
Baquer G, Sementé L, Mahamdi T, Correig X, Ràfols P, García-Altares M. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging. Mass Spectrom Rev  2022; e21794: e21794.
 [http://dx.doi.org/10.1002/mas.21794] [PMID: 35822576]

[22]
Dunn WB, Wilson ID, Nicholls AW, Broadhurst D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis  2012; 4(18): 2249-64.
 [http://dx.doi.org/10.4155/bio.12.204] [PMID:  23046267]

[23]
Rey-Stolle F, Dudzik D, Gonzalez-Riano C, et al. Low and high resolution gas chromatography-mass spectrometry for untargeted metabolomics: A tutorial. Anal Chim Acta  2022; 1210: 339043.
 [http://dx.doi.org/10.1016/j.aca.2021.339043] [PMID:  35595356]

[24]
O'Sullivan A, Avizonis D, German JB, Slupsky CM. Software tools for NMR metabolomics.eMagRes. Wiley Online Library 2011.
 [http://dx.doi.org/10.1002/9780470034590.emrstm1232]

[25]
Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M. Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinform  2012; 7(1): 96-108.
 [http://dx.doi.org/10.2174/157489312799304431] [PMID:  22438836]

[26]
Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun  2022; 58(72): 9979-90.
 [http://dx.doi.org/10.1039/D2CC03598G] [PMID:  35997016]

[27]
Wang X, Dong Y, Zheng Y, Chen Y. Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective. J Genet Genomics  2021; 48(7): 520-30.
 [http://dx.doi.org/10.1016/j.jgg.2021.05.008] [PMID:  34362682]

[28]
Panina SB, Pei J, Kirienko NV. Mitochondrial metabolism as a target for acute myeloid leukemia treatment. Cancer Metab  2021; 9(1): 17.
 [http://dx.doi.org/10.1186/s40170-021-00253-w] [PMID:  33883040]

[29]
Kiesel VA, Sheeley MP, Coleman MF, et al. Pyruvate carboxylase and cancer progression. Cancer Metab  2021; 9(1): 20.
 [http://dx.doi.org/10.1186/s40170-021-00256-7] [PMID:  33931119]

[30]
Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin  2021; 71(4): 333-58.
 [http://dx.doi.org/10.3322/caac.21670] [PMID:  33982817]

[31]
Xiao Y, Ma D, Yang YS, et al. Comprehensive metabolomics expands precision medicine for triple-negative breast cancer. Cell Res  2022; 32(5): 477-90.
 [http://dx.doi.org/10.1038/s41422-022-00614-0] [PMID:  35105939]

[32]
Carracedo A, Cantley LC, Pandolfi PP. Cancer metabolism: Fatty acid oxidation in the limelight. Nat Rev Cancer  2013; 13(4): 227-32.
 [http://dx.doi.org/10.1038/nrc3483] [PMID:  23446547]

[33]
Wu H, Ganguly S, Tollefsbol TO. Modulating microbiota as a new strategy for breast cancer prevention and treatment. Microorganisms  2022; 10(9): 1727.
 [http://dx.doi.org/10.3390/microorganisms10091727] [PMID:  36144329]

[34]
Wermke M, Felip E, Gambardella V, et al. Phase I trial of the DLL3/CD3 bispecific T-cell engager BI 764532 in DLL3-positive small-cell lung cancer and neuroendocrine carcinomas. Future Oncol  2022; 18(24): 2639-49.
 [http://dx.doi.org/10.2217/fon-2022-0196] [PMID:  35815644]

[35]
Cozma A, Sporis ND, Lazar AL, et al. Cardiac toxicity associated with immune checkpoint inhibitors: A systematic review. Int J Mol Sci  2022; 23(18): 10948.
 [http://dx.doi.org/10.3390/ijms231810948] [PMID:  36142866]

[36]
Zhang A, Sun H, Yan G, Han Y, Ye Y, Wang X. Urinary metabolic profiling identifies a key role for glycocholic acid in human liver cancer by ultra-performance liquid-chromatography coupled with high-definition mass spectrometry. Clin Chim Acta  2013; 418: 86-90.
 [http://dx.doi.org/10.1016/j.cca.2012.12.024] [PMID:  23313056]

[37]
Ganti S, Taylor SL, Kim K, et al. Urinary acylcarnitines are altered in human kidney cancer. Int J Cancer  2012; 130(12): 2791-800.
 [http://dx.doi.org/10.1002/ijc.26274] [PMID:  21732340]

[38]
Gao H, Dong B, Jia J, et al. Application of ex vivo 1H NMR metabonomics to the characterization and possible detection of renal cell carcinoma metastases. J Cancer Res Clin Oncol  2012; 138(5): 753-61.
 [http://dx.doi.org/10.1007/s00432-011-1134-6] [PMID:  22258851]

[39]
Klicka K, Grzywa TM, Mielniczuk A, Klinke A, Włodarski PK. The role of miR-200 family in the regulation of hallmarks of cancer. Front Oncol  2022; 12: 965231.
 [http://dx.doi.org/10.3389/fonc.2022.965231] [PMID:  36158660]

[40]
Tse GM, Yeung DKY, King AD, Cheung HS, Yang WT. In vivo proton magnetic resonance spectroscopy of breast lesions: An update. Breast Cancer Res Treat  2007; 104(3): 249-55.
 [http://dx.doi.org/10.1007/s10549-006-9412-8] [PMID:  17051424]

[41]
Zhang T, Wu X, Ke C, et al. Identification of potential biomarkers for ovarian cancer by urinary metabolomic profiling. J Proteome Res  2013; 12(1): 505-12.
 [http://dx.doi.org/10.1021/pr3009572] [PMID:  23163809]

[42]
Yue H, Wang Y, Zhang Y, et al. A metabonomics study of colorectal cancer by RRLC-QTOF/MS. J Liq Chromatogr Relat Technol  2013; 36(4): 428-38.
 [http://dx.doi.org/10.1080/10826076.2012.657738]

[43]
Nishiumi S, Kobayashi T, Ikeda A, et al. A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS One  2012; 7(7): e40459.
 [http://dx.doi.org/10.1371/journal.pone.0040459] [PMID:  22792336]

[44]
Tagde P, Tagde P, Tagde S, et al. Natural bioactive molecules: An alternative approach to the treatment and control of glioblastoma multiforme. Biomed Pharmacother  2021; 141: 111928.
 [http://dx.doi.org/10.1016/j.biopha.2021.111928] [PMID:  34323701]

[45]
Pi M, Kuang H, Yue C, et al. Targeting metabolism to overcome cancer drug resistance: A promising therapeutic strategy for diffuse large B cell lymphoma. Drug Resist Updat  2022; 61: 100822.
 [http://dx.doi.org/10.1016/j.drup.2022.100822] [PMID:  35257981]

[46]
Matés JM, Campos-Sandoval JA, Santos-Jiménez J, Márquez J. Dysregulation of glutaminase and glutamine synthetase in cancer. Cancer Lett  2019; 467: 29-39.
 [http://dx.doi.org/10.1016/j.canlet.2019.09.011] [PMID:  31574293]

[47]
Bedi M, Ray M, Ghosh A. Active mitochondrial respiration in cancer: A target for the drug. Mol Cell Biochem  2022; 477(2): 345-61.
 [http://dx.doi.org/10.1007/s11010-021-04281-4] [PMID:  34716860]

[48]
Ciliberto G, Mancini R, Paggi MG. Drug repurposing against COVID-19: Focus on anticancer agents. J Exp Clin Cancer Res  2020; 39(1): 86.
 [http://dx.doi.org/10.1186/s13046-020-01590-2] [PMID:  32398164]

[49]
Moreno Ontalba A, Gómez Durán Á, Ruiz Cabezas L, Cidoncha Pérez E, Díaz Delgado M, Rubio Fernández A. Cystic tubulopapillary adenoma with apocrine differentiation and BRAF V600E mutation. A case report and review of the literature. Rev Esp Patol  2022; 55(1): S64-8.
 [http://dx.doi.org/10.1016/j.patol.2020.02.004] [PMID:  36075666]

[50]
Corona G, Rizzolio F, Giordano A, Toffoli G. Pharmaco-metabolomics: An emerging “omics” tool for the personalization of anticancer treatments and identification of new valuable therapeutic targets. J Cell Physiol  2012; 227(7): 2827-31.
 [http://dx.doi.org/10.1002/jcp.24003] [PMID:  22105661]

[51]
Khamis MM, Adamko DJ, El-Aneed A. Mass spectrometric based approaches in urine metabolomics and biomarker discovery. Mass Spectrom Rev  2017; 36(2): 115-34.
 [http://dx.doi.org/10.1002/mas.21455] [PMID:  25881008]

[52]
Kuligowski J, Sanjuan-Herráez D, Vázquez-Sánchez MA, et al. Metabolomic analysis of gastric cancer progression within the correa’s cascade using ultraperformance liquid chromatography–mass spectrometry. J Proteome Res  2016; 15(8): 2729-38.
 [http://dx.doi.org/10.1021/acs.jproteome.6b00281] [PMID:  27384260]

[53]
Peng J, Chen YT, Chen CL, Li L. Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite-biomarker discovery. Anal Chem  2014; 86(13): 6540-7.
 [http://dx.doi.org/10.1021/ac5011684] [PMID:  24877652]

[54]
Yang QJ, Zhao JR, Hao J, et al. Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia. J Cache Sarcop Mus  2018; 9(1): 71-85.
 [http://dx.doi.org/10.1002/jcsm.12246] [PMID:  29152916]

[55]
Mayerle J, Kalthoff H, Reszka R, et al. Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis. Gut  2018; 67(1): 128-37.
 [http://dx.doi.org/10.1136/gutjnl-2016-312432] [PMID:  28108468]

[56]
Li Y, Song X, Zhao X, Zou L, Xu G. Serum metabolic profiling study of lung cancer using ultra high performance liquid chromatography/quadrupole time-of-flight mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci  2014; 966: 147-53.
 [http://dx.doi.org/10.1016/j.jchromb.2014.04.047] [PMID:  24856296]

[57]
Puchades-Carrasco L, Lecumberri R, Martínez-López J, et al. Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission. Clin Cancer Res  2013; 19(17): 4770-9.
 [http://dx.doi.org/10.1158/1078-0432.CCR-12-2917] [PMID:  23873687]

[58]
Luo P, Yin P, Hua R, et al. A Large‐scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma. Hepatology  2018; 67(2): 662-75.
 [http://dx.doi.org/10.1002/hep.29561] [PMID:  28960374]

[59]
Choi C, Ganji SK, DeBerardinis RJ, et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med  2012; 18(4): 624-9.
 [http://dx.doi.org/10.1038/nm.2682] [PMID:  22281806]

[60]
Morad SAF, Cabot MC. Ceramide-orchestrated signalling in cancer cells. Nat Rev Cancer  2013; 13(1): 51-65.
 [http://dx.doi.org/10.1038/nrc3398] [PMID:  23235911]

[61]
Morad SAF, Messner MC, Levin JC, et al. Potential role of acid ceramidase in conversion of cytostatic to cytotoxic end-point in pancreatic cancer cells. Cancer Chemother Pharmacol  2013; 71(3): 635-45.
 [http://dx.doi.org/10.1007/s00280-012-2050-4] [PMID:  23263160]

[62]
Manna SK, Tanaka N, Krausz KW, et al. Biomarkers of coordinate metabolic reprogramming in colorectal tumors in mice and humans. Gastroenterology  2014; 146(5): 1313-24.
 [http://dx.doi.org/10.1053/j.gastro.2014.01.017] [PMID:  24440673]

[63]
Koutros S, Meyer TE, Fox SD, et al. Prospective evaluation of serum sarcosine and risk of prostate cancer in the prostate, lung, colorectal and ovarian cancer screening trial. Carcinogenesis  2013; 34(10): 2281-5.
 [http://dx.doi.org/10.1093/carcin/bgt176] [PMID:  23698636]

[64]
Plewa S, Horała A, Dereziński P, et al. Usefulness of amino acid profiling in ovarian cancer screening with special emphasis on their role in cancerogenesis. Int J Mol Sci  2017; 18(12): 2727.
 [http://dx.doi.org/10.3390/ijms18122727] [PMID:  29258187]

[65]
Tabe Y, Konopleva M. Advances in understanding the leukaemia microenvironment. Br J Haematol  2014; 164(6): 767-78.
 [http://dx.doi.org/10.1111/bjh.12725] [PMID:  24405087]

[66]
Poisson LM, Munkarah A, Madi H, et al. A metabolomic approach to identifying platinum resistance in ovarian cancer. J Ovarian Res  2015; 8(1): 13.
 [http://dx.doi.org/10.1186/s13048-015-0140-8] [PMID:  25880539]

[67]
Yang M, Soga T, Pollard PJ. Oncometabolites: Linking altered metabolism with cancer. J Clin Invest  2013; 123(9): 3652-8.
 [http://dx.doi.org/10.1172/JCI67228] [PMID:  23999438]

[68]
Zabala-Letona A, Arruabarrena-Aristorena A, Martín-Martín N, et al. mTORC1-dependent AMD1 regulation sustains polyamine metabolism in prostate cancer. Nature  2017; 547(7661): 109-13.
 [http://dx.doi.org/10.1038/nature22964] [PMID:  28658205]

[69]
Fan X, Rao J, Zhang Z, et al. Macranthoidin B modulates key metabolic pathways to enhance ros generation and induce cytotoxicity and apoptosis in colorectal cancer. Cell Physiol Biochem  2018; 46(4): 1317-30.
 [http://dx.doi.org/10.1159/000489147] [PMID:  29689551]

[70]
Huang Q, Aa J, Jia H, et al. A pharmacometabonomic approach to predicting metabolic phenotypes and pharmacokinetic parameters of atorvastatin in healthy volunteers. J Proteome Res  2015; 14(9): 3970-81.
 [http://dx.doi.org/10.1021/acs.jproteome.5b00440] [PMID:  26216528]

[71]
Wang DR, Wu XL, Sun YL. Therapeutic targets and biomarkers of tumor immunotherapy: Response versus non-response. Signal Transduct Target Ther  2022; 7(1): 331.
 [http://dx.doi.org/10.1038/s41392-022-01136-2] [PMID:  36123348]

[72]
Kaddurah-Daouk R, Weinshilboum RM. Pharmacometabolomics: Implications for clinical pharmacology and systems pharmacology. Clin Pharmacol Ther  2014; 95(2): 154-67.
 [http://dx.doi.org/10.1038/clpt.2013.217] [PMID:  24193171]

[73]
Huang G, Liu X, Jiao L, et al. Metabolomic evaluation of the response to endocrine therapy in patients with prostate cancer. Eur J Pharmacol  2014; 729: 132-7.
 [http://dx.doi.org/10.1016/j.ejphar.2014.01.048] [PMID:  24556387]

[74]
Bannur Z, Teh LK, Hennesy T, et al. The differential metabolite profiles of acute lymphoblastic leukaemic patients treated with 6-mercaptopurine using untargeted metabolomics approach. Clin Biochem  2014; 47(6): 427-31.
 [http://dx.doi.org/10.1016/j.clinbiochem.2014.02.013] [PMID:  24582698]

[75]
Tenori L, Oakman C, Claudino WM, et al. Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: A pilot study. Mol Oncol  2012; 6(4): 437-44.
 [http://dx.doi.org/10.1016/j.molonc.2012.05.003] [PMID:  22687601]

[76]
Liesenfeld DB, Habermann N, Owen RW, Scalbert A, Ulrich CM. Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiol Biomarkers Prev  2013; 22(12): 2182-201.
 [http://dx.doi.org/10.1158/1055-9965.EPI-13-0584] [PMID:  24096148]

[77]
Lindeman NI, Cagle PT, Beasley MB, et al. Molecular testing guideline for selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors: Guideline from the college of american pathologists, international association for the study of lung cancer, and association for molecular pathology. J Thorac Oncol  2013; 8(7): 823-59.
 [http://dx.doi.org/10.1097/JTO.0b013e318290868f] [PMID:  23552377]

[78]
Kühn T, Floegel A, Sookthai D, et al. Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Med  2016; 14(1): 13.
 [http://dx.doi.org/10.1186/s12916-016-0552-3] [PMID:  26817443]

[79]
Claus J, Patel G, Ng T, Parker PJ. A role for the pseudokinase HER3 in the acquired resistance against EGFR- and HER2-directed targeted therapy. Biochem Soc Trans  2014; 42(4): 831-6.
 [http://dx.doi.org/10.1042/BST20140043] [PMID:  25109965]

[80]
Wishart DS. Is cancer a genetic disease or a metabolic disease? EBioMedicine  2015; 2(6): 478-9.
 [http://dx.doi.org/10.1016/j.ebiom.2015.05.022] [PMID:  26288805]

[81]
Fu X, Chin RM, Vergnes L, et al. 2-Hydroxyglutarate inhibits atp synthase and mTOR signaling. Cell Metab  2015; 22(3): 508-15.
 [http://dx.doi.org/10.1016/j.cmet.2015.06.009] [PMID:  26190651]

[82]
Shanmugasundaram K, Nayak B, Shim EH, Livi CB, Block K, Sudarshan S. The oncometabolite fumarate promotes pseudohypoxia through noncanonical activation of NF-κB signaling. J Biol Chem  2014; 289(35): 24691-9.
 [http://dx.doi.org/10.1074/jbc.M114.568162] [PMID:  25028521]

[83]
Morin A, Letouzé E, Gimenez-Roqueplo AP, Favier J. Oncometabolites-driven tumorigenesis: From genetics to targeted therapy. Int J Cancer  2014; 135(10): 2237-48.
 [http://dx.doi.org/10.1002/ijc.29080] [PMID:  25124653]

[84]
Yang M, Soga T, Pollard PJ, Adam J. The emerging role of fumarate as an oncometabolite. Front Oncol  2012; 2: 85.
 [http://dx.doi.org/10.3389/fonc.2012.00085] [PMID:  22866264]

[85]
Chen KT, Tsai MH, Wu CH, Jou MJ, Wei IH, Huang CC. AMPA Receptor–mTOR activation is required for the antidepressant-like effects of sarcosine during the forced swim test in rats: Insertion of AMPA receptor may play a role. Front Behav Neurosci  2015; 9: 162.
 [http://dx.doi.org/10.3389/fnbeh.2015.00162] [PMID:  26150775]

[86]
Khan AP, Rajendiran TM, Bushra A, et al. The role of sarcosine metabolism in prostate cancer progression. Neoplasia  2013; 15(5): 491-IN13.
 [http://dx.doi.org/10.1593/neo.13314] [PMID:  23633921]

[87]
Mullard A. New drugs cost US$2.6 billion to develop. Nat Rev Drug Dis  2014; 13: 877.
 [http://dx.doi.org/10.1038/nrd4507]

[88]
Minetti CA, Remeta DP. Forces driving a magic bullet to its target: Revisiting the role of thermodynamics in drug design, development, and optimization. Life  2022; 12(9): 1438.
 [http://dx.doi.org/10.3390/life12091438] [PMID:  36143474]

[89]
Kontostathi G, Zoidakis J, Anagnou NP, Pappa KI, Vlahou A, Makridakis M. Proteomics approaches in cervical cancer: Focus on the discovery of biomarkers for diagnosis and drug treatment monitoring. Expert Rev Proteomics  2016; 13(8): 731-45.
 [http://dx.doi.org/10.1080/14789450.2016.1210514] [PMID:  27398979]

[90]
Nishiumi S, Fujigaki S, Kobayashi T, et al. Metabolomics-based discovery of serum biomarkers to predict the side-effects of neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma. Anticancer Res  2019; 39(1): 519-26.
 [http://dx.doi.org/10.21873/anticanres.13143] [PMID:  30591504]

[91]
Ferrara M, Samaden M, Ruggieri E, Vénéreau E. Cancer cachexia as a multiorgan failure: Reconstruction of the crime scene. Front Cell Dev Biol  2022; 10: 960341.
 [http://dx.doi.org/10.3389/fcell.2022.960341] [PMID:  36158184]

[92]
Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov  2016; 15(7): 473-84.
 [http://dx.doi.org/10.1038/nrd.2016.32] [PMID:  26965202]

[93]
Krauss RM, Zhu H, Kaddurah-Daouk R. Pharmacometabolomics of statin response. Clin Pharmacol Ther  2013; 94(5): 562-5.
 [http://dx.doi.org/10.1038/clpt.2013.164] [PMID:  23945822]

[94]
Wikoff WR, Frye RF, Zhu H, et al. Pharmacometabolomics reveals racial differences in response to atenolol treatment. PLoS One  2013; 8(3): e57639.
 [http://dx.doi.org/10.1371/journal.pone.0057639] [PMID:  23536766]

[95]
Ellero-Simatos S, Lewis JP, Georgiades A, et al. Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT Pharmacometrics Syst Pharmacol  2014; 3(7): 125.
 [http://dx.doi.org/10.1038/psp.2014.22] [PMID:  25029353]

[96]
Kim B, Lee JW, Hong KT, et al. Pharmacometabolomics for predicting variable busulfan exposure in paediatric haematopoietic stem cell transplantation patients. Sci Rep  2017; 7(1): 1711.
 [http://dx.doi.org/10.1038/s41598-017-01861-7] [PMID:  28490733]
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DOI https://dx.doi.org/10.2174/2589977516666230912120412	Print ISSN 2589-9775
Publisher Name Bentham Science Publisher	Online ISSN 2589-9783
Current Drug Research Reviews

Metabolomics: A Tool to Envisage Biomarkers in Clinical Interpretation of Cancer

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