代谢组学在肿瘤药物靶点筛选中的应用进展

doi:10.13560/j.cnki.biotech.bull.1985.2021-0204

摘要/Abstract

摘要：

肿瘤是一种多因素参与造成机体各系统功能平衡紊乱的代谢性疾病,代谢重编程是恶性肿瘤的重要特征之一。研究“代谢指纹图谱”的代谢组学,通过揭示肿瘤或药物引起的宿主内源性代谢物的变化,为肿瘤药物靶点的筛选提供了可能。但目前对代谢组在肿瘤药物靶点筛选中的整体性综述并不多见,因此,本文在介绍了代谢组学筛选肿瘤药物靶点的流程的基础上,然后依次对代谢组学在糖代谢、氨基酸代谢、脂质代谢等能量领域中肿瘤药物靶点筛选及其在揭示肿瘤耐药机制和靶向药物筛选中的应用进行了阐述,最后对代谢组学在肿瘤药物靶点研究中存在的问题以及未来发展趋势进行了探讨,以期为深入研究理解代谢组学在肿瘤机制和药物靶点发现中的重要作用提供参考和科学依据。

关键词: 代谢组学, 肿瘤, 代谢重编程, 药物靶点

Abstract:

Tumor is a metabolic disease that is involved in various factors and causes the disorder of the function balance of various systems of the body. Metabolic reprogramming is an important sign of malignant tumor. Metabolomics of studying “metabolic fingerprint” can be used to mine the key metabolites related to tumor and their metabolic reprogramming pathways,and is widely used in the screening of tumor target drugs. However,there are few overall reviews on metabonomics in tumor drug target screening. This paper firstly introduces the process of metabolomics screening tumor drug targets,then elaborates the metabolomics in the screening of tumor drug targets in the fields of glucose metabolism,amino acid metabolism,lipid metabolism and other fields,revealing tumor drug-resistant mechanism,and in the application of screening target drugs. Finally the paper discusses the existing issues and future development trend of metabolomics in the research of tumor drug targets,aiming to provide reference and scientific basis for further studying and understanding the important role of metabolomics in tumor pathogenesis and drug target uncovering.

Key words: metabolomics, tumor, metabolic reprogramming, drug targets

吴玉苹, 周勇, 蒲娟, 李会, 章金刚, 朱艳平. 代谢组学在肿瘤药物靶点筛选中的应用进展[J]. 生物技术通报, 2022, 38(1): 311-318.

WU Yu-ping, ZHOU Yong, PU Juan, LI Hui, ZHANG Jin-gang, ZHU Yan-ping. Application Progress of Metabolomics in Tumor Drug Target Screening[J]. Biotechnology Bulletin, 2022, 38(1): 311-318.

图/表 2

参考文献 45

[1]	Tweeddale H, Notley-McRobb L, Ferenci T. Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool(“metabolome”)analysis[J]. J Bacteriol, 1998, 180(19):5109-5116. pmid: 9748443
[2]	Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’:understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data[J]. Xenobiotica, 1999, 29(11):1181-1189. pmid: 10598751
[3]	Faubert B, Solmonson A, DeBerardinis RJ. Metabolic reprogramming and cancer progression[J]. Science, 2020, 368(6487):eaaw5473.
[4]	Zhang AH, Sun H, Yan GL, et al. Metabolomics study of type 2 diabetes using ultra-performance LC-ESI/quadrupole-TOF high-definition MS coupled with pattern recognition methods[J]. J Physiol Biochem, 2014, 70(1):117-128.
[5]	Armitage EG, Barbas C. Metabolomics in cancer biomarker discovery:current trends and future perspectives[J]. J Pharm Biomed Anal, 2014, 87:1-11. doi: 10.1016/j.jpba.2013.08.041 pmid: 24091079
[6]	Fillet M, Frédérich M. The emergence of metabolomics as a key discipline in the drug discovery process[J]. Drug Discov Today Technol, 2015, 13:19-24. doi: 10.1016/j.ddtec.2015.01.006 URL
[7]	Patti GJ, Yanes O, Siuzdak G. Innovation:Metabolomics:the apogee of the omics trilogy[J]. Nat Rev Mol Cell Biol, 2012, 13(4):263-269. doi: 10.1038/nrm3314 URL
[8]	Pandey R, Caflisch L, Lodi A, et al. Metabolomic signature of brain cancer[J]. Mol Carcinog, 2017, 56(11):2355-2371. doi: 10.1002/mc.22694 URL
[9]	Lee JY. Book Review:Cancer as a metabolic disease:on the origin, management, and prevention of cancer[J]. J Gynecol Oncol, 2014, 25(3):260. doi: 10.3802/jgo.2014.25.3.260 URL
[10]	Wang X, Liu R, Zhu W, et al. UDP-glucose accelerates SNAI1 mRNA decay and impairs lung cancer metastasis[J]. Nature, 2019, 571(7763):127-131. doi: 10.1038/s41586-019-1340-y URL
[11]	Satoh K, Yachida S, Sugimoto M, et al. Global metabolic reprogramming of colorectal cancer occurs at adenoma stage and is induced by MYC[J]. PNAS, 2017, 114(37):E7697-E7706. doi: 10.1073/pnas.1710366114 URL
[12]	Frédérich M, Pirotte B, Fillet M, et al. Metabolomics as a challenging approach for medicinal chemistry and personalized medicine[J]. J Med Chem, 2016, 59(19):8649-8666. pmid: 27295417
[13]	Lu J, Tan M, Cai Q. The Warburg effect in tumor progression:mitochondrial oxidative metabolism as an anti-metastasis mechanism[J]. Cancer Lett, 2015, 356(2 pt a):156-164. doi: 10.1016/j.canlet.2014.04.001 URL
[14]	Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology:a review[J]. Clin Cancer Res, 2009, 15(2):431-440. doi: 10.1158/1078-0432.CCR-08-1059 URL
[15]	Yumba Mpanga A, Siluk D, Jacyna J, et al. Targeted metabolomics in bladder cancer:From analytical methods development and validation towards application to clinical samples[J]. Anal Chim Acta, 2018, 1037:188-199. doi: 10.1016/j.aca.2018.01.055 URL
[16]	Wang ZZ, Cui BB, Zhang F, et al. Development of a correlative strategy to discover colorectal tumor tissue derived metabolite biomarkers in plasma using untargeted metabolomics[J]. Anal Chem, 2019, 91(3):2401-2408. doi: 10.1021/acs.analchem.8b05177 URL
[17]	Chen X, Yu DS. Metabolomics study of oral cancers[J]. Metabolomics, 2019, 15(2):22. doi: 10.1007/s11306-019-1483-8 pmid: 30830419
[18]	Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine[J]. Nat Rev Drug Discov, 2016, 15(7):473-484. doi: 10.1038/nrd.2016.32 pmid: 26965202
[19]	Sukumar M, Roychoudhuri R, Restifo NP. Nutrient competition:a new axis of tumor immunosuppression[J]. Cell, 2015, 162(6):1206-1208. doi: 10.1016/j.cell.2015.08.064 URL
[20]	Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect:the metabolic requirements of cell proliferation[J]. Science, 2009, 324(5930):1029-1033. doi: 10.1126/science.1160809 URL
[21]	Yuan M, Breitkopf SB, Yang X, et al. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue[J]. Nat Protoc, 2012, 7(5):872-881. doi: 10.1038/nprot.2012.024 pmid: 22498707
[22]	Bhanot H, Reddy MM, Nonami A, et al. Pathological glycogenesis through glycogen synthase 1 and suppression of excessive AMP kinase activity in myeloid leukemia cells[J]. Leukemia, 2015, 29(7):1555-1563. doi: 10.1038/leu.2015.46 URL
[23]	Gao P, Yang C, Nesvick CL, et al. Hypotaurine evokes a malignant phenotype in glioma through aberrant hypoxic signaling[J]. Oncotarget, 2016, 7(12):15200-15214. doi: 10.18632/oncotarget.v7i12 URL
[24]	Zhu B, Wei H, Wang Q, et al. A simultaneously quantitative method to profiling twenty endogenous nucleosides and nucleotides in cancer cells using UHPLC-MS/MS[J]. Talanta, 2018, 179:615-623. doi: 10.1016/j.talanta.2017.11.054 URL
[25]	Gao X, Sanderson SM, Dai Z, et al. Dietary methionine influences therapy in mouse cancer models and alters human metabolism[J]. Nature, 2019, 572(7769):397-401. doi: 10.1038/s41586-019-1437-3 URL
[26]	Eaton JK, Furst L, Ruberto RA, et al. Selective covalent targeting of GPX4 using masked nitrile-oxide electrophiles[J]. Nat Chem Biol, 2020, 16(5):497-506. doi: 10.1038/s41589-020-0501-5 pmid: 32231343
[27]	Zhang W, Liu W, Jia L, et al. Targeting KDM4A epigenetically activates tumor-cell-intrinsic immunity by inducing DNA replication stress[J]. Mol Cell, 2021, 81(10):2148-2165. e9.
[28]	Visweswaran M, Arfuso F, Warrier S, et al. Aberrant lipid metabolism as an emerging therapeutic strategy to target cancer stem cells[J]. Stem Cells, 2020, 38(1):6-14. doi: 10.1002/stem.3101 pmid: 31648395
[29]	Stoykova GE, Schlaepfer IR. Lipid metabolism and endocrine resistance in prostate cancer, and new opportunities for therapy[J]. Int J Mol Sci, 2019, 20(11):2626. doi: 10.3390/ijms20112626 URL
[30]	Camarda R, Zhou AY, Kohnz RA, et al. Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer[J]. Nat Med, 2016, 22(4):427-432. doi: 10.1038/nm.4055 pmid: 26950360
[31]	Zadra G, Ribeiro CF, Chetta P, et al. Inhibition of de novo lipogenesis targets androgen receptor signaling in castration-resistant prostate cancer[J]. PNAS, 2019, 116(2):631-640. doi: 10.1073/pnas.1808834116 URL
[32]	Kimmelman AC, White E. Autophagy and tumor metabolism[J]. Cell Metab, 2017, 25(5):1037-1043. doi: S1550-4131(17)30209-7 pmid: 28467923
[33]	Cheong H. Integrating autophagy and metabolism in cancer[J]. Arch Pharm Res, 2015, 38(3):358-371. doi: 10.1007/s12272-015-0562-2 URL
[34]	Boroughs LK, DeBerardinis RJ. Metabolic pathways promoting cancer cell survival and growth[J]. Nat Cell Biol, 2015, 17(4):351-359. doi: 10.1038/ncb3124 pmid: 25774832
[35]	Lu SY, Lu RG, Song H, et al. Metabolomic study of natrin-induced apoptosis in SMMC-7721 hepatocellular carcinoma cells by ultra-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry[J]. Int J Biol Macromol, 2019, 124:1264-1273. doi: 10.1016/j.ijbiomac.2018.11.060 URL
[36]	Cao B, Li M, Zha W, et al. Metabolomic approach to evaluating adriamycin pharmacodynamics and resistance in breast cancer cells[J]. Metabolomics, 2013, 9(5):960-973. doi: 10.1007/s11306-013-0517-x URL
[37]	Wang TY, Fahrmann JF, Lee H, et al. JAK/STAT3-regulated fatty acid β-oxidation is critical for breast cancer stem cell self-renewal and chemoresistance[J]. Cell Metab, 2018, 27(6):1357.
[38]	Sun H, Zhang AH, Liu SB, et al. Cell metabolomics identify regulatory pathways and targets of magnoline against prostate cancer[J]. J Chromatogr B Analyt Technol Biomed Life Sci, 2018, 1102/1103:143-151. doi: 10.1016/j.jchromb.2018.10.017 URL
[39]	Ma C, Li Y, Wu H, et al. Metabolomics analysis of the potential anticancer mechanism of annonaceous acetogenins on a multidrug resistant mammary adenocarcinoma cell[J]. Anal Biochem, 2018, 553:1-6. doi: 10.1016/j.ab.2018.04.022 URL
[40]	Jaiswal S, Kumar M, Mandeep, et al. Systems biology approaches for therapeutics development against COVID-19[J]. Front Cell Infect Microbiol, 2020, 10:560240. doi: 10.3389/fcimb.2020.560240 URL
[41]	Misra BB, Langefeld C, Olivier M, et al. Integrated omics:tools, advances and future approaches[J]. J Mol Endocrinol, 2019, 62(1):R21-R45. doi: 10.1530/JME-18-0055 URL
[42]	Vazquez A, Kamphorst JJ, Markert EK, et al. Cancer metabolism at a glance[J]. J Cell Sci, 2016, 129(18):3367-3373. doi: 10.1242/jcs.181016 pmid: 27635066
[43]	Vitale I, Manic G, Coussens LM, et al. Macrophages and metabolism in the tumor microenvironment[J]. Cell Metab, 2019, 30(1):36-50. doi: S1550-4131(19)30304-3 pmid: 31269428
[44]	Meads MB, Gatenby RA, Dalton WS. Environment-mediated drug resistance:a major contributor to minimal residual disease[J]. Nat Rev Cancer, 2009, 9(9):665-674. doi: 10.1038/nrc2714 pmid: 19693095
[45]	Li X, Wenes M, Romero P, et al. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy[J]. Nat Rev Clin Oncol, 2019, 16(7):425-441. doi: 10.1038/s41571-019-0203-7 URL