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胆管癌(cholangiocarcinoma, CCA)是胆道系统一组致死率极高的上皮细胞恶性肿瘤,按照解剖学定位,可分为肝内胆管癌(ICC)和肝外胆管癌(ECC), 后者又分为肝门部胆管癌(PCC)和远端胆管癌(DCC)[1]。胆管癌在原发性肝癌中占15%左右,仅次于肝细胞癌(HCC),我国胆管癌的发病率约为7.5/10万,而泰国东北部的发病率是我国的11倍[2-3]。全球胆管癌的死亡率为1~6/10万,并在全球范围内有上升之势,严重威胁着人民的生命健康[4]。
手术切除是唯一可能治愈胆管癌的方法,但因早期胆管癌症状不典型,绝大多数患者(60%~70%)在诊断时已经处于疾病中晚期,错过了肝病治愈性切除的最佳时机[5-6]。同时,胆管癌对放化疗不敏感且存在耐药现象,胆管癌的预后非常差[7]。胆管癌中位生存时间为24个月,5 年死亡率高达90%[8]。早期发现的胆管癌患者的生存率远高于晚期诊断的患者[9],因而,早期诊断胆管癌显得尤为重要。目前,胆管癌早期诊断普遍使用的是影像学检查以及肿瘤标志物临床筛查。超声、CT 和核磁共振成像等影像学方法是通过肝内占位来判断胆管癌,其准确性不佳,易出现漏诊和误诊,且对人体组织具有一定的伤害性[10]。癌胚抗原(CEA)和糖类抗原19-9(CA19-9)和125(CA125)是临床上用于胆管癌早期诊断和预后监测的生物标志物[11]。这些生物标志物的灵敏度和特异性较差,尚不能满足临床的需求[12]。因此,现在急迫地需要寻找和开发特定的生物标志物,用于胆管癌的早期诊断和预后监测。
在胆管癌的发生和发展过程中,患者机体内的糖类、氨基酸和脂质等内源性小分子代谢物会发生异常变化[4]。代谢物不仅是基因和蛋白质表达的最终产物,也是基因和内部环境相互作用的结果[13-14]。因此,代谢物浓度的变化有利于揭示机体疾病的病理生理过程。代谢组学已经发展成为一种技术工具,它使用高通量、高灵敏度的分析技术来筛选生物样品中与机体功能变化密切相关的低分子量代谢物[15-16]。代谢组学通过分析和验证疾病的特定生物标志物,在此基础上寻找相关代谢途径,使我们能够更好地了解胆管癌的病理过程和物质代谢途径,最终帮助胆管癌的临床诊疗。本文主要对代谢组学在胆管癌发病机制和复发机制、早期诊断、药理研究和药效评价中的研究进展进行综述。
Advances in metabolomics of cholangiocarcinoma
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摘要: 胆管癌(CCA)恶性程度高,由于早期缺乏典型症状且尚无准确的生物标志物,发现时常处于晚期,预后较差。胆管癌的发生发展与代谢密切相关,代谢组学是研究在病理生理或基因修饰等因素影响下,生物体内内源性小分子代谢物变化的学科,具有全局分析、高通量和反映生物体系实时变化的特点,可以为胆管癌生物标志物的筛选、疾病诊疗提供新途径。综述近年来代谢组学在胆管癌方面的研究进展,以期为进一步研究提供参考。Abstract: As a highly malignant tumor, the diagnosis of cholangiocarcinoma (CCA) is often late and the prognosis is poor for which the early symptoms are atypical and the lack of accurate biomarkers. Metabolomics is an emerging science that researches the alterations of all endogenous small molecule metabolites in an organism under the influence of pathological, physiological or genetic modification. The development and progress of CCA is closely related to metabolism. Metabolomic is characterized by global analysis, high throughput and reflects real-time alterations in biology system, providing a new avenue for biomarker screening and diseases diagnosis and treatment. The advances of metabolomics studies on CCA in the recent years were reviewed in this paper which could provide the reference for further research.
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Key words:
- cholangiocarcinoma /
- metabolomic /
- biomarkers /
- mechanism /
- early diagnosis /
- pharmacodynamic evaluation
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[1] BRINDLEY P J, BACHINI M, ILYAS S I, et al. Cholangiocarcinoma[J]. Nat Rev Dis Primers,2021,7(1):65-81. doi: 10.1038/s41572-021-00300-2 [2] BANALES J M, MARIN J J G, LAMARCA A, et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and management[J]. Nat Rev Gastroenterol Hepatol,2020,17(9):557-588. doi: 10.1038/s41575-020-0310-z [3] BANALES J M, CARDINALE V, CARPINO G, et al. Expert consensus document: Cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA)[J]. Nat Rev Gastroenterol Hepatol,2016,13(5):261-280. doi: 10.1038/nrgastro.2016.51 [4] RAGGI C, TADDEI M L, RAE C, et al. Metabolic reprogramming in cholangiocarcinoma[J]. J Hepatol,2022,77(3):849-864. doi: 10.1016/j.jhep.2022.04.038 [5] MASSIRONI S, PILLA L, ELVEVI A, et al. New and emerging systemic therapeutic options for advanced cholangiocarcinoma[J]. Cells,2020,9(3):688-709. doi: 10.3390/cells9030688 [6] VALLEJO A, ERICE O, ENTRIALGO-CADIERNO R, et al. FOSL1 promotes cholangiocarcinoma via transcriptional effectors that could be therapeutically targeted[J]. J Hepatol,2021,75(2):363-376. doi: 10.1016/j.jhep.2021.03.028 [7] RIZVI S, KHAN S A, HALLEMEIER C L, et al. Cholangiocarcinoma - evolving concepts and therapeutic strategies[J]. Nat Rev Clin Oncol,2018,15(2):95-111. doi: 10.1038/nrclinonc.2017.157 [8] DIGGS L P, RUF B, MA C, et al. CD40-mediated immune cell activation enhances response to anti-PD-1 in murine intrahepatic cholangiocarcinoma[J]. J Hepatol,2021,74(5):1145-1154. doi: 10.1016/j.jhep.2020.11.037 [9] BERTUCCIO P, MALVEZZI M, CARIOLI G, et al. Global trends in mortality from intrahepatic and extrahepatic cholangiocarcinoma[J]. J Hepatol,2019,71(1):104-114. doi: 10.1016/j.jhep.2019.03.013 [10] JOO I, LEE J M, YOON J H. Imaging diagnosis of intrahepatic and perihilar cholangiocarcinoma: recent advances and challenges[J]. Radiology,2018,288(1):7-13. doi: 10.1148/radiol.2018171187 [11] MUNUGALA N, MAITHEL S K, SHROFF R T. Novel biomarkers and the future of targeted therapies in cholangiocarcinoma: a narrative review[J]. Hepatobiliary Surg Nutr,2022,11(2):253-266. doi: 10.21037/hbsn-20-475 [12] RODRIGUES P M, VOGEL A, ARRESE M, et al. Next-generation biomarkers for cholangiocarcinoma[J]. Cancers (Basel),2021,13(13):3222-3246. doi: 10.3390/cancers13133222 [13] CHO K, MAHIEU N G, JOHNSON S L, et al. After the feature presentation: technologies bridging untargeted metabolomics and biology[J]. Curr Opin Biotechnol,2014,28:143-148. doi: 10.1016/j.copbio.2014.04.006 [14] GAUGUIER D. Application of quantitative metabolomics in systems genetics in rodent models of complex phenotypes[J]. Arch Biochem Biophys,2016,589:158-167. doi: 10.1016/j.abb.2015.09.016 [15] CHUA E C, SHUI G H, LEE I T, et al. Extensive diversity in circadian regulation of plasma lipids and evidence for different circadian metabolic phenotypes in humans[J]. Proc Natl Acad Sci U S A,2013,110(35):14468-14473. doi: 10.1073/pnas.1222647110 [16] SUHRE K, SHIN S Y, PETERSEN A K, et al. Human metabolic individuality in biomedical and pharmaceutical research[J]. Nature,2011,477(7362):54-60. doi: 10.1038/nature10354 [17] NICHOLSON J K, LINDON J C, 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. doi: 10.1080/004982599238047 [18] BINO R J, HALL R D, FIEHN O, et al. Potential of metabolomics as a functional genomics tool[J]. Trends Plant Sci, 2004, 9(9): 418-425. [19] GUMA M, TIZIANI S, FIRESTEIN G S. Metabolomics in rheumatic diseases: desperately seeking biomarkers[J]. Nat Rev Rheumatol,2016,12(5):269-281. doi: 10.1038/nrrheum.2016.1 [20] GRIFFITHS W J, KOAL T, WANG Y Q, et al. Targeted metabolomics for biomarker discovery[J]. Angew Chem Int Ed Engl,2010,49(32):5426-5445. doi: 10.1002/anie.200905579 [21] DENERY J R, NUNES A A, DICKERSON T J. Characterization of differences between blood sample matrices in untargeted metabolomics[J]. Anal Chem,2011,83(3):1040-1047. doi: 10.1021/ac102806p [22] DETTMER K, ARONOV P A, HAMMOCK B D. Mass spectrometry-based metabolomics[J]. Mass Spectrom Rev,2007,26(1):51-78. doi: 10.1002/mas.20108 [23] FIEHN O. Metabolomics: the link between genotypes and phenotypes[J]. Plant Mol Biol,2002,48(1-2):155-171. [24] CLIMACO PINTO R, KARAMAN I, LEWIS M R, et al. Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets[J]. Anal Chem,2022,94(14):5493-5503. doi: 10.1021/acs.analchem.1c03592 [25] KISELEVA O, KURBATOV I, ILGISONIS E, et al. Defining blood plasma and serum metabolome by GC-MS[J]. Metabolites,2021,12(1):15-45. doi: 10.3390/metabo12010015 [26] MARKLEY J L, BRÜSCHWEILER R, EDISON A S, et al. The future of NMR-based metabolomics[J]. Curr Opin Biotechnol,2017,43:34-40. [27] JIN Q, MA R C W. Metabolomics in diabetes and diabetic complications: Insights from epidemiological studies[J]. Cells,2021,10(11):2832-2869. [28] LINDON J C, NICHOLSON J K. Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics[J]. Annu Rev Anal Chem (Palo Alto Calif),2008,1:45-69. doi: 10.1146/annurev.anchem.1.031207.113026 [29] BOROUGHS L K, DEBERARDINIS R J. Metabolic pathways promoting cancer cell survival and growth[J]. Nat Cell Biol,2015,17(4):351-359. doi: 10.1038/ncb3124 [30] LIBERTI M V, LOCASALE J W. The Warburg effect: how does it benefit cancer cells?[J]. Trends Biochem Sci, 2016, 41(3): 211-218. [31] KOUNDOUROS N, POULOGIANNIS G. Reprogramming of fatty acid metabolism in cancer[J]. Br J Cancer,2020,122(1):4-22. doi: 10.1038/s41416-019-0650-z [32] TSUN Z Y, POSSEMATO R. Amino acid management in cancer[J]. Semin Cell Dev Biol,2015,43:22-32. doi: 10.1016/j.semcdb.2015.08.002 [33] HASHIM ABDALLA M S, TAYLOR-ROBINSON S D, SHARIF A W, et al. Differences in phosphatidylcholine and bile acids in bile from Egyptian and UK patients with and without cholangiocarcinoma[J]. HPB,2011,13(6):385-390. doi: 10.1111/j.1477-2574.2011.00296.x [34] SHARIF A W, WILLIAMS H R, LAMPEJO T, et al. Metabolic profiling of bile in cholangiocarcinoma using in vitro magnetic resonance spectroscopy[J]. HPB (Oxford),2010,12(6):396-402. [35] MURAKAMI Y, KUBO S, TAMORI A, et al. Comprehensive analysis of transcriptome and metabolome analysis in Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma[J]. Sci Rep,2015,5:16294-16305. doi: 10.1038/srep16294 [36] PADTHAISONG S, PHETCHARABURANIN J, KLANRIT P, et al. Integration of global metabolomics and lipidomics approaches reveals the molecular mechanisms and the potential biomarkers for postoperative recurrence in early-stage cholangiocarcinoma[J]. Cancer Metab,2021,9(1):30-44. [37] YI M, LI J J, CHEN S N, et al. Emerging role of lipid metabolism alterations in Cancer stem cells[J]. J Exp Clin Cancer Res,2018,37(1):118-135. [38] LIANG Q, WANG C, LI B B, et al. Lipidomics analysis based on liquid chromatography mass spectrometry for hepatocellular carcinoma and intrahepatic cholangiocarcinoma[J]. RSC Adv,2015,5(78):63711-63718. [39] WANG X X, LI J, ZHANG A H. Urine metabolic phenotypes analysis of extrahepatic cholangiocarcinoma disease using ultra-high performance liquid chromatography-mass spectrometry[J]. RSC Adv,2016,6(67):63049-63057. doi: 10.1039/C6RA09430A [40] HAZNADAR M, DIEHL C M, PARKER A L, et al. Urinary metabolites diagnostic and prognostic of intrahepatic cholangiocarcinoma[J]. Cancer Epidemiol Biomarkers Prev,2019,28(10):1704-1711. [41] BANALES J M, IÑARRAIRAEGUI M, ARBELAIZ A, et al. Serum metabolites as diagnostic biomarkers for cholangiocarcinoma, hepatocellular carcinoma, and primary sclerosing cholangitis[J]. Hepatology,2019,70(2):547-562. doi: 10.1002/hep.30319 [42] MACIAS R I R, MUÑOZ-BELLVÍS L, SÁNCHEZ-MARTÍN A, et al. A novel serum metabolomic profile for the differential diagnosis of distal cholangiocarcinoma and pancreatic ductal adenocarcinoma[J]. Cancers (Basel),2020,12(6):1433-1450. doi: 10.3390/cancers12061433 [43] KOTAWONG K, CHAIJAROENKUL W, ROYTRAKUL S, et al. Screening of molecular targets of action of atractylodin in cholangiocarcinoma by applying proteomic and metabolomic approaches[J]. Metabolites,2019,9(11):260-273. doi: 10.3390/metabo9110260 [44] GIUSTARINI D, GALVAGNI F, TESEI A, et al. Glutathione, glutathione disulfide, and S-glutathionylated proteins in cell cultures[J]. Free Radic Biol Med,2015,89:972-981. doi: 10.1016/j.freeradbiomed.2015.10.410 [45] ZHANG J, HANG C H, JIANG T, et al. Nuclear magnetic resonance-based metabolomic analysis of the anticancer effect of metformin treatment on cholangiocarcinoma cells[J]. Front Oncol,2020,10:570516-570528. doi: 10.3389/fonc.2020.570516 [46] BI L, REN Y D, FENG M X, et al. HDAC11 regulates glycolysis through the LKB1/AMPK signaling pathway to maintain hepatocellular carcinoma stemness[J]. Cancer Res,2021,81(8):2015-2028. doi: 10.1158/0008-5472.CAN-20-3044 [47] STEPHENNE X, FORETZ M, TALEUX N, et al. Metformin activates AMP-activated protein kinase in primary human hepatocytes by decreasing cellular energy status[J]. Diabetologia,2011,54(12):3101-3110. doi: 10.1007/s00125-011-2311-5 [48] CHEN X, LIU H S, SHEN L, et al. Untargeted UPLC-MS-based metabolomics analysis reveals the metabolic profile of intrahepatic cholangiocarcinoma process and the intervention effect of Osthole in mice[J]. Pharmacol Res Mod Chin Med,2022,3:100096-100113. doi: 10.1016/j.prmcm.2022.100096
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