[1] WILKINSON L, GATHANI T. Understanding breast cancer as a global health concern[J]. Br J Radiol,2022,95(1130):20211033-20211036. doi:  10.1259/bjr.20211033
[2] CAO W, CHEN H D, YU Y W, et al. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020[J]. Chin Med J (Engl),2021,134(7):783-791. doi:  10.1097/CM9.0000000000001474
[3] SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA A Cancer J Clin,2021,71(3):209-249. doi:  10.3322/caac.21660
[4] LI A H, SHEN Z, SUN Z F, et al. Occupational risk factors and breast cancer in Beijing, China: a hospital-based case-control study[J]. BMJ Open,2022,12(2):e054151-e054159. doi:  10.1136/bmjopen-2021-054151
[5] 徐文斌, 龚乘丙, 李尧, 等. p53 codon 72基因多态性与中国女性乳腺癌发生风险关系的系统评价与Meta分析[J]. 医学新知, 2022, 32(1):23-32.
[6] 周星彤, 沈松杰, 孙强. 中国乳腺癌筛查现状及进展[J]. 中国医学前沿杂志(电子版), 2020, 12(3):6-11.
[7] 王柏田, 王笑峰. 血清肿瘤标记物联合动态检测在乳腺癌诊断和监控治疗中的应用价值[J]. 中国医学创新, 2021, 18(3):6-11.
[8]

YANG L Q, WANG Y, CAI H S, et al. Application of metabolomics in the diagnosis of breast cancer: a systematic review[J]. J Cancer,2020,11(9):2540-2551. doi:  10.7150/jca.37604
[9] 程柳柳, 李俊. 乳腺癌早期诊断技术[J]. 国际肿瘤学杂志, 2013, 40(6):453-455. doi:  10.3760/cma.j.issn.1673-422X.2013.06.017
[10]

BLACK E, RICHMOND R. Improving early detection of breast cancer in sub-Saharan Africa: why mammography may not be the way forward[J]. Global Health,2019,15(1):3-14. doi:  10.1186/s12992-018-0446-6
[11]

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
[12]

SAIGUSA D, MATSUKAWA N, HISHINUMA E, et al. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics[J]. Drug Metab Pharmacokinet,2021,37:100373-100389. doi:  10.1016/j.dmpk.2020.11.008
[13]

KIM K J, KIM H J, PARK H G, et al. A MALDI-MS-based quantitative analytical method for endogenous estrone in human breast cancer cells[J]. Sci Rep,2016,6:24489-24496. doi:  10.1038/srep24489
[14]

PANIS C, BINATO R, CORREA S, et al. Short infusion of paclitaxel imbalances plasmatic lipid metabolism and correlates with cardiac markers of acute damage in patients with breast cancer[J]. Cancer Chemother Pharmacol,2017,80(3):469-478. doi:  10.1007/s00280-017-3384-8
[15]

SILVA C L, OLIVAL A, PERESTRELO R, et al. Untargeted urinary 1H NMR-based metabolomic pattern as a potential platform in breast cancer detection[J]. Metabolites,2019,9(11):269-286. doi:  10.3390/metabo9110269
[16]

BAI X P, NI J, BERETOV J, et al. Triple-negative breast cancer therapeutic resistance: where is the Achilles' heel? Cancer Lett,2021,497:100-111. doi:  10.1016/j.canlet.2020.10.016
[17]

ZHANG A H, SUN H, WANG X J. Serum metabolomics as a novel diagnostic approach for disease: a systematic review[J]. Anal Bioanal Chem,2012,404(4):1239-1245. doi:  10.1007/s00216-012-6117-1
[18]

MAMAS M, DUNN W B, NEYSES L, et al. The role of metabolites and metabolomics in clinically applicable biomarkers of disease[J]. Arch Toxicol,2011,85(1):5-17. doi:  10.1007/s00204-010-0609-6
[19]

JOHNSON C H, IVANISEVIC J, SIUZDAK G. Metabolomics: beyond biomarkers and towards mechanisms[J]. Nat Rev Mol Cell Biol,2016,17(7):451-459. doi:  10.1038/nrm.2016.25
[20]

BEGLEY P, FRANCIS-MCINTYRE S, DUNN W B, et al. Development and performance of a gas Chromatography−Time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum[J]. Anal Chem,2009,81(16):7038-7046. doi:  10.1021/ac9011599
[21]

ZHANG T L, ZHANG A H, QIU S, et al. Current trends and innovations in bioanalytical techniques of metabolomics[J]. Crit Rev Anal Chem,2016,46(4):342-351. doi:  10.1080/10408347.2015.1079475
[22]

TEAV T, GALLART-AYALA H, VAN DER VELPEN V, et al. Merged targeted quantification and untargeted profiling for comprehensive assessment of acylcarnitine and amino acid metabolism[J]. Anal Chem,2019,91(18):11757-11769. doi:  10.1021/acs.analchem.9b02373
[23] 金丹, 刘萌萌, 郭咸希, 等. 代谢组学技术在糖尿病中的应用研究进展[J]. 中国药师, 2021, 24(3):542-547. doi:  10.3969/j.issn.1008-049X.2021.03.028
[24]

ALEXANDROV T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence[J]. Annu Rev Biomed Data Sci,2020,3:61-87. doi:  10.1146/annurev-biodatasci-011420-031537
[25]

GEIER B, SOGIN E M, MICHELLOD D, et al. Spatial metabolomics of in situ host-microbe interactions at the micrometre scale[J]. Nat Microbiol,2020,5(3):498-510. doi:  10.1038/s41564-019-0664-6
[26]

SHARIATGORJI R, NILSSON A, FRIDJONSDOTTIR E, et al. Spatial visualization of comprehensive brain neurotransmitter systems and neuroactive substances by selective in situ chemical derivatization mass spectrometry imaging[J]. Nat Protoc,2021,16(7):3298-3321. doi:  10.1038/s41596-021-00538-w
[27]

CHONG J, WISHART D S, XIA J G. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis[J]. Curr Protoc Bioinformatics,2019,68(1):e86-e214.
[28]

PORTO-FIGUEIRA P, PEREIRA J A M, CÂMARA J S. Exploring the potential of needle trap microextraction combined with chromatographic and statistical data to discriminate different types of cancer based on urinary volatomic biosignature[J]. Anal Chim Acta,2018,1023:53-63. doi:  10.1016/j.aca.2018.04.027
[29]

RAMAUTAR R. Capillary electrophoresis-mass spectrometry for clinical metabolomics[J]. Adv Clin Chem,2016,74:1-34.
[30]

CHEN T L, YOU Y J, XIE G X, et al. Strategy for an association study of the intestinal microbiome and brain metabolome across the lifespan of rats[J]. Anal Chem,2018,90(4):2475-2483. doi:  10.1021/acs.analchem.7b02859
[31] 谭起龙, 邓魁, 李康, 等. 随机森林回归分析方法在代谢组学批次效应移除中的应用[J]. 中国卫生统计, 2020, 37(5):667-671.
[32]

LI M, GUO Y, FENG Y M, et al. Identification of triple-negative breast cancer genes and a novel high-risk breast cancer prediction model development based on PPI data and support vector machines[J]. Front Genet,2019,10:180-192. doi:  10.3389/fgene.2019.00180
[33]

ZHOU L, RUEDA M, ALKHATEEB A. Classification of breast cancer Nottingham prognostic index using high-dimensional embedding and residual neural network[J]. Cancers,2022,14(4):934-940. doi:  10.3390/cancers14040934
[34]

CATARINA LS, ANA O, ROSA P, et al. Untargeted urinary 1H NMR-based metabolomic pattern as a potential platform in breast cancer detection[J]. Metabolites, 2019, 9(11): 269-286.
[35]

KOU F, ZHU B J, ZHOU W B, et al. Targeted metabolomics reveals dynamic portrayal of amino acids and derivatives in triple-negative breast cancer cells and culture media[J]. Mol Omics,2021,17(1):142-152. doi:  10.1039/D0MO00126K
[36]

XAVIER ASSAD D, ACEVEDO A C, CANÇADO PORTO MASCARENHAS E, et al. Using an untargeted metabolomics approach to identify salivary metabolites in women with breast cancer[J]. Metabolites,2020,10(12):506-519. doi:  10.3390/metabo10120506
[37]

DÍAZ-BELTRÁN L, GONZÁLEZ-OLMEDO C, LUQUE-CARO N, et al. Human plasma metabolomics for biomarker discovery: targeting the molecular subtypes in breast cancer[J]. Cancers,2021,13(1):147-165. doi:  10.3390/cancers13010147
[38]

CHEONG J E, SUN L J. Targeting the IDO1/TDO2KYN-AhR pathway for cancer immunotherapy - challenges and opportunities[J]. Trends Pharmacol Sci,2018,39(3):307-325. doi:  10.1016/j.tips.2017.11.007
[39]

SUN L C, SUO C X, LI S T, et al. Metabolic reprogramming for cancer cells and their microenvironment: beyond the Warburg Effect[J]. Biochim Biophys Acta Rev Cancer,2018,1870(1):51-66. doi:  10.1016/j.bbcan.2018.06.005
[40]

FAUBERT B, SOLMONSON A, DEBERARDINIS R J. Metabolic reprogramming and cancer progression[J]. Science,2020,368(6487):112-123.
[41]

TESLAA T, TEITELL M A. Techniques to monitor glycolysis[J]. Methods Enzymol,2014,542:91-114.
[42]

CORBET C, FERON O. Cancer cell metabolism and mitochondria: Nutrient plasticity for TCA cycle fueling[J]. Biochim Biophys Acta Rev Cancer,2017,1868(1):7-15. doi:  10.1016/j.bbcan.2017.01.002
[43]

COURTNAY R, NGO D C, MALIK N, et al. Cancer metabolism and the Warburg effect: the role of HIF-1 and PI3K[J]. Mol Biol Rep,2015,42(4):841-851. doi:  10.1007/s11033-015-3858-x
[44]

GHANEM A, MELZER A M, ZAAL E, et al. Ascorbate kills breast cancer cells by rewiring metabolism via redox imbalance and energy crisis[J]. Free Radic Biol Med,2021,163:196-209. doi:  10.1016/j.freeradbiomed.2020.12.012
[45]

ARMIÑÁN A, PALOMINO-SCHÄTZLEIN M, DELADRIERE C, et al. Metabolomics facilitates the discrimination of the specific anti-cancer effects of free- and polymer-conjugated doxorubicin in breast cancer models[J]. Biomaterials,2018,162:144-153. doi:  10.1016/j.biomaterials.2018.02.015
[46]

GIESBERTZ P, ECKER J, HAAG A, et al. An LC-MS/MS method to quantify acylcarnitine species including isomeric and odd-numbered forms in plasma and tissues[J]. J Lipid Res,2015,56(10):2029-2039. doi:  10.1194/jlr.D061721
[47]

SUN C L, WANG F K, ZHANG Y, et al. Mass spectrometry imaging-based metabolomics to visualize the spatially resolved reprogramming of carnitine metabolism in breast cancer[J]. Theranostics,2020,10(16):7070-7082. doi:  10.7150/thno.45543
[48]

LYON D E, STARKWEATHER A, YAO Y W, et al. Pilot study of metabolomics and psychoneurological symptoms in women with early stage breast cancer[J]. Biol Res Nurs,2018,20(2):227-236. doi:  10.1177/1099800417747411
[49]

SUN Y H, KIM J H, VANGIPURAM K, et al. Pharmacometabolomics reveals a role for histidine, phenylalanine, and threonine in the development of paclitaxel-induced peripheral neuropathy[J]. Breast Cancer Res Treat,2018,171(3):657-666. doi:  10.1007/s10549-018-4862-3
[50]

DE VRIES SCHULTINK A H M, ALEXI X, VAN WERKHOVEN E, et al. An Antiestrogenic Activity Score for tamoxifen and its metabolites is associated with breast cancer outcome[J]. Breast Cancer Res Treat,2017,161(3):567-574. doi:  10.1007/s10549-016-4083-6
[51]

KUS K, KIJ A, ZAKRZEWSKA A, et al. Alterations in arginine and energy metabolism, structural and signalling lipids in metastatic breast cancer in mice detected in plasma by targeted metabolomics and lipidomics[J]. Breast Cancer Res,2018,20(1):148-161. doi:  10.1186/s13058-018-1075-y
[52]

SPARANO J A, GRAY R J, MAKOWER D F, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer[J]. N Engl J Med,2018,379(2):111-121. doi:  10.1056/NEJMoa1804710
[53]

GOLDSTEIN D A, MAYER C, SHOCHAT T, et al. The concordance of treatment decision guided by OncotypeDX and the PREDICT tool in real-world early-stage breast cancer[J]. Cancer Med,2020,9(13):4603-4612. doi:  10.1002/cam4.3088
[54]

DINAN M A, WILSON L E, REED S D, et al. Association of 21-gene assay (OncotypeDX) testing and receipt of chemotherapy in the medicare breast cancer patient population following initial adoption[J]. Clin Breast Cancer,2020,20(6):487-494. doi:  10.1016/j.clbc.2020.05.010
[55]

MCCARTNEY A, VIGNOLI A, TENORI L, et al. Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification[J]. NPJ Breast Cancer,2019,5:26-31. doi:  10.1038/s41523-019-0123-9
[56]

SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2021[J]. CA Cancer J Clin,2021,71(1):7-33. doi:  10.3322/caac.21654
[57]

JIANG Y L, CHEN X P, FU S M. Advances in the correlation between intestinal microbiota and breast cancer development[J]. J Cancer Ther,2020,11(12):758-771. doi:  10.4236/jct.2020.1112066