Advances in metabolites of breast cancer based on metabolomics_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

WU Xueshuo , FENG Jing ,  ZHOU Yi

Department of Breast Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, P. R. China;

Corresponding author：

ZHOU Yi, Email: lubj2001@163.com

Keywords：

breast cancer; metabolomics; tumor microenvironment

DOI：

10.7507/1007-9424.202101056

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To summarize the research results of metabolites of breast cancer based on metabonomics technology, and systematically reviews them in order to provide a new direction for the research of metabolism of breast cancer.Method By searching the relevant literatures in recent years, the application of metabonomics in identifying high-risk breast cancer population, monitoring the progress of tumor and evaluating the response of radiotherapy and chemotherapy were analyzed and summarized.Results With the development of high-resolution, high-sensitivity and high-throughput bioanalysis platform technology, metabolomics had been widely used in breast cancer research field by virtue of its unique perspective and technical advantages to more accurately, systematically and dynamically monitor the changes of host metabolites.Conclusion Metabolomics technology provides a new research direction for primary prevention, early screening and diagnosis of breast cancer and optimal treatment strategy selection.

Citation： WU Xueshuo, FENG Jing, ZHOU Yi. Advances in metabolites of breast cancer based on metabolomics. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(12): 1661-1665. doi: 10.7507/1007-9424.202101056 Copy

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2.	Madssen TS, Cao MD, Pladsen AV, et al. Historical biobanks in breast cancer metabolomics- challenges and opportunities. Metabolites, 2019, 9(11): 278.
3.	Hu C, Hart SN, Gnanaolivu R, et al. A population-based study of genes previously implicated in breast cancer. N Engl J Med, 2021, 384(5): 440-451.
4.	Fernández MF, Reina-Pérez I, Astorga JM, et al. Breast cancer and its relationship with the microbiota. Int J Environ Res Public Health, 2018, 15(8): 1747.
5.	Rashed R, Darwish H, Omran M, et al. A novel serum metabolome score for breast cancer diagnosis. Br J Biomed Sci, 2020, 77(4): 196-201.
6.	Geier B, Sogin EM, Michellod D, et al. Spatial metabolomics of in situ host-microbe interactions at the micrometre scale. Nat Microbiol, 2020, 5(3): 498-510.
7.	Bjerrum JT. Metabonomics: methods and protocols: preface. Methods Mol Biol, 2015, 1277: v.
8.	Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov, 2016, 15(7): 473-484.
9.	Chandler PD, Song Y, Lin J, et al. Lipid biomarkers and long-term risk of cancer in the women’s health study. Am J Clin Nutr, 2016, 103(6): 1397-1407.
10.	Klawitter J, Gurshtein J, Christians U, et al. Bezielle (BZL101)-induced oxidative stress damage followed by redistribution of metabolic fluxes in breast cancer cells: a combined proteomic and metabolomic study. Int J Cancer, 2011, Dec 15: 45-57.
11.	Shu X, Wu L, Khankari NK, et al. Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis. Int J Epidemiol, 2019, 48(3): 795-806.
12.	Xie G, Zhou B, Zhao A, et al. Lowered circulating aspartate is a metabolic feature of human breast cancer. Oncotarget, 2015, 6(32): 33369-33381.
13.	Zhang Y, Gao Y, Li Y, et al. Characterization of the relationship between the expression of aspartate β-hydroxylase and the pathological characteristics of breast cancer. Med Sci Monit, 2020, 26: e926752.
14.	Miolo G, Muraro E, Caruso D, et al. Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer. Oncotarget, 2016, 7(26): 39809-39822.
15.	徐君南, 李晓睿, 孙涛. 基于超高效串联质谱的氨基酸代谢组学预测晚期乳腺癌化疗反应初探. 中国肿瘤临床, 2017, 44(24): 1253-1257.
16.	His M, Viallon V, Dossus L, et al. Prospective analysis of circulating metabolites and breast cancer in EPIC. BMC Med, 2019, 17(1): 178.
17.	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: 13.
18.	Bene J, Hadzsiev K, Melegh B. Role of carnitine and its derivatives in the development and management of type 2 diabetes. Nutr Diabetes, 2018, 8(1): 8.
19.	Bahcecioglu G, Basara G, Ellis BW, et al. Breast cancer models: engineering the tumor microenvironment. Acta Biomater, 2020, 106: 1-21.
20.	Warburg O. On the origin of cancer cells. Science, 1956, 123(3191): 309-314.
21.	Maldonado R, Talana CA, Song C, et al. β-hydroxybutyrate does not alter the effects of glucose deprivation on breast cancer cells. Oncol Lett, 2021, 21(1): 65.
22.	Hua W, Ten Dijke P, Kostidis S, et al. TGFβ-induced metabolic reprogramming during epithelial-to-mesenchymal transition in cancer. Cell Mol Life Sci, 2020, 77(11): 2103-2123.
23.	Nilchian A, Giotopoulou N, Sun W, et al. Different regulation of Glut1 expression and glucose uptake during the induction and chronic stages of TGFβ1-induced EMT in breast cancer cells. Biomolecules, 2020, 10(12): 1621.
24.	Silva C, Perestrelo R, Silva P, et al. Volatomic pattern of breast cancer and cancer-free tissues as a powerful strategy to identify potential biomarkers. Analyst, 2019, 144(14): 4153-4161.
25.	Budczies J, Denkert C, Müller BM, et al. Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue—a GC-TOFMS based metabolomics study. BMC Genomics, 2012, 13: 334.
26.	Bujak-Gizycka B, Madej J, Bystrowska B, et al. Angiotensin 1-7 formation in breast tissue is attenuated in breast cancer—a study on the metabolism of angiotensinogen in breast cancer cell lines. J Physiol Pharmacol, 2019, 70(4): 503-514.
27.	Ishikane S, Takahashi-Yanaga F. The role of angiotensin Ⅱ in cancer metastasis: potential of renin-angiotensin system blockade as a treatment for cancer metastasis. Biochem Pharmacol, 2018, 151: 96-103.
28.	Zarychta E, Rhone P, Bielawski K, et al. Elevated plasma levels of tissue factor as a valuable diagnostic biomarker with relevant efficacy for prediction of breast cancer morbidity. J Physiol Pharmacol, 2018, 69(6): 921-931.
29.	Kim HY, Lee KM, Kim SH, et al. Comparative metabolic and lipidomic profiling of human breast cancer cells with different metastatic potentials. Oncotarget, 2016, 7(41): 67111-67128.
30.	Pan M, Reid MA, Lowman XH, et al. Regional glutamine deficiency in tumours promotes dedifferentiation through inhibition of histone demethylation. Nat Cell Biol, 2016, 18(10): 1090-1101.
31.	Borgan E, Sitter B, Lingjærde OC, et al. Merging transcriptomics and metabolomics—advances in breast cancer profiling. BMC Cancer, 2010, 10: 628.
32.	Gong Y, Ji P, Yang YS, et al. Metabolic-pathway-based subtyping of triple-negative breast cancer reveals potential therapeutic targets. Cell Metab, 2021, 33(1): 51-64.
33.	Dinges SS, Hohm A, Vandergrift LA, et al. Cancer metabolomic markers in urine: evidence, techniques and recommendations. Nat Rev Urol, 2019, 16(6): 339-362.
34.	Silva CL, Perestrelo R, Silva P, et al. Implementing a central composite design for the optimization of solid phase microextraction to establish the urinary volatomic expression: a first approach for breast cancer. Metabolomics, 2019, 15(4): 64.
35.	Omran MM, Rashed RE, Darwish H, et al. Development of a gas chromatography-mass spectrometry method for breast cancer diagnosis based on nucleoside metabolomes 1-methyl adenosine, 1-methylguanosine and 8-hydroxy-2’-deoxyguanosine. Biomed Chromatogr, 2020, 34(1): e4713.
36.	Murata T, Yanagisawa T, Kurihara T, et al. Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination. Breast Cancer Res Treat, 2019, 177(3): 591-601.
37.	Takayama T, Tsutsui H, Shimizu I, et al. Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clin Chim Acta, 2016, 452: 18-26.

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin, 2020, 70(1): 7-30.
2. Madssen TS, Cao MD, Pladsen AV, et al. Historical biobanks in breast cancer metabolomics- challenges and opportunities. Metabolites, 2019, 9(11): 278.
3. Hu C, Hart SN, Gnanaolivu R, et al. A population-based study of genes previously implicated in breast cancer. N Engl J Med, 2021, 384(5): 440-451.
4. Fernández MF, Reina-Pérez I, Astorga JM, et al. Breast cancer and its relationship with the microbiota. Int J Environ Res Public Health, 2018, 15(8): 1747.
5. Rashed R, Darwish H, Omran M, et al. A novel serum metabolome score for breast cancer diagnosis. Br J Biomed Sci, 2020, 77(4): 196-201.
6. Geier B, Sogin EM, Michellod D, et al. Spatial metabolomics of in situ host-microbe interactions at the micrometre scale. Nat Microbiol, 2020, 5(3): 498-510.
7. Bjerrum JT. Metabonomics: methods and protocols: preface. Methods Mol Biol, 2015, 1277: v.
8. Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov, 2016, 15(7): 473-484.
9. Chandler PD, Song Y, Lin J, et al. Lipid biomarkers and long-term risk of cancer in the women’s health study. Am J Clin Nutr, 2016, 103(6): 1397-1407.
10. Klawitter J, Gurshtein J, Christians U, et al. Bezielle (BZL101)-induced oxidative stress damage followed by redistribution of metabolic fluxes in breast cancer cells: a combined proteomic and metabolomic study. Int J Cancer, 2011, Dec 15: 45-57.
11. Shu X, Wu L, Khankari NK, et al. Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis. Int J Epidemiol, 2019, 48(3): 795-806.
12. Xie G, Zhou B, Zhao A, et al. Lowered circulating aspartate is a metabolic feature of human breast cancer. Oncotarget, 2015, 6(32): 33369-33381.
13. Zhang Y, Gao Y, Li Y, et al. Characterization of the relationship between the expression of aspartate β-hydroxylase and the pathological characteristics of breast cancer. Med Sci Monit, 2020, 26: e926752.
14. Miolo G, Muraro E, Caruso D, et al. Pharmacometabolomics study identifies circulating spermidine and tryptophan as potential biomarkers associated with the complete pathological response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer. Oncotarget, 2016, 7(26): 39809-39822.
15. 徐君南, 李晓睿, 孙涛. 基于超高效串联质谱的氨基酸代谢组学预测晚期乳腺癌化疗反应初探. 中国肿瘤临床, 2017, 44(24): 1253-1257.
16. His M, Viallon V, Dossus L, et al. Prospective analysis of circulating metabolites and breast cancer in EPIC. BMC Med, 2019, 17(1): 178.
17. 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: 13.
18. Bene J, Hadzsiev K, Melegh B. Role of carnitine and its derivatives in the development and management of type 2 diabetes. Nutr Diabetes, 2018, 8(1): 8.
19. Bahcecioglu G, Basara G, Ellis BW, et al. Breast cancer models: engineering the tumor microenvironment. Acta Biomater, 2020, 106: 1-21.
20. Warburg O. On the origin of cancer cells. Science, 1956, 123(3191): 309-314.
21. Maldonado R, Talana CA, Song C, et al. β-hydroxybutyrate does not alter the effects of glucose deprivation on breast cancer cells. Oncol Lett, 2021, 21(1): 65.
22. Hua W, Ten Dijke P, Kostidis S, et al. TGFβ-induced metabolic reprogramming during epithelial-to-mesenchymal transition in cancer. Cell Mol Life Sci, 2020, 77(11): 2103-2123.
23. Nilchian A, Giotopoulou N, Sun W, et al. Different regulation of Glut1 expression and glucose uptake during the induction and chronic stages of TGFβ1-induced EMT in breast cancer cells. Biomolecules, 2020, 10(12): 1621.
24. Silva C, Perestrelo R, Silva P, et al. Volatomic pattern of breast cancer and cancer-free tissues as a powerful strategy to identify potential biomarkers. Analyst, 2019, 144(14): 4153-4161.
25. Budczies J, Denkert C, Müller BM, et al. Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue—a GC-TOFMS based metabolomics study. BMC Genomics, 2012, 13: 334.
26. Bujak-Gizycka B, Madej J, Bystrowska B, et al. Angiotensin 1-7 formation in breast tissue is attenuated in breast cancer—a study on the metabolism of angiotensinogen in breast cancer cell lines. J Physiol Pharmacol, 2019, 70(4): 503-514.
27. Ishikane S, Takahashi-Yanaga F. The role of angiotensin Ⅱ in cancer metastasis: potential of renin-angiotensin system blockade as a treatment for cancer metastasis. Biochem Pharmacol, 2018, 151: 96-103.
28. Zarychta E, Rhone P, Bielawski K, et al. Elevated plasma levels of tissue factor as a valuable diagnostic biomarker with relevant efficacy for prediction of breast cancer morbidity. J Physiol Pharmacol, 2018, 69(6): 921-931.
29. Kim HY, Lee KM, Kim SH, et al. Comparative metabolic and lipidomic profiling of human breast cancer cells with different metastatic potentials. Oncotarget, 2016, 7(41): 67111-67128.
30. Pan M, Reid MA, Lowman XH, et al. Regional glutamine deficiency in tumours promotes dedifferentiation through inhibition of histone demethylation. Nat Cell Biol, 2016, 18(10): 1090-1101.
31. Borgan E, Sitter B, Lingjærde OC, et al. Merging transcriptomics and metabolomics—advances in breast cancer profiling. BMC Cancer, 2010, 10: 628.
32. Gong Y, Ji P, Yang YS, et al. Metabolic-pathway-based subtyping of triple-negative breast cancer reveals potential therapeutic targets. Cell Metab, 2021, 33(1): 51-64.
33. Dinges SS, Hohm A, Vandergrift LA, et al. Cancer metabolomic markers in urine: evidence, techniques and recommendations. Nat Rev Urol, 2019, 16(6): 339-362.
34. Silva CL, Perestrelo R, Silva P, et al. Implementing a central composite design for the optimization of solid phase microextraction to establish the urinary volatomic expression: a first approach for breast cancer. Metabolomics, 2019, 15(4): 64.
35. Omran MM, Rashed RE, Darwish H, et al. Development of a gas chromatography-mass spectrometry method for breast cancer diagnosis based on nucleoside metabolomes 1-methyl adenosine, 1-methylguanosine and 8-hydroxy-2’-deoxyguanosine. Biomed Chromatogr, 2020, 34(1): e4713.
36. Murata T, Yanagisawa T, Kurihara T, et al. Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination. Breast Cancer Res Treat, 2019, 177(3): 591-601.
37. Takayama T, Tsutsui H, Shimizu I, et al. Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clin Chim Acta, 2016, 452: 18-26.

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Advances in metabolites of breast cancer based on metabolomics

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