Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Results
2.1. Patients’ Characteristics
2.2. LC-HRMS Analysis
2.3. Chemometric Analysis
2.4. Differential Metabolomic Profiling
2.5. Biomarker Evaluation and Model Creation
2.6. Pathway Analysis
3. Discussion
4. Materials and Methods
4.1. Participants and Ethics
4.2. Plasma Sample Preparation
4.3. Metabolite Extraction
4.4. LC-HRMS Analysis
4.5. Data Processing
4.6. Normalization and Analytical Validation
4.7. Statistical Analysis
4.8. Metabolite Identification
4.9. Biomarker Evaluation
4.10. Pathway Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Nguyen, T.L.; Pham, T.Q.N.; Hoang, V.M.; Kim, B.G.; Phan, T.H.; Doan, T.H.; Nguyen, T.L.; Van Duong, K.; Luong, N.K. Trends in Second-Hand Tobacco Smoke Exposure Levels at Home among Viet Nam School Children Aged 13–15 and Associated Factors. Asian Pac. J. Cancer Prev. 2016, 17, 43–47. [Google Scholar] [CrossRef]
- Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fan, Y.; Zhou, X.; Xia, T.-S.; Chen, Z.; Li, J.; Liu, Q.; Alolga, R.N.; Chen, Y.; Lai, M.-D.; Li, P.; et al. Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer. Oncotarget 2016, 7, 9925–9938. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Haukaas, T.H.; Euceda, L.R.; Giskeødegård, G.F.; Lamichhane, S.; Krohn, M.; Jernström, S.; Aure, M.R.; Lingjærde, O.C.; Schlichting, E.; Garred, Ø.; et al. Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes. Cancer Metab. 2016, 4, 1–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cardoso, M.R.; Santos, J.C.; Ribeiro, M.L.; Talarico, M.C.R.; Viana, L.R.; Derchain, S.F.M. A Metabolomic Approach to Predict Breast Cancer Behavior and Chemotherapy Response. Int. J. Mol. Sci. 2018, 19, 617. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kalinowski, L.; Saunus, J.M.; Reed, A.E.M.; Lakhani, S.R. Breast Cancer Heterogeneity in Primary and Metastatic Disease. Adv. Exp. Med. Biol. 2019, 1152, 75–104. [Google Scholar] [CrossRef]
- Mendoza-Diaz, S.; Castilla-Tarra, J.A.; Pena-Torres, E.; Orozco-Ospino, M.; Mendoza-Diaz, S.; Nuñez-Lemus, M.; Garcia-Angulo, O.; Garcia-Mora, M.; Guzman-AbiSaab, L.; Lehmann-Mosquera, C.; et al. Pathological Response to Neoadjuvant Chemotherapy and the Molecular Classification of Locally Advanced Breast Cancer in a Latin American Cohort. Oncologist 2019, 24, e1360–e1370. [Google Scholar] [CrossRef] [Green Version]
- Wang, R.; Zhao, H.; Zhang, X.; Zhao, X.; Song, Z.; Ouyang, J. Metabolic Discrimination of Breast Cancer Subtypes at the Single-Cell Level by Multiple Microextraction Coupled with Mass Spectrometry. Anal. Chem. 2019, 91, 3667–3674. [Google Scholar] [CrossRef]
- Tsimberidou, A.-M.; Iskander, N.G.; Hong, D.S.; Wheler, J.J.; Falchook, G.S.; Fu, S.; Piha-Paul, S.; Naing, A.; Janku, F.; Luthra, R.; et al. Personalized Medicine in a Phase I Clinical Trials Program: The MD Anderson Cancer Center Initiative. Clin. Cancer Res. 2012, 18, 6373–6383. [Google Scholar] [CrossRef] [Green Version]
- Huang, S.; Chong, N.; Lewis, N.E.; Sijia, H.; Xie, G.; Garmire, L.X. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis. Genome Med. 2016, 8, 1–14. [Google Scholar] [CrossRef] [Green Version]
- Carels, N.; Spinassé, L.B.; Tilli, T.M.; Tuszynski, J.A. Toward precision medicine of breast cancer. Theor. Biol. Med Model. 2016, 13, 1–46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hirschey, M.D.; DeBerardinis, R.J.; Diehl, A.M.E.; Drew, J.E.; Frezza, C.; Green, M.F.; Jones, L.W.; Ko, Y.H.; Le, A.; Lea, M.A.; et al. Dysregulated metabolism contributes to oncogenesis. Semin. Cancer Biol. 2015, 35, S129–S150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Beger, R.D.; Dunn, W.; Schmidt, M.A.; Gross, S.S.; Kirwan, J.A.; Cascante, M.; Brennan, L.; Wishart, D.S.; Oresic, M.; Hankemeier, T.; et al. Metabolomics enables precision medicine: “A White Paper, Community Perspective”. Metabolomics 2016, 12, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Griffin, J.L.; Shockcor, J.P. Metabolic profiles of cancer cells. Nat. Rev. Cancer 2004, 4, 551–561. [Google Scholar] [CrossRef] [PubMed]
- Luo, X.; Yu, H.; Song, Y.; Sun, T. Integration of metabolomic and transcriptomic data reveals metabolic pathway alteration in breast cancer and impact of related signature on survival. J. Cell. Physiol. 2019, 234, 13021–13031. [Google Scholar] [CrossRef]
- Cheung, P.K.; Ma, M.H.; Tse, H.F.; Yeung, K.F.; Tsang, H.F.; Chu, M.K.M.; Kan, C.M.; Cho, W.C.; Ng, L.B.W.; Chan, L.W.C.; et al. The applications of metabolomics in the molecular diagnostics of cancer. Expert Rev. Mol. Diagn. 2019, 19, 785–793. [Google Scholar] [CrossRef]
- Giskeødegård, G.F.; Grinde, M.T.; Sitter, B.; Axelson, D.E.; Lundgren, S.; Fjøsne, H.E.; Dahl, S.; Gribbestad, I.S.; Bathen, T.F. Multivariate Modeling and Prediction of Breast Cancer Prognostic Factors Using MR Metabolomics. J. Proteome Res. 2010, 9, 972–979. [Google Scholar] [CrossRef]
- Sitter, B.; Bathen, T.F.; Singstad, T.E.; Fjøsne, H.E.; Lundgren, S.; Halgunset, J.; Gribbestad, I.S. Quantification of metabolites in breast cancer patients with different clinical prognosis using HR MAS MR spectroscopy. NMR Biomed. 2010, 23, 424–431. [Google Scholar] [CrossRef]
- Asiago, V.M.; Alvarado, L.Z.; Shanaiah, N.; Gowda, G.A.N.; Owusu-Sarfo, K.; Ballas, R.A.; Raftery, D. Early Detection of Recurrent Breast Cancer Using Metabolite Profiling. Cancer Res. 2010, 70, 8309–8318. [Google Scholar] [CrossRef] [Green Version]
- Zhao, H.; Shen, J.; Moore, S.C.; Ye, Y.; Wu, X.; Esteva, F.J.; Tripathy, D.; Chow, W.-H.; Zanetti, K.A. Breast cancer risk in relation to plasma metabolites among Hispanic and African American women. Breast Cancer Res. Treat. 2019, 176, 687–696. [Google Scholar] [CrossRef]
- Silva, C.L.; Perestrelo, R.; Silva, P.; Tomás, H.; Câmara, J.S. 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, 64. [Google Scholar] [CrossRef] [PubMed]
- Murata, T.; Yanagisawa, T.; Kurihara, T.; Kaneko, M.; Ota, S.; Enomoto, A.; Tomita, M.; Sugimoto, M.; Sunamura, M.; Hayashida, T.; et al. Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination. Breast Cancer Res. Treat. 2019, 177, 591–601. [Google Scholar] [CrossRef] [PubMed]
- Jové, M.; Collado, R.; Quiles, J.L.; Ramírez-Tortosa, M.-C.; Sol, J.; Ruiz-Sanjuan, M.; Fernandez, M.; Cabrera, C.D.L.T.; Ramírez-Tortosa, C.; Granados-Principal, S.; et al. A plasma metabolomic signature discloses human breast cancer. Oncotarget 2017, 8, 19522–19533. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gouazé-Andersson, V.; Cabot, M.C. Glycosphingolipids and drug resistance. Biochim. Biophys. Acta (BBA)-Biomembr. 2006, 1758, 2096–2103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- D’Angelo, G.; Capasso, S.; Sticco, L.; Russo, D. Glycosphingolipids: Synthesis and functions. FEBS J. 2013, 280, 6338–6353. [Google Scholar] [CrossRef] [PubMed]
- Ong, E.S.; Zou, L.; Li, S.; Cheah, P.Y.; Eu, K.W.; Ong, C.N. Metabolic profiling in colorectal cancer reveals signature metabolic shifts during tumorigenesis. Mol. Cell. Proteom. 2010, 9, 1–42. [Google Scholar] [CrossRef]
- Sun, S.-Q.; Gu, X.; Gao, X.-S.; Li, Y.; Yu, H.; Xiong, W.; Yu, H.; Wang, W.; Li, Y.; Teng, Y.; et al. Overexpression of AKR1C3 significantly enhances human prostate cancer cells resistance to radiation. Oncotarget 2016, 7, 48050–48058. [Google Scholar] [CrossRef] [Green Version]
- Sales, K.; Boddy, S.C.; Jabbour, H.N. F-prostanoid receptor alters adhesion, morphology and migration of endometrial adenocarcinoma cells. Oncogene 2007, 27, 2466–2477. [Google Scholar] [CrossRef] [Green Version]
- Caiazza, F.; McCarthy, N.S.; Young, L.S.; Hill, A.D.K.; Harvey, B.J.; Thomas, W. Cytosolic phospholipase A2-α expression in breast cancer is associated with EGFR expression and correlates with an adverse prognosis in luminal tumours. Br. J. Cancer 2010, 104, 338–344. [Google Scholar] [CrossRef] [Green Version]
- Godzien, J.; Ciborowski, M.; Angulo-Díaz-Parreño, S.; Barbas, C. From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS. Electrophoresis 2013, 34, 2812–2826. [Google Scholar] [CrossRef]
- Schymanski, E.L.; Jeon, J.; Gulde, R.; Fenner, K.; Ruff, M.; Singer, H.P.; Hollender, J. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environ. Sci. Technol. 2014, 48, 2097–2098. [Google Scholar] [CrossRef] [PubMed]
- Díaz, C.; Del Palacio, J.P.; Valero-Guillén, P.L.; García, P.M.; Pérez, I.; Vicente, F.; Martin, C.; Genilloud, O.; Sanchez-Pozo, A.; Gonzalo-Asensio, J.; et al. Comparative Metabolomics between Mycobacterium tuberculosis and the MTBVAC Vaccine Candidate. ACS Infect. Dis. 2019, 5, 1317–1326. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Warburg, O. On the Origin of Cancer Cells. Science 1956, 123, 309–314. [Google Scholar] [CrossRef]
- Liberti, M.V.; Locasale, J.W. The Warburg Effect: How Does it Benefit Cancer Cells? (vol 41, pg 211, 2016). Trends Biochem. Sci. 2016, 41, 287. [Google Scholar] [CrossRef] [PubMed]
- Eniu, D.T.; Romanciuc, F.; Moraru, C.; Goidescu, I.; Eniu, D.; Staicu, A.; Rachieriu, C.; Buiga, R.; Socaciu, C. The decrease of some serum free amino acids can predict breast cancer diagnosis and progression. Scand. J. Clin. Lab. Investig. 2019, 79, 17–24. [Google Scholar] [CrossRef]
- More, T.H.; Roychoudhury, S.; Christie, J.; Taunk, K.; Mane, A.; Santra, M.K.; Chaudhury, K.; Rapole, S. Metabolomic alterations in invasive ductal carcinoma of breast: A comprehensive metabolomic study using tissue and serum samples. Oncotarget 2017, 9, 2678–2696. [Google Scholar] [CrossRef] [Green Version]
- Cao, Y.; Wang, Q.; Gao, P.; Dong, J.; Zhu, Z.; Fang, Y.; Fang, Z.; Sun, X.; Sun, T. A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection. Onco. Targets Ther. 2016, 9, 1389–1398. [Google Scholar] [CrossRef] [Green Version]
- Miyagi, Y.; Higashiyama, M.; Gochi, A.; Akaike, M.; Ishikawa, T.; Miura, T.; Saruki, N.; Bando, E.; Kimura, H.; Imamura, F.; et al. Plasma Free Amino Acid Profiling of Five Types of Cancer Patients and Its Application for Early Detection. PLoS ONE 2011, 6, e24143. [Google Scholar] [CrossRef] [Green Version]
- Opitz, C.A.; Litzenburger, U.M.; Sahm, F.; Ott, M.; Tritschler, I.; Trump, S.; Schumacher, T.; Jestaedt, L.; Schrenk, D.; Weller, M.; et al. An endogenous tumour-promoting ligand of the human aryl hydrocarbon receptor. Nature 2011, 478, 197–203. [Google Scholar] [CrossRef]
- Grohmann, U. Tolerance, DCs and tryptophan: Much ado about IDO. Trends Immunol. 2003, 24, 242–248. [Google Scholar] [CrossRef]
- Ye, Z.; Yue, L.; Shi, J.; Shao, M.; Wu, T. Role of IDO and TDO in Cancers and Related Diseases and the Therapeutic Implications. J. Cancer 2019, 10, 2771–2782. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cheong, J.E.; Sun, L. Targeting the IDO1/TDO2–KYN–AhR Pathway for Cancer Immunotherapy—Challenges and Opportunities. Trends Pharmacol. Sci. 2018, 39, 307–325. [Google Scholar] [CrossRef] [PubMed]
- Platten, M.; Wick, W.; Van de Eynde, B.J. Tryptophan Catabolism in Cancer: Beyond IDO and Tryptophan Depletion. Cancer Res. 2012, 72, 5435–5440. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhai, L.; Spranger, S.; Binder, D.C.; Gritsina, G.; Lauing, K.L.; Giles, F.J.; Wainwright, D.A. Molecular Pathways: Targeting IDO1 and Other Tryptophan Dioxygenases for Cancer Immunotherapy Lijie. Physiol. Behav. 2015, 176, 139–148. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yu, C.-P.; Song, Y.-L.; Zhu, Z.-M.; Huang, B.; Xiao, Y.-Q.; Luo, D. Targeting TDO in cancer immunotherapy. Med. Oncol. 2017, 34, 73. [Google Scholar] [CrossRef]
- Wei, L.; Zhu, S.; Li, M.; Li, F.; Wei, F.; Liu, J.; Ren, X. High Indoleamine 2,3-Dioxygenase Is Correlated With Microvessel Density and Worse Prognosis in Breast Cancer. Front. Immunol. 2018, 9, 724. [Google Scholar] [CrossRef]
- Mariotti, V.; Han, H.; Ismail-Khan, R.; Tang, S.J.; Dillon, P.; Montero, A.J.; Poklepovic, A.; Melin, S.; Ibrahim, N.K.; Kennedy, E.; et al. Effect of Taxane Chemotherapy With or Without Indoximod in Metastatic Breast Cancer. JAMA Oncol. 2020, 33612, 1–9. [Google Scholar] [CrossRef]
- Günther, J.; Däbritz, J.; Wirthgen, E. Limitations and Off-Target Effects of Tryptophan-Related IDO Inhibitors in Cancer Treatment. Front. Immunol. 2019, 10, 1801. [Google Scholar] [CrossRef]
- Carracedo, A.; Cantley, L.C.; Pandolfi, P.P. Cancer metabolism: Fatty acid oxidation in the limelight. Nat. Rev. Cancer 2013, 13, 227–232. [Google Scholar] [CrossRef]
- Cappelletti, V.; Iorio, E.; Miodini, P.; Silvestri, M.; Dugo, M.; Daidone, M.G. Metabolic Footprints and Molecular Subtypes in Breast Cancer. Dis. Markers 2017, 2017, 7687851-19. [Google Scholar] [CrossRef] [Green Version]
- Zhu, L.; Bakovic, M. Breast cancer cells adapt to metabolic stress by increasing ethanolamine phospholipid synthesis and CTP:ethanolaminephosphate cytidylyltransferase-Pcyt2 activity. Biochem. Cell Biol. 2012, 90, 188–199. [Google Scholar] [CrossRef] [PubMed]
- Osawa, T.; Shimamura, T.; Saito, K.; Hasegawa, Y.; Ishii, N.; Nishida, M.; Ando, R.; Kondo, A.; Anwar, M.; Tsuchida, R.; et al. Phosphoethanolamine Accumulation Protects Cancer Cells under Glutamine Starvation through Downregulation of PCYT2. Cell Rep. 2019, 29, 89–103.e7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- His, M.; Viallon, V.; Dossus, L.; Gicquiau, A.; Achaintre, D.; Scalbert, A.; Ferrari, P.; Romieu, I.; Onland-Moret, N.C.; Weiderpass, E.; et al. Prospective analysis of circulating metabolites and breast cancer in EPIC. BMC Med. 2019, 17, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Vasavda, C.; Kothari, R.; Malla, A.P.; Tokhunts, R.; Lin, A.; Ji, M.; Ricco, C.; Xu, R.; Saavedra, H.G.; Sbodio, J.I.; et al. Bilirubin Links Heme Metabolism to Neuroprotection by Scavenging Superoxide. Cell Chem. Biol. 2019, 26, 1450–1460.e7. [Google Scholar] [CrossRef] [PubMed]
- Xi, X.-X.; Wang, H.-L.; Chen, T.; Dai, J.-R.; Hou, S.-Y.; Chen, Y.-G. Prognostic value of preoperative serum bilirubin levels in ovarian cancer. Am. J. Transl. Res. 2020, 12, 2267–2280. [Google Scholar]
- Nitti, M.; Piras, S.; Marinari, U.M.; Moretta, L.; Pronzato, M.A.; Furfaro, A.L. HO-1 Induction in Cancer Progression: A Matter of Cell Adaptation. Antioxidants 2017, 6, 29. [Google Scholar] [CrossRef]
- Chiang, S.-K.; Chen, S.-E.; Chang, L.-C. A Dual Role of Heme Oxygenase-1 in Cancer Cells. Int. J. Mol. Sci. 2018, 20, 39. [Google Scholar] [CrossRef] [Green Version]
- Canesin, G.; Hejazi, S.M.; Swanson, K.D.; Wegiel, B. Heme-Derived Metabolic Signals Dictate Immune Responses. Front. Immunol. 2020, 11, 66. [Google Scholar] [CrossRef] [Green Version]
- Baker, P.R.; Wilton, J.C.; Jones, C.E.; Stenzel, D.J.; Watson, N.; Smith, G.J. Bile acids influence the growth, oestrogen receptor and oestrogen-regulated proteins of MCF-7 human breast cancer cells. Br. J. Cancer 1992, 65, 566–572. [Google Scholar] [CrossRef] [Green Version]
- Costarelli, V.; Sanders, T.A.B. Plasma bile acids and risk of breast cancer. IARC Sci. Publ. 2002, 156, 305–306. [Google Scholar]
- Tang, W.; Putluri, V.; Ambati, C.R.; Dorsey, T.H.; Putluri, N.; Ambs, S. Liver- and Microbiome-derived Bile Acids Accumulate in Human Breast Tumors and Inhibit Growth and Improve Patient Survival. Clin. Cancer Res. 2019, 25, 5972–5983. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Martín-Blázquez, A.; Díaz, C.; González-Flores, E.; Franco-Rivas, D.; Jiménez-Luna, C.; Melguizo, C.; Prados, J.; Genilloud, O.; Vicente, F.; Caba, O.; et al. Untargeted LC-HRMS-based metabolomics to identify novel biomarkers of metastatic colorectal cancer. Sci. Rep. 2019, 9, 20198–20199. [Google Scholar] [CrossRef] [PubMed]
- Cajka, T.; Fiehn, O. Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics. Anal. Chem. 2016, 88, 524–545. [Google Scholar] [CrossRef] [PubMed]
- Chong, J.; Soufan, O.; Li, C.; Caraus, I.; Li, S.; Bourque, G.; Wishart, D.S.; Xia, J. MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018, 46, W486–W494. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- van den Berg, R.A.; Hoefsloot, H.C.J.; Westerhuis, J.A.; Smilde, A.K.; Van Der Werf, M.J. Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genom. 2006, 7, 142. [Google Scholar] [CrossRef] [Green Version]
- Di Guida, R.; Engel, J.; Allwood, J.W.; Weber, R.J.M.; Jones, M.R.; Sommer, U.; Viant, M.R.; Dunn, W.B. Non-targeted UHPLC-MS metabolomic data processing methods: A comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics 2016, 12, 1–14. [Google Scholar] [CrossRef] [Green Version]
- Alonso, A.; Marsal, S.; Julià, A. Analytical Methods in Untargeted Metabolomics: State of the Art in 2015. Front. Bioeng. Biotechnol. 2015, 3, 23. [Google Scholar] [CrossRef] [Green Version]
- Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate—A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B-Methodol. 1995, 57, 289–300. [Google Scholar] [CrossRef]
- Naz, S.; Vallejo, M.; García, A.; Barbas, C. Method validation strategies involved in non-targeted metabolomics. J. Chromatogr. A 2014, 1353, 99–105. [Google Scholar] [CrossRef]
- Guijas, C.; Montenegro-Burke, J.R.; Domingo-Almenara, X.; Palermo, A.; Warth, B.; Hermann, G.; Koellensperger, G.; Huan, T.; Uritboonthai, W.; Aisporna, A.E.; et al. METLIN: A Technology Platform for Identifying Knowns and Unknowns. Anal. Chem. 2018, 90, 3156–3164. [Google Scholar] [CrossRef] [Green Version]
- Wishart, D.S.; Feunang, Y.D.; Marcu, A.; Guo, A.C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Allison, P.; Karu, N.; et al. HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Res. 2018, 46, D608–D617. [Google Scholar] [CrossRef] [PubMed]
- Fahy, E.; Sud, M.; Cotter, D.; Subramaniam, S. LIPID MAPS online tools for lipid research. Nucleic Acids Res. 2007, 35, W606–W612. [Google Scholar] [CrossRef] [Green Version]
- Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.; Yu, B.; et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res. 2019, 47, D1102–D1109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Horai, H.; Arita, M.; Kanaya, S.; Nihei, Y.; Ikeda, T.; Suwa, K.; Ojima, Y.; Tanaka, K.; Tanaka, S.; Aoshima, K.; et al. MassBank: A public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 2010, 45, 703–714. [Google Scholar] [CrossRef] [PubMed]
- Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27, 29–34. [Google Scholar] [CrossRef] [Green Version]
BC Molecular Subtype | Tentative ID | m/z | RT | Mass Error (ppm) | p (FDR) | FC * (BC/HC) | Adduct | Molecular Formula |
---|---|---|---|---|---|---|---|---|
ESI+ | ||||||||
LB | LysoPE(18:2) | 478.2916 | 11.34 | −2.5 | 1.670 × 10−8 | 0.6008 | [M+H] | C23H44NO7P |
LysoPE(18:1(11Z/9Z)) | 480.3108 | 12.15 | 4.8 | 5.365 × 10−3 | 0.6303 | [M+H] | C23H46NO7P | |
LysoPE(18:1(11Z/9Z)) | 480.3073 | 12.47 | −2.5 | 9.058 × 10−10 | 0.4713 | [M+H] | C23H46NO7P | |
LysoPC(20:3) | 546.3539 | 12.16 | −2.7 | 2.214 × 10−2 | 0.7303 | [M+H] | C28H52NO7P | |
Biliverdin | 583.2566 | 8.95 | 2.6 | 7.390 × 10−9 | 1.5681 | [M+H] | C33H34N4O6 | |
LA | L-Tryptophan 1 | 188.0707 | 3.73 | 0.5 | 2.503 × 10−2 | 0.6362 | [M+H-NH3] | C11H12N2O2 |
LysoPC(14:0) | 468.3084 | 9.66 | −0.2 | 3.745 × 10−2 | 0.5849 | [M+H] | C22H46NO7P | |
HER2 | LysoPE(18:1(11Z)/9Z) | 480.3109 | 12.31 | 5 | 6.192 × 10−3 | 0.6407 | [M+H] | C23H46NO7P |
LysoPC(0:0/16:0) | 496.3411 | 11.71 | 2.6 | 6.396 × 10−6 | 0.6701 | [M+H] | C24H50NO7P | |
Biliverdin | 583.2525 | 8.65 | −4.5 | 2.0621 × 10−6 | 1.6265579 | [M+H] | C33H34N4O6 | |
TN | L-Tryptophan 1 | 188.0702 | 3.4 | 2.1 | 4.153 × 10−2 | 0.625911 | [M+H-NH3] | C11H12N2O2 |
LysoPC(16:0/0:0) | 518.3224 | 10.07 | 1.3 | 0.03043 | 0.5289669 | [M+Na] | C24H50NO7P | |
LB | ESI− | |||||||
LysoPE(16:0) | 452.2796 | 5.71 | 2.9 | 5.427 × 10−14 | 0.5342 | [M-H-H2O] | C21H44NO7P | |
LysoPE(18:2) | 476.2804 | 5.59 | 4.4 | 1.304 × 10−8 | 0.5498 | [M-H] | C23H44NO7P | |
LA | L-Tryptophan 2 | 203.0824 | 1.27 | −1 | 1.637 × 10−2 | 0.6543 | [M-H] | C11H12N2O2 |
Glycoursodeoxycholic acid 3 | 448.3066 | 3.24 | −0.4 | 2.861 × 10−2 | 0.5646 | [M-H] | C18H34O4 | |
LysoPE(18:2) | 476.2766 | 5 | −3.6 | 3.489 × 10−2 | 0.6711 | [M-H] | C23H44NO7P | |
HER2 | L-Tryptophan 2 | 203.0836 | 1 | 4.9 | 7.536 × 10−5 | 0.6744 | [M-H] | C11H12N2O2 |
LysoPE(18:2) | 514.2381 | 5.5 | 7.8 | 3.403 × 10−4 | 0.6408 | [M+K-2H] | C23H44NO7P | |
TN | LysoPE(18:1(11Z)/9Z) | 957.5976 | 5.86 | 2.6 | 0.027908 | 0.4407772 | [2M-H] | C23H46NO7P |
BC Molecular Subtype | BM | AUC | 95% CI | Confusion Matrix | |
---|---|---|---|---|---|
BC | HC | ||||
LA | 5 | 0.87 | 0.651–0.992 | 14/20 | 16/21 |
HER2 | 5 | 0.919 | 0.819–0.985 | 26/31 | 28/34 |
TN | 3 | 0.961 | 0.8–1 | 13/15 | 14/15 |
LB | 7 | 0.954 | 0.886–0.995 | 50/56 | 54/62 |
Altered Pathways | BC Molecular Subtype | p-Value |
---|---|---|
Porphyrin and chlorophyll metabolism | LB and HER2 | 0.038347 |
Glycerophospholipid metabolism | LA, LB, TN and HER2 | 0.045927 |
Characteristics | LB | HC | LA | HC | TN | HC | HER2 | HC |
---|---|---|---|---|---|---|---|---|
Subjects | 61 | 64 | 21 | 21 | 15 | 15 | 34 | 34 |
Age (Range) | 49 (27–75) | 50 (42–56) | 50 (32–81) | 49 (34–60) | 49 (29–71) | 51 (26–63) | 51 (33–70) | 49 (28–62) |
BMI (Kg·m−2) | 25.63 (16.9–40.5) | 25.35 (19.8–30.0) | 24.90 (20.0–37.2) | 25.00 (18.0–28.3) | 27.60 (21.60–41.23) | 26.5 (21.3–30.0) | 26.10 (21.0–33.3) | 25.30 (20.80–29.80) |
HER2 | Negative | - | Negative | - | Negative | - | Positive | - |
PR | Neg/Pos | - | Neg/Pos | - | Negative | - | Neg/Pos | - |
ER | Positive | - | Positive | - | Negative | - | Neg/Pos | - |
Ki67 | >20% | - | <20% | - | - | - | - | - |
TNM-stage IA | 0 | - | 1 | - | 0 | - | 1 | - |
TNM-stage IIA | 26 | - | 10 | - | 9 | - | 9 | - |
TNM-stage IIIA | 12 | - | 0 | - | 0 | - | 3 | - |
TNM-stage IIB | 19 | - | 9 | - | 3 | - | 19 | - |
TNM-stage IIIB | 2 | - | 1 | - | 2 | - | 1 | - |
TNM-stage IC | 2 | - | 0 | - | 1 | - | 1 | - |
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Díaz-Beltrán, L.; González-Olmedo, C.; Luque-Caro, N.; Díaz, C.; Martín-Blázquez, A.; Fernández-Navarro, M.; Ortega-Granados, A.L.; Gálvez-Montosa, F.; Vicente, F.; Pérez del Palacio, J.; et al. Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer. Cancers 2021, 13, 147. https://doi.org/10.3390/cancers13010147
Díaz-Beltrán L, González-Olmedo C, Luque-Caro N, Díaz C, Martín-Blázquez A, Fernández-Navarro M, Ortega-Granados AL, Gálvez-Montosa F, Vicente F, Pérez del Palacio J, et al. Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer. Cancers. 2021; 13(1):147. https://doi.org/10.3390/cancers13010147
Chicago/Turabian StyleDíaz-Beltrán, Leticia, Carmen González-Olmedo, Natalia Luque-Caro, Caridad Díaz, Ariadna Martín-Blázquez, Mónica Fernández-Navarro, Ana Laura Ortega-Granados, Fernando Gálvez-Montosa, Francisca Vicente, José Pérez del Palacio, and et al. 2021. "Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer" Cancers 13, no. 1: 147. https://doi.org/10.3390/cancers13010147