Serum metabolite and metal ions profiles for breast cancer screening.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 Oct 2024
19 Oct 2024
Historique:
received:
15
05
2024
accepted:
13
09
2024
medline:
20
10
2024
pubmed:
20
10
2024
entrez:
19
10
2024
Statut:
epublish
Résumé
Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types-DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients' metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.
Identifiants
pubmed: 39426973
doi: 10.1038/s41598-024-73097-1
pii: 10.1038/s41598-024-73097-1
doi:
Substances chimiques
Metals
0
Biomarkers, Tumor
0
Ions
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24559Subventions
Organisme : Politechnika Wrocławska
ID : WCB KNOW 2014-2018
Informations de copyright
© 2024. The Author(s).
Références
Didkowska Joanna, W. & Urszula, O. P. Nowotwory złośliwe w Polsce w 2015 roku (Ministerstwo zdrowia, 2017).
Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 69, 7–34 (2019).
pubmed: 30620402
doi: 10.3322/caac.21551
Jassem, J. & Krzakowski, M. Rak piersi, Praktyczny przewodnik dla lekarzy. (2014).
Chen, L., Linden, H. M., Anderson, B. O. & Li, C. I. Trends in 5-year survival rates among breast cancer patients by hormone receptor status and stage. Breast Cancer Res. Treat. 147, 609–616 (2014).
pubmed: 25164974
pmcid: 4174984
doi: 10.1007/s10549-014-3112-6
Cao, S. S. & Lu, C. T. Recent perspectives of breast cancer prognosis and predictive factors (review). Oncol. Lett. 12, 3674–3678 (2016).
pubmed: 27900052
pmcid: 5104147
doi: 10.3892/ol.2016.5149
Fuller, M. S., Lee, C. I. & Elmore, J. G. Breast cancer screening: an evidence-based update. Med. Clin. North. Am. 99, 451–468 (2015).
pubmed: 25841594
pmcid: 5064844
doi: 10.1016/j.mcna.2015.01.002
Seely, J. M. & Alhassan, T. Screening for breast cancer in 2018—what should we be doing today? Curr. Oncol. 25, S115–S124 (2018).
pubmed: 29910654
pmcid: 6001765
doi: 10.3747/co.25.3770
Bujak, R., Struck-Lewicka, W., Markuszewski, M. J. & Kaliszan, R. Metabolomics for laboratory diagnostics. J. Pharm. Biomed. Anal. 113, 108–120 (2015).
pubmed: 25577715
doi: 10.1016/j.jpba.2014.12.017
Zhang, A., Sun, H., Yan, G., Wang, P. & Wang, X. Metabolomics for biomarker discovery: moving to the clinic. Biomed Res. Int. (2015).
Gribbestad, I. S. et al. In vitro proton NMR spectroscopy of extracts from human breast tumours and non-involved breast tissue. Anticancer Res. 13, 1973–1980 (1993).
pubmed: 8297103
Asiago, V. M. et al. Early detection of recurrent breast cancer using metabolite profiling. Cancer Res. 70, 8309–8318 (2010).
pubmed: 20959483
pmcid: 2995269
doi: 10.1158/0008-5472.CAN-10-1319
Günther, U. L. Metabolomics biomarkers for breast cancer. Pathobiology. 82, 153–165 (2015).
pubmed: 26330356
doi: 10.1159/000430844
McCartney, A. et al. Metabolomics in breast cancer: a decade in review. Cancer Treat. Rev. 67, 88–96 (2018).
pubmed: 29775779
doi: 10.1016/j.ctrv.2018.04.012
Vignoli, A. et al. Precision oncology via NMR-based metabolomics: a review on breast cancer. Int. J. Mol. Sci. 22 (2021).
Keun, H. C. et al. Serum molecular signatures of weight change during early breast cancer chemotherapy. Clin. cancer Res. Off J. Am. Assoc. Cancer Res. 15, 6716–6723 (2009).
doi: 10.1158/1078-0432.CCR-09-1452
Tenori, L. et al. Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study. Mol. Oncol. 9, 128–139 (2015).
pubmed: 25151299
doi: 10.1016/j.molonc.2014.07.012
Richard, V., Conotte, R., Mayne, D. & Colet, J. M. Does the 1H-NMR plasma metabolome reflect the host-tumor interactions in human breast cancer? Oncotarget. 8, 49915–49930 (2017).
pubmed: 28611296
pmcid: 5564817
doi: 10.18632/oncotarget.18307
Suman, S., Sharma, R. K., Kumar, V., Sinha, N. & Shukla, Y. Metabolic fingerprinting in breast cancer stages through 1H NMR spectroscopy-based metabolomic analysis of plasma. J. Pharm. Biomed. Anal. 160, 38–45 (2018).
pubmed: 30059813
doi: 10.1016/j.jpba.2018.07.024
Hart, C. D. et al. Serum metabolomic profiles identify ER-Positive early breast cancer patients at increased risk of disease recurrence in a multicenter population. Clin. Cancer Res. 23, 1422–1431 (2017).
pubmed: 28082280
pmcid: 5695865
doi: 10.1158/1078-0432.CCR-16-1153
Jobard, E. et al. Longitudinal serum metabolomics evaluation of trastuzumab and everolimus combination as pre-operative treatment for HER-2 positive breast cancer patients. Oncotarget. 8, 83570–83584 (2017).
pubmed: 29137365
pmcid: 5663537
doi: 10.18632/oncotarget.18784
Singh, A. et al. 1H NMR metabolomics reveals association of high expression of inositol 1, 4, 5 trisphosphate receptor and metabolites in breast cancer patients. PLoS One. 12, e0169330 (2017).
pubmed: 28072864
pmcid: 5225010
doi: 10.1371/journal.pone.0169330
Zhou, J., Wang, Y. & Zhang, X. Metabonomics studies on serum and urine of patients with breast cancer using 1 H-NMR spectroscopy. Oncotarget. 4 (2017).
Cala, M. P. et al. Multiplatform plasma metabolic and lipid fingerprinting of breast cancer: a pilot control-case study in Colombian hispanic women. PLoS One. 13, e0190958–e0190958 (2018).
pubmed: 29438405
pmcid: 5810980
doi: 10.1371/journal.pone.0190958
Lécuyer, L. et al. NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer. Int. J. Epidemiol. 47, 484–494 (2018).
pubmed: 29365091
doi: 10.1093/ije/dyx271
Jiang, L., Lee, S. C. & Ng, T. C. Pharmacometabonomics analysis reveals serum formate and acetate potentially associated with varying response to gemcitabine-carboplatin chemotherapy in metastatic breast cancer patients. J. Proteome Res. 17, 1248–1257 (2018).
pubmed: 29385795
doi: 10.1021/acs.jproteome.7b00859
Gu, H. et al. Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer. Anal. Chim. Acta. 686, 57–63 (2011).
pubmed: 21237308
doi: 10.1016/j.aca.2010.11.040
Debik, J. et al. Assessing treatment response and prognosis by serum and tissue metabolomics in breast cancer patients. J. Proteome Res. 18, 3649–3660 (2019).
pubmed: 31483662
doi: 10.1021/acs.jproteome.9b00316
McCartney, A. et al. Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification. NPJ Breast Cancer. 5, 26 (2019).
pubmed: 31482106
pmcid: 6715716
doi: 10.1038/s41523-019-0123-9
Vignoli, A. et al. Effect of estrogen receptor status on circulatory immune and metabolomics profiles of HER2-Positive breast cancer patients enrolled for Neoadjuvant targeted chemotherapy. Cancers (Basel) 12, (2020).
Jobard, E. et al. Investigation of circulating metabolites associated with breast cancer risk by untargeted metabolomics: a case-control study nested within the French E3N cohort. Br. J. Cancer. 124, 1734–1743 (2021).
pubmed: 33723391
pmcid: 8110540
doi: 10.1038/s41416-021-01304-1
Oakman, C. et al. Identification of a serum-detectable metabolomic fingerprint potentially correlated with the presence of micrometastatic disease in early breast cancer patients at varying risks of disease relapse by traditional prognostic methods. Ann. Oncol. Off J. Eur. Soc. Med. Oncol. 22, 1295–1301 (2011).
doi: 10.1093/annonc/mdq606
Stebbing, J. et al. A metabolic phenotyping approach to understanding relationships between metabolic syndrome and breast tumour responses to chemotherapy. Ann. Oncol. Off J. Eur. Soc. Med. Oncol. 23, 860–866 (2012).
doi: 10.1093/annonc/mdr347
Tenori, L. et al. Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: a pilot study. Mol. Oncol. 6, 437–444 (2012).
pubmed: 22687601
pmcid: 5528357
doi: 10.1016/j.molonc.2012.05.003
Wei, S. et al. Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer. Mol. Oncol. 7, 297–307 (2013).
pubmed: 23142658
doi: 10.1016/j.molonc.2012.10.003
Jobard, E. et al. A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer. Cancer Lett. 343, 33–41 (2014).
pubmed: 24041867
doi: 10.1016/j.canlet.2013.09.011
Bro, R. et al. Forecasting individual breast cancer risk using plasma metabolomics and biocontours. Metabolomics. 11, 1376–1380 (2015).
pubmed: 26366139
pmcid: 4559100
doi: 10.1007/s11306-015-0793-8
Louis, E. et al. Phenotyping human blood plasma by 1H-NMR: a robust protocol based on metabolite spiking and its evaluation in breast cancer. Metabolomics. 11, 225–236 (2015).
doi: 10.1007/s11306-014-0690-6
Ulrich, E. L. et al. BioMagResBank. Nucleic Acids Res. 36, D402–D408 (2007).
pubmed: 17984079
pmcid: 2238925
doi: 10.1093/nar/gkm957
Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).
pubmed: 29140435
doi: 10.1093/nar/gkx1089
Tomasi, G., van den Berg, F. & Andersson, C. Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. J. Chemom. 18, 231–241 (2004).
doi: 10.1002/cem.859
Savorani, F., Tomasi, G. & Engelsen, S. B. Icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J. Magn. Reson. 202, 190–202 (2010).
pubmed: 20004603
doi: 10.1016/j.jmr.2009.11.012
Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78, 4281–4290 (2006).
pubmed: 16808434
doi: 10.1021/ac051632c
Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).
pubmed: 34019663
pmcid: 8265181
doi: 10.1093/nar/gkab382
Daszykowski, M., Walczak, B. & Massart, D. L. Representative subset selection. Anal. Chim. Acta. 468, 91–103 (2002).
doi: 10.1016/S0003-2670(02)00651-7
Ghoncheh, M., Pournamdar, Z. & Salehiniya, H. Incidence and mortality and epidemiology of breast cancer in the World. Asian Pac. J. Cancer Prev. 17, 43–46 (2016).
pubmed: 27165206
doi: 10.7314/APJCP.2016.17.S3.43
Panyard, D. J., Yu, B. & Snyder, M. P. The metabolomics of human aging: advances, challenges, and opportunities. Sci. Adv. 8, eadd6155 (2023).
doi: 10.1126/sciadv.add6155
Marciniak, W. et al. Blood arsenic levels and the risk of familial breast cancer in Poland. Int. J. cancer. 146, 2721–2727 (2020).
pubmed: 31348523
doi: 10.1002/ijc.32595
Pavithra, V. et al. Serum levels of metal ions in female patients with breast cancer. J. Clin. Diagn. Res. 9, BC25–c27 (2015).
pubmed: 25737978
pmcid: 4347069
Almquist, M., Manjer, J., Bondeson, L. & Bondeson, A. G. Serum calcium and breast cancer risk: results from a prospective cohort study of 7847 women. Cancer Causes Control. 18, 595–602 (2007).
pubmed: 17410477
doi: 10.1007/s10552-007-9001-0
Fontelles, C. C. & Ong, T. P. Selenium and breast cancer risk: focus on cellular and molecular mechanisms. Adv. Cancer Res. 136, 173–192 (2017).
pubmed: 29054418
doi: 10.1016/bs.acr.2017.08.001
Demircan, K. et al. Serum selenium, selenoprotein P and glutathione peroxidase 3 as predictors of mortality and recurrence following breast cancer diagnosis: a multicentre cohort study. Redox Biol. 47, 102145 (2021).
pubmed: 34563873
pmcid: 8476451
doi: 10.1016/j.redox.2021.102145
Sandsveden, M., Nilsson, E., Borgquist, S., Rosendahl, A. H. & Manjer, J. Prediagnostic serum selenium levels in relation to breast cancer survival and tumor characteristics. Int. J. Cancer. 147, 2424–2436 (2020).
pubmed: 32378183
doi: 10.1002/ijc.33031
Szwiec, M. et al. Serum selenium level predicts 10-year survival after breast cancer. Nutrients. 13 (2021).
Lopez-Saez, J. B. & Senra-Varela, A., Pousa-Estevez, L. Selenium in breast cancer. Oncology. 64, 227–231 (2003).
pubmed: 12697962
doi: 10.1159/000069312
Jouybari, L. et al. A meta-analysis of zinc levels in breast cancer. J. Trace Elem. Med. Biol. 56, 90–99 (2019).
pubmed: 31442959
doi: 10.1016/j.jtemb.2019.06.017
Feng, Y. et al. Serum copper and zinc levels in breast cancer: a meta-analysis. J. Trace Elem. Med. Biol. 62, 126629 (2020).
pubmed: 32745979
doi: 10.1016/j.jtemb.2020.126629
Wu, X., Tang, J. & Xie, M. Serum and hair zinc levels in breast cancer: a meta-analysis. Sci. Rep. 5, 12249 (2015).
pubmed: 26179508
pmcid: 4503961
doi: 10.1038/srep12249
Bengtsson, Y., Sandsveden, M., Borgquist, S. & Manjer, J. Serum zinc and dietary intake of zinc in relation to risk of different breast cancer subgroups and serum levels as a marker of intake: a prospective nested case-control study. Breast Cancer Res. Treat. 189, 571–583 (2021).
pubmed: 34224055
pmcid: 8357733
doi: 10.1007/s10549-021-06318-0
Qu, Z. et al. A systematic study on zinc-related metabolism in breast cancer. Nutrients. 15 (2023).
Alam, S. & Kelleher, S. L. Cellular mechanisms of zinc dysregulation: a perspective on zinc homeostasis as an etiological factor in the development and progression of breast cancer. Nutrients. 4, 875–903 (2012).
pubmed: 23016122
pmcid: 3448077
doi: 10.3390/nu4080875
Hart, C. D., Tenori, L. & Luchinat, C. Di Leo, A. Metabolomics in breast cancer: current status and perspectives. Adv. Exp. Med. Biol. 882, 217–234 (2016).
pubmed: 26987537
doi: 10.1007/978-3-319-22909-6_9
Jové, M. et al. A plasma metabolomic signature discloses human breast cancer. Oncotarget. 8, 12 (2017).
Gu, Y. et al. Perioperative dynamics and significance of amino acid profiles in patients with cancer. J. Transl.Med. 13, 35 (2015).
pubmed: 25622826
pmcid: 4332895
doi: 10.1186/s12967-015-0408-1
Eniu, D. T. et al. The decrease of some serum free amino acids can predict breast cancer diagnosis and progression. Scand. J. Clin. Lab. Investig. 79, 17–24 (2019).
pubmed: 30880483
doi: 10.1080/00365513.2018.1542541
Bonuccelli, G. et al. Ketones and lactate ‘fuel’ tumor growth and metastasis: evidence that epithelial cancer cells use oxidative mitochondrial metabolism. Cell. Cycle. 9, 3506–3514 (2010).
pubmed: 20818174
pmcid: 3047616
doi: 10.4161/cc.9.17.12731
Yaffe, M. J. & Pritchard, K. I. Overdiagn. Overdiagn. Oncol. 19, 103–106 (2014).
pubmed: 24536050
doi: 10.1634/theoncologist.2014-0036
Ward, E. M. et al. Cancer statistics: breast cancer in situ. CA Cancer J. Clin. 65, 481–495 (2015).
pubmed: 26431342
doi: 10.3322/caac.21321
Duffy, S. W. et al. Screen detection of ductal carcinoma in situ and subsequent incidence of invasive interval breast cancers: a retrospective population-based study. Lancet Oncol. 17, 109–114 (2016).
pubmed: 26655422
pmcid: 4691349
doi: 10.1016/S1470-2045(15)00446-5
Zarghami, N. et al. Creatine kinase BB isoenzyme levels in tumour cytosols and survival of breast cancer patients. Br. J. Cancer. 73, 386–390 (1996).
pubmed: 8562347
pmcid: 2074443
doi: 10.1038/bjc.1996.66
More, T. H. et al. Metabolomic alterations in invasive ductal carcinoma of breast: a comprehensive metabolomic study using tissue and serum samples. Oncotarget. 9, 2 (2017).