Association of altered metabolic profiles and long non-coding RNAs expression with disease severity in breast cancer patients: analysis by
1H NMR spectroscopy
Breast cancer
Diagnosis
LncRNA
Metabolomics
Multivariate analysis
Prognosis
Journal
Metabolomics : Official journal of the Metabolomic Society
ISSN: 1573-3890
Titre abrégé: Metabolomics
Pays: United States
ID NLM: 101274889
Informations de publication
Date de publication:
30 01 2023
30 01 2023
Historique:
received:
28
08
2022
accepted:
12
01
2023
entrez:
29
1
2023
pubmed:
30
1
2023
medline:
1
2
2023
Statut:
epublish
Résumé
Globally, one of the major causes of cancer related deaths in women is breast cancer. Although metabolic pattern is altered in cancer patients, robust metabolic biomarkers with a potential to improve the screening and disease monitoring are lacking. A complete metabolome profiling of breast cancer patients may lead to the identification of diagnostic/prognostic markers and potential targets. The aim of this study was to analyze the metabolic profile in the serum from 43 breast cancer patients and 13 healthy individuals. We used Metabolites such as amino acids, lipids, membrane metabolites, lipoproteins, and energy metabolites were observed in the serum from both patients and healthy individuals. Using unsupervised PCA, supervised PLS-DA, supervised OPLS-DA, and random forest classification, we observed that more than 25 metabolites were altered in the breast cancer patients. Metabolites with AUC value > 0.9 were selected for further analysis that revealed significant elevation of lactate, LPR and glycerol, while the level of glucose, succinate, and isobutyrate was reduced in breast cancer patients in comparison to healthy control. The level of these metabolites (except LPR) was altered in advanced-stage breast cancer patients in comparison to early-stage breast cancer patients. The altered metabolites were also associated with over 25 signaling pathways related to metabolism. Further, lncRNAs such as H19, MEG3 and GAS5 were dysregulated in the breast tumor tissue in comparison to normal adjacent tissue. The study provides insights into metabolic alteration in breast cancer patients. It also provides an avenue to examine the association of lncRNAs with metabolic patterns in patients.
Identifiants
pubmed: 36710275
doi: 10.1007/s11306-023-01972-5
pii: 10.1007/s11306-023-01972-5
doi:
Substances chimiques
RNA, Long Noncoding
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
8Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Awasthee, N., Rai, V., Verma, S. S., Francis, K. S., Nair, M. S., & Gupta, S. C. (2018). Anti-cancer activities of Bharangin against breast cancer: Evidence for the role of NF-κB and lncRNAs. Biochimica et Biophysica Acta (BBA)-General Subjects, 1862, 2738–2749.
pubmed: 30251663
doi: 10.1016/j.bbagen.2018.08.016
Bhandari, P. M., Thapa, K., Dhakal, S., Bhochhibhoya, S., Deuja, R., Acharya, P., & Mishra, S. R. (2016). Breast cancer literacy among higher secondary students: Results from a cross-sectional study in Western Nepal. BMC cancer, 16, 1–9.
doi: 10.1186/s12885-016-2166-8
Bonuccelli, G., Tsirigos, A., Whitaker-Menezes, D., Pavlides, S., Pestell, R. G., Chiavarina, B., Frank, P. G., Flomenberg, N., Howell, A., Martinez-Outschoorn, U. E., & Sotgia, F. (2010). Ketones and lactate “fuel” tumor growth and metastasis: Evidence that epithelial cancer cells use oxidative mitochondrial metabolism. Cell Cycle, 9, 3506–3514.
pubmed: 20818174
pmcid: 3047616
doi: 10.4161/cc.9.17.12731
Cassim, S., & Pouyssegur, J. (2019). Tumor microenvironment: A metabolic player that shapes the immune response. International Journal of Molecular Sciences, 21, 157.
pubmed: 31881671
pmcid: 6982275
doi: 10.3390/ijms21010157
Chandra Gupta, S., & Nandan Tripathi, Y. (2017). Potential of long non-coding RNAs in cancer patients: From biomarkers to therapeutic targets. International Journal of Cancer, 140, 1955–1967.
pubmed: 27925173
doi: 10.1002/ijc.30546
Chen, Y., Zhang, R., Song, Y., He, J., Sun, J., Bai, J., An, Z., Dong, L., Zhan, Q., & Abliz, Z. (2009). RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: Finding potential biomarkers for breast cancer. The Analyst, 134, 2003–2011.
pubmed: 19768207
doi: 10.1039/b907243h
Chong, J., Wishart, D. S., & Xia, J. (2019). Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Current Protocols in Bioinformatics, 68, e89.
doi: 10.1002/cpbi.86
Chong, J., & Xia, J. (2020). Using MetaboAnalyst 4.0 for metabolomics data analysis, interpretation, and integration with other omics data. Computational Methods and Data Analysis for Metabolomics, 2104, 337–360.
doi: 10.1007/978-1-0716-0239-3_17
DeFeo, E. M., Wu, C. L., McDougal, W. S., & Cheng, L. L. (2011). A decade in prostate cancer: from NMR to metabolomics. Nature Reviews Urology, 8, 301–311.
pubmed: 21587223
doi: 10.1038/nrurol.2011.53
Deja, S., Porebska, I., Kowal, A., Zabek, A., Barg, W., Pawelczyk, K., Stanimirova, I., Daszykowski, M., Korzeniewska, A., Jankowska, R., & Mlynarz, P. (2014). Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. Journal of Pharmaceutical and Biomedical Analysis, 100, 369–380.
pubmed: 25213261
doi: 10.1016/j.jpba.2014.08.020
Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., & Bray, F. (2021). Cancer statistics for the year 2020: An overview. International Journal of Cancer, 149, 778–789.
doi: 10.1002/ijc.33588
Garcia, E., Andrews, C., Hua, J., Kim, H. L., Sukumaran, D. K., Szyperski, T., & Odunsi, K. (2011). Diagnosis of early stage ovarian cancer by 1H NMR metabonomics of serum explored by use of a microflow NMR probe. Journal of Proteome Research, 10, 1765–1771.
pubmed: 21218854
pmcid: 3074977
doi: 10.1021/pr101050d
Gordon, F. E., Nutt, C. L., Cheunsuchon, P., Nakayama, Y., Provencher, K. A., Rice, K. A., Zhou, Y., Zhang, X., & Klibanski, A. (2010). Increased expression of angiogenic genes in the brains of mouse meg3-null embryos. Endocrinology, 151, 2443–2452.
pubmed: 20392836
pmcid: 2875815
doi: 10.1210/en.2009-1151
Gowda, G. N., & Raftery, D. (2015). Can NMR solve some significant challenges in metabolomics? Journal of Magnetic Resonance, 260, 144–160.
pmcid: 4646661
doi: 10.1016/j.jmr.2015.07.014
Gu, H., Pan, Z., Xi, B., Asiago, V., Musselman, B., & Raftery, D. (2011). 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. Analytica chimica acta, 686, 57–63.
pubmed: 21237308
doi: 10.1016/j.aca.2010.11.040
Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144, 646–674.
pubmed: 21376230
doi: 10.1016/j.cell.2011.02.013
Hirschhaeuser, F., Sattler, U. G., & Mueller-Klieser, W. (2011). Lactate: A metabolic key player in cancer. Cancer Research, 71, 6921–6925.
pubmed: 22084445
doi: 10.1158/0008-5472.CAN-11-1457
Hung, C. L., Wang, L. Y., Yu, Y. L., Chen, H. W., Srivastava, S., Petrovics, G., & Kung, H. J. (2014). A long noncoding RNA connects c-Myc to tumor metabolism. Proceedings of the National Academy of Sciences, 111, 18697–18702.
doi: 10.1073/pnas.1415669112
Jiang, T., Lin, Y., Yin, H., Wang, S., Sun, Q., Zhang, P., & Bi, W. (2015). Correlation analysis of urine metabolites and clinical staging in patients with ovarian cancer. International Journal of Clinical and Experimental Medicine, 8, 18165.
pubmed: 26770415
pmcid: 4694315
Jin, H., Du, W., Huang, W., Yan, J., Tang, Q., Chen, Y., & Zou, Z. (2021). lncRNA and breast cancer: Progress from identifying mechanisms to challenges and opportunities of clinical treatment. Molecular Therapy-Nucleic Acids, 25, 613–637.
pubmed: 34589282
pmcid: 8463317
doi: 10.1016/j.omtn.2021.08.005
Jobard, E., Pontoizeau, C., Blaise, B. J., Bachelot, T., Elena-Herrmann, B., & Trédan, O. (2014). A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer. Cancer Letters, 343, 33–41.
pubmed: 24041867
doi: 10.1016/j.canlet.2013.09.011
Kalyanaraman, B. (2017). Teaching the basics of cancer metabolism: developing antitumor strategies by exploiting the differences between normal and cancer cell metabolism. Redox Biology, 12, 833–842.
pubmed: 28448945
pmcid: 5406543
doi: 10.1016/j.redox.2017.04.018
Kumar, U., Jain, A., Guleria, A., Misra, D. P., Goel, R., Danda, D., Misra, R., & Kumar, D. (2020). Circulatory glutamine/glucose ratio for evaluating disease activity in Takayasu arteritis: A NMR based serum metabolomics study. Journal of Pharmaceutical and Biomedical Analysis, 180, 113080.
pubmed: 31896520
doi: 10.1016/j.jpba.2019.113080
Kumar, U., Kumar, A., Singh, S., Arya, P., Singh, S. K., Chaurasia, R. N., Singh, A., & Kumar, D. (2021). An elaborative NMR based plasma metabolomics study revealed metabolic derangements in patients with mild cognitive impairment: A study on north indian population. Metabolic Brain Disease, 36, 957–968.
pubmed: 33651272
doi: 10.1007/s11011-021-00700-z
Kusum, K., Raj, R., Rai, S., Pranjali, P., Ashish, A., & Vicente-Muñoz S, Chaube R, Kumar D,. (2022). Elevated circulatory proline to glutamine ratio (PQR) in endometriosis and its potential as a diagnostic biomarker. ACS Omega, 7, 14856–14866.
pubmed: 35557708
pmcid: 9088897
doi: 10.1021/acsomega.2c00332
Lan, X., Sun, W., Dong, W., Wang, Z., Zhang, T., He, L., & Zhang, H. (2018). Downregulation of long noncoding RNA H19 contributes to the proliferation and migration of papillary thyroid carcinoma. Gene, 646, 98–105.
pubmed: 29287713
doi: 10.1016/j.gene.2017.12.051
Li, H., Li, J., Jia, S., Wu, M., An, J., Zheng, Q., Zhang, W., & Lu, D. (2015). miR675 upregulates long noncoding RNA H19 through activating EGR1 in human liver cancer. Oncotarget, 6, 31958.
pubmed: 26376677
pmcid: 4741653
doi: 10.18632/oncotarget.5579
Li, Q., Cao, L., Tian, Y., Zhang, P., Ding, C., Lu, W., Jia, C., Shao, C., Liu, W., Wang, D., & Ye, H. (2018). Butyrate suppresses the proliferation of colorectal cancer cells via targeting pyruvate kinase M2 and metabolic reprogramming. Molecular & Cellular Proteomics, 17, 1531–1545.
doi: 10.1074/mcp.RA118.000752
Lin, W., Zhou, Q., Wang, C. Q., Zhu, L., Bi, C., Zhang, S., Wang, X., & Jin, H. (2020). LncRNAs regulate metabolism in cancer. International Journal of Biological Sciences, 16, 1194.
pubmed: 32174794
pmcid: 7053319
doi: 10.7150/ijbs.40769
Malhotra, P., Sidhu, L., & Singh, S. (1986). Serum lactate dehydrogenase level in various malignancies. Neoplasma, 33, 641–647.
pubmed: 3785469
Momtazmanesh, S., & Rezaei, N. (2021). Long non-coding RNAs in diagnosis, treatment, prognosis, and progression of glioma: A state-of-the-art review. Frontiers in oncology, 11, 712786.
pubmed: 34322395
pmcid: 8311560
doi: 10.3389/fonc.2021.712786
Nishiumi, S., Kobayashi, T., Ikeda, A., Yoshie, T., Kibi, M., Izumi, Y., Okuno, T., Hayashi, N., Kawano, S., Takenawa, T., & Azuma, T. A. (2012). A novel serum metabolomics-based diagnostic approach for colorectal cancer. PloS One, 7, e40459.
pubmed: 22792336
pmcid: 3394708
doi: 10.1371/journal.pone.0040459
Onitilo, A. A., Engel, J. M., Greenlee, R. T., & Mukesh, B. N. (2009). Breast cancer subtypes based on ER/PR and Her2 expression: Comparison of clinicopathologic features and survival. Clinical Medicine & Research, 7, 4–13.
doi: 10.3121/cmr.2008.825
Parks, S. K., Mueller-Klieser, W., & Pouysségur, J. (2020). Lactate and acidity in the cancer microenvironment. Annual Review of Cancer Biology, 4, 141–158.
doi: 10.1146/annurev-cancerbio-030419-033556
Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Metabolomics: The apogee of the omics trilogy. Nature Reviews Molecular Cell Biology, 13, 263–269.
pubmed: 22436749
pmcid: 3682684
doi: 10.1038/nrm3314
Putluri, N., Shojaie, A., Vasu, V. T., Vareed, S. K., Nalluri, S., Putluri, V., Thangjam, G. S., Panzitt, K., Tallman, C. T., Butler, C., & Sana, T. R. (2011). Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Research, 71, 7376–7386.
pubmed: 21990318
pmcid: 3249241
doi: 10.1158/0008-5472.CAN-11-1154
Racker, E. (1972). Bioenergetics and the problem of tumor growth: An understanding of the mechanism of the generation and control of biological energy may shed light on the problem of tumor growth. American Scientist, 60, 56–63.
pubmed: 4332766
Reis-Mendes, A., Carvalho, F., Remião, F., Sousa, E., Bastos, M. L., & Costa, V. M. (2019). The main metabolites of fluorouracil + adriamycin + cyclophosphamide (FAC) are not major contributors to FAC toxicity in H9c2 cardiac differentiated cells. Biomolecules, 9, 98.
pubmed: 30862114
pmcid: 6468772
doi: 10.3390/biom9030098
Rodic, S., & Vincent, M. D. (2018). Reactive oxygen species (ROS) are a key determinant of cancer’s metabolic phenotype. International Journal of Cancer, 142, 440–448.
pubmed: 28940517
doi: 10.1002/ijc.31069
Salani, B., Ravera, S., Amaro, A., Salis, A., Passalacqua, M., Millo, E., Damonte, G., Marini, C., Pfeffer, U., Sambuceti, G., & Cordera, R. (2015). IGF1 regulates PKM2 function through akt phosphorylation. Cell Cycle, 14, 1559–1567.
pubmed: 25790097
pmcid: 4612106
doi: 10.1080/15384101.2015.1026490
Schmittgen, T. D., & Livak, K. J. (2008). Analyzing real-time PCR data by the comparative CT method. Nature Protocols, 3, 1101–1108.
pubmed: 18546601
doi: 10.1038/nprot.2008.73
Singh, A., Prakash, V., Gupta, N., Kumar, A., Kant, R., & Kumar, D. (2022). Serum metabolic disturbances in lung cancer investigated through an elaborative NMR-based serum metabolomics approach. ACS Omega, 7, 5510–5520.
pubmed: 35187366
pmcid: 8851899
doi: 10.1021/acsomega.1c06941
Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R. J., Li, Y., & Nyati, M. K. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457, 910–914.
pubmed: 19212411
pmcid: 2724746
doi: 10.1038/nature07762
Tenori, L., Oakman, C., Claudino, W. M., Bernini, P., Cappadona, S., Nepi, S., Biganzoli, L., Arbushites, M. C., Luchinat, C., Bertini, I., & Di Leo, A. (2012). Exploration of serum metabolomic profiles and outcomes in women with metastatic breast cancer: A pilot study. Molecular Oncology, 6, 437–444.
pubmed: 22687601
pmcid: 5528357
doi: 10.1016/j.molonc.2012.05.003
Waks, A. G., & Winer, E. P. (2019). Breast cancer treatment: A review. JAMA, 321, 288–300.
pubmed: 30667505
doi: 10.1001/jama.2018.19323
Walenta, S., Wetterling, M., Lehrke, M., Schwickert, G., Sundfør, K., Rofstad, E. K., & Mueller-Klieser, W. (2000). High lactate levels predict likelihood of metastases, tumor recurrence, and restricted patient survival in human cervical cancers. Cancer Research, 60, 916–921.
pubmed: 10706105
Wang, X., Zhang, H., & Chen, X. (2019). Drug resistance and combating drug resistance in cancer. Cancer Drug Resistance (Alhambra Calif), 2, 141.
pubmed: 34322663
Wang, Y., Wu, P., Lin, R., Rong, L., Xue, Y., & Fang, Y. (2015). LncRNA NALT interaction with NOTCH1 promoted cell proliferation in pediatric T cell acute lymphoblastic leukemia. Scientific Reports, 5, 1–10.
Wang, Y., & Zhou, B. P. (2011). Epithelial-mesenchymal transition in breast cancer progression and metastasis. Chinese Journal of Cancer, 30, 603.
pubmed: 21880181
pmcid: 3702729
doi: 10.5732/cjc.011.10226
Wang, Y., Zhou, P., Li, P., Yang, F., & Gao, X. (2020). Long non-coding RNA H19 regulates proliferation and doxorubicin resistance in MCF-7 cells by targeting PARP1. Bioengineered, 11, 536–546.
pubmed: 32345117
pmcid: 8291873
doi: 10.1080/21655979.2020.1761512
Warburg, O., Wind, F., & Negelein, E. (1927). Killing-off of tumor cells in vitro. Journal of General Physiology, 8, 519–530.
pubmed: 19872213
pmcid: 2140820
doi: 10.1085/jgp.8.6.519
Ward, P. S., & Thompson, C. B. (2012). Metabolic reprogramming: A cancer hallmark even warburg did not anticipate. Cancer Cell, 21, 297–308.
pubmed: 22439925
pmcid: 3311998
doi: 10.1016/j.ccr.2012.02.014
Weigelt, B., & Reis-Filho, J. S. (2010). Molecular profiling currently offers no more than tumour morphology and basic immunohistochemistry. Breast Cancer Research, 12, 1–4.
doi: 10.1186/bcr2734
Woo, H. M., Kim, K. M., Choi, M. H., Jung, B. H., Lee, J., Kong, G., Nam, S. J., Kim, S., Bai, S. W., & Chung, B. C. (2009). Mass spectrometry based metabolomic approaches in urinary biomarker study of women’s cancers. Clinica chimica acta, 400, 63–69.
doi: 10.1016/j.cca.2008.10.014
Zaal, E. A., & Berkers, C. R. (2018). The influence of metabolism on drug response in cancer. Frontiers in Oncology, 8, 500.
pubmed: 30456204
pmcid: 6230982
doi: 10.3389/fonc.2018.00500