, 14:119 | Cite as

Alteration in lipid composition differentiates breast cancer tissues: a 1H HRMAS NMR metabolomic study

  • Anup Paul
  • Surendra KumarEmail author
  • Anubhav Raj
  • Abhinav A. Sonkar
  • Sudha Jain
  • Atin Singhai
  • Raja RoyEmail author
Original Article



Breast cancer is the most frequent diagnosed cancer among women with a mortality rate of 15% of all cancer related deaths in women. Breast cancer is heterogeneous in nature and produces plethora of metabolites allowing its early detection using molecular diagnostic techniques like magnetic resonance spectroscopy.


To evaluate the variation in metabolic profile of breast cancer focusing on lipids as triglycerides (TG) and free fatty acids (FFA) that may alter in malignant breast tissues and lymph nodes from adjacent benign breast tissues by HRMAS 1H NMR spectroscopy.


The 1H NMR spectra recorded on 173 tissue specimens comprising of breast tumor tissues, adjacent tissues, few lymph nodes and overlying skin tissues obtained from 67 patients suffering from breast cancer. Multivariate statistical analysis was employed to identify metabolites acting as major confounders for differentiation of malignancy.


Reduction in lipid content were observed in malignant breast tissues along with a higher fraction of FFA. Four small molecule metabolites e.g., choline containing compounds (Chocc), taurine, glycine, and glutamate were also identified as major confounders. The test set for prediction provided sensitivity and specificity of more than 90% excluding the lymph nodes and skin tissues.


Fatty acids composition in breast cancer using in vivo magnetic resonance spectroscopy (MRS) is gaining its importance in clinical settings (Coum et al. in Magn Reson Mater Phys Biol Med 29:1–4, 2016). The present study may help in future for precise evaluation of lipid classification including small molecules as a source of early diagnosis of invasive ductal carcinoma by employing in vivo magnetic resonance spectroscopic methods.


Breast cancer Malignant tumor tissue Benign tissue HRMAS 1H NMR spectroscopy Triglycerides PUFA MUFA SFA Metabolic profiling 



High resolution magic angle spinning




Nuclear overhauser effect spectroscopy


Nuclear magnetic resonance


Principal component analysis


Orthogonal signal correction


Orthogonal partial least square discriminant analysis


Variable importance in projection


QUANTification by Artificial Signal



The authors are thankful to Division of SAIF, CSIR-Centre of Drug Research Institute, Lucknow where the HRMAS 1H NMR measurements were conducted. Mr. Anup Paul would like to extend his thanks to UGC (SRF Award No. 18-12/2011(ii)EU-V) for financial assistance.

Author contributions

SK, AAS and RR designed the study. SK, AAS and AR performed the surgery and sampled the tissue specimens. AR, SK, RR and AP has conducted the experiments. AP, SK and RR analyzed the data. AS performed the histopathology. Initial draft written by AP. SK, AS and RR edited and revised the paper. Project administration of the study have carried under SK, AAS, SJ and RR. All authors carefully read and agree to be accountable for all aspects of the work.

Compliance with ethical standards

Conflict of interest

The authors have no potential conflict of interest. The disclosure of potential conflict of interest in the prescribed format has been obtained from all the authors.

Ethical approval

The study was ethically approved and the work was performed in strict accordance with the guidelines of Institutional Ethical Committee of King George’s Medical University (KGMU) (Ref. Code: XXII ECM/P 6). The subjects were explained the study procedure and written and informed consent were obtained from them prior to the study. The authors: Anup Paul, Surendra Kumar, Anubhav Raj, Abhinav Sonkar, Sudha Jain, Atin Singhai and Raja Roy are aware of ethical policy.

Supplementary material

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  1. Agarwal, K., Sharma, U., Mathur, S., Seenu, V., Parshad, R., & Jagannathan, N. R. (2018). Study of lipid metabolism by estimating the fat fraction in different breast tissues and in various breast tumor sub-types by in vivo 1H MR spectroscopy. Magnetic Resonance Imaging, 49, 116–122.CrossRefPubMedGoogle Scholar
  2. Agouza, I. M. E., Eissa, S. S., Houseini, M. M. E., El-Nashar, D. E., & Abd El Hameed, O. M. (2011). Taurine: A novel tumor marker for enhanced detection of breast cancer among female patients. Angiogenesis, 14, 321.CrossRefPubMedGoogle Scholar
  3. Amelio, I., Cutruzzolá, F., Antonov, A., Agostini, M., & Melino, G. (2014). Serine and glycine metabolism in cancer. Trends in Biochemical Sciences, 39, 191–198.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Baenke, F., Peck, B., Miess, H., & Schulze, A. (2013). Hooked on fat: The role of lipid synthesis in cancer metabolism and tumour development. Disease Models & Mechanisms, 6, 1353–1363.CrossRefGoogle Scholar
  5. Barh, D. (2016). Omics approaches in breast cancer. New Delhi: Springer.Google Scholar
  6. Bathen, T. F., Geurts, B., Sitter, B., Fjøsne, H. E., Lundgren, S., Buydens, L. M., Gribbestad, I. S., Postma, G., & Giskeødegård, G. F. (2013). Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery. PLoS ONE, 8, e61578.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bharti, S. K., & Roy, R. (2012). Quantitative 1H NMR spectroscopy. TrAC Trends in Analytical Chemistry, 35, 5–26.CrossRefGoogle Scholar
  8. Bray, F., Ren, J.-S., Masuyer, E., & Ferlay, J. (2013). Global estimates of cancer prevalence for 27 sites in the adult population in 2008. International Journal of Cancer, 132, 1133–1145.CrossRefPubMedGoogle Scholar
  9. Budczies, J., Pfitzner, B. M., Györffy, B., Winzer, K.-J., Radke, C., Dietel, M., Fiehn, O., & Denkert, C. (2015). Glutamate enrichment as new diagnostic opportunity in breast cancer. International Journal of Cancer, 136, 1619–1628.CrossRefPubMedGoogle Scholar
  10. Calligaris, D., Caragacianu, D., Liu, X., Norton, I., Thompson, C. J., Richardson, A. L., Golshan, M., Easterling, M. L., Santagata, S., & Dillon, D. A. (2014). Application of desorption electrospray ionization mass spectrometry imaging in breast cancer margin analysis. Proceedings of the National Academy of Sciences, 111, 15184–15189.CrossRefGoogle Scholar
  11. Chae, E. Y., Shin, H. J., Kim, S., Baek, H.-M., Yoon, D., Kim, S., Shim, Y. E., Kim, H. H., Cha, J. H., & Choi, W. J. (2016). The role of high-resolution magic angle spinning 1H nuclear magnetic resonance spectroscopy for predicting the invasive component in patients with ductal carcinoma in situ diagnosed on preoperative biopsy. PLoS ONE, 11, e0161038.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Cheng, L. L., Chang, I.-W., Smith, B. L., & Gonzalez, R. G. (1998). Evaluating human breast ductal carcinomas with high-resolution magic-angle spinning proton magnetic resonance spectroscopy. Journal of Magnetic Resonance, 135, 194–202.CrossRefPubMedGoogle Scholar
  13. Chenomx NMR (2015). Suite. Edmonton: Chenomx Inc.Google Scholar
  14. Cífková, E., Holčapek, M., Lísa, M., Vrána, D., Gatěk, J., & Melichar, B. (2015). Determination of lipidomic differences between human breast cancer and surrounding normal tissues using HILIC-HPLC/ESI-MS and multivariate data analysis. Analytical and Bioanalytical Chemistry, 407, 991–1002.CrossRefPubMedGoogle Scholar
  15. Coum, A., Ouldamer, L., Noury, F., Barantin, L., Saint-Hilaire, A., Vilde, A., Bougnoux, P., & Gambarota, G. (2016). In vivo MR spectroscopy of human breast tissue: Quantification of fatty acid composition at a clinical field strength (3 T). Magnetic Resonance Materials in Physics, Biology and Medicine, 29, 1–4.CrossRefGoogle Scholar
  16. Dalle, J.-R., Leow, W. K., Racoceanu, D., Tutac, A. E., & Putti, T. C. (2008). Automatic breast cancer grading of histopathological images. In 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) (pp. 3052–3055). IEEE.Google Scholar
  17. de Graaf, R. A., Klomp, D. W. J., Luijten, P. R., & Boer, V. O. (2014). Intramolecular zero-quantum-coherence 2D NMR spectroscopy of lipids in the human breast at 7 T. Magnetic Resonance in Medicine, 71, 451–457.CrossRefPubMedGoogle Scholar
  18. DeBerardinis, R. J., & Chandel, N. S. (2016). Fundamentals of cancer metabolism. Science Advances, 2, e1600200.CrossRefPubMedPubMedCentralGoogle Scholar
  19. DeVivo, D. C., Leckie, M. P., & Agrawal, H. C. (1975). d-β-HYDROXYBUTYRATE: A MAJOR PRECURSOR OF AMINO ACIDS IN DEVELOPING RAT BRAIN. Journal of Neurochemistry, 25, 161–170.CrossRefPubMedGoogle Scholar
  20. Dimitrov, I. E., Douglas, D., Ren, J., Smith, N. B., Webb, A. G., Sherry, A. D., & Malloy, C. R. (2012). In vivo determination of human breast fat composition by 1H magnetic resonance spectroscopy at 7 T. Magnetic Resonance in Medicine, 67, 20–26.CrossRefPubMedGoogle Scholar
  21. Drisis, S., Flamen, P., Ignatiadis, M., Metens, T., Chao, S.-L., Chintinne, M., & Lemort, M. (2018). Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status. Journal of Magnetic Resonance Imaging. Scholar
  22. Ellis, I. O., Galea, M., Broughton, N., Locker, A., Blamey, R. W., & Elston, C. W. (1992). Pathological prognostic factors in breast cancer. II. Histological type. Relationship with survival in a large study with long-term follow-up. Histopathology, 20, 479–489.CrossRefPubMedGoogle Scholar
  23. Elston, C. W., & Ellis, I. O. (1991). Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: Experience from a large study with long-term follow-up. Histopathology, 19, 403–410.CrossRefPubMedGoogle Scholar
  24. Fazzari, J., Lin, H., Murphy, C., Ungard, R., & Singh, G. (2015). Inhibitors of glutamate release from breast cancer cells; new targets for cancer-induced bone-pain. Scientific Reports, 5, 8380.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., & Bray, F. (2015). Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. Lyon, France: International Agency for Research on Cancer. GLOBOCAN. 2013; 2012 v1. 0. Retrieved from
  26. Foschini, M. P., Morandi, L., Leonardi, E., Flamminio, F., Ishikawa, Y., Masetti, R., & Eusebi, V. (2013). Genetic clonal mapping of in situ and invasive ductal carcinoma indicates the field cancerization phenomenon in the breast. Human Pathology, 44, 1310–1319.CrossRefPubMedGoogle Scholar
  27. Freed, M., Storey, P., Lewin, A. A., Babb, J., Moccaldi, M., Moy, L., & Kim, S. G. (2016). Evaluation of breast lipid composition in patients with benign tissue and cancer by using multiple gradient-echo MR imaging. Radiology, 281, 43–53.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Gavaghan, C. L., Wilson, I. D., & Nicholson, J. K. (2002). Physiological variation in metabolic phenotyping and functional genomic studies: Use of orthogonal signal correction and PLS-DA. FEBS Letters, 530, 191–196.CrossRefPubMedGoogle Scholar
  29. Gogiashvili, M., Horsch, S., Marchan, R., Gianmoena, K., Cadenas, C., Tanner, B., Naumann, S., Ersova, D., Lippek, F., & Rahnenführer, J. (2017). Impact of intratumoral heterogeneity of breast cancer tissue on quantitative metabolomics using high-resolution magic angle spinning 1H NMR spectroscopy. NMR in Biomedicine, 31, e3862.CrossRefGoogle Scholar
  30. Hardy, S., St-Onge, G. G., Joly, É, Langelier, Y., & Prentki, M. (2005). Oleate promotes the proliferation of breast cancer cells via the G protein-coupled receptor GPR40. Journal of Biological Chemistry, 280, 13285–13291.CrossRefPubMedGoogle Scholar
  31. Harris, A. D., Saleh, M. G., & Edden, R. A. (2017). Edited 1H magnetic resonance spectroscopy in vivo: Methods and metabolites. Magnetic Resonance in Medicine, 77, 1377–1389.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Haukaas, T. H., Euceda, L. R., Giskeødegård, G. F., & Bathen, T. F. (2017). Metabolic portraits of breast cancer by HR MAS MR spectroscopy of intact tissue samples. Metabolites, 7, 18.CrossRefPubMedCentralGoogle Scholar
  33. Hilvo, M., Denkert, C., Lehtinen, L., Müller, B., Brockmöller, S., Seppänen-Laakso, T., Budczies, J., Bucher, E., Yetukuri, L., & Castillo, S. (2011). Novel theranostic opportunities offered by characterization of altered membrane lipid metabolism in breast cancer progression. Cancer Research, 71, 3236–3245.CrossRefPubMedGoogle Scholar
  34. Hirschhaeuser, F., Sattler, U. G. A., & Mueller-Klieser, W. (2011). Lactate: A metabolic key player in cancer. Cancer Research, 71, 6921–6925.CrossRefPubMedGoogle Scholar
  35. Jagannathan, N. R., & Sharma, U. (2017). Breast tissue metabolism by magnetic resonance spectroscopy. Metabolites, 7, 25.CrossRefPubMedCentralGoogle Scholar
  36. 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.CrossRefPubMedGoogle Scholar
  37. Kendler, B. S. (1989). Taurine: An overview of its role in preventive medicine. Preventive Medicine, 18, 79–100.CrossRefPubMedGoogle Scholar
  38. Lebovic, G. S., Hollingsworth, A., & Feig, S. A. (2010). Risk assessment, screening and prevention of breast cancer: A look at cost-effectiveness. The Breast, 19, 260–267.CrossRefPubMedGoogle Scholar
  39. Li, M., Song, Y., Cho, N., Chang, J. M., Koo, H. R., Yi, A., Kim, H., Park, S., & Moon, W. K. (2011). An HR-MAS MR metabolomics study on breast tissues obtained with core needle biopsy. PLoS ONE, 6, e25563.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Malvia, S., Bagadi, S. A., Dubey, U. S., & Saxena, S. (2017). Epidemiology of breast cancer in Indian women. Asia-Pacific Journal of Clinical Oncology, 13, 289–295.CrossRefPubMedGoogle Scholar
  41. Martinez-Outschoorn, U., Sotgia, F., & Lisanti, M. P. (2014). Tumor microenvironment and metabolic synergy in breast cancers: Critical importance of mitochondrial fuels and function. In Seminars in oncology (Vol. 41, No. 2, pp. 195–216). WB SaundersGoogle Scholar
  42. Menendez, J. A., & Lupu, R. (2007). Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nature Reviews Cancer, 7, 763.CrossRefPubMedGoogle Scholar
  43. Moestue, S., Sitter, B., Bathen, T. F., Tessem, M.-B., & Gribbestad, I. S. (2011). HR MAS MR spectroscopy in metabolic characterization of cancer. Current Topics in Medicinal Chemistry, 11, 2–26.CrossRefPubMedGoogle Scholar
  44. Mountford, C. E., Doran, S., Lean, C. L., & Russell, P. (2004). Proton MRS can determine the pathology of human cancers with a high level of accuracy. Chemical Reviews, 104, 3677–3704.CrossRefPubMedGoogle Scholar
  45. Mountford, C. E., Somorjai, R. L., Malycha, P., Gluch, L., Lean, C., Russell, P., Barraclough, B., Gillett, D., Himmelreich, U., & Dolenko, B. (2001). Diagnosis and prognosis of breast cancer by magnetic resonance spectroscopy of fine-needle aspirates analysed using a statistical classification strategy. British Journal of Surgery, 88, 1234–1240.CrossRefPubMedGoogle Scholar
  46. NCDIR-NCRP. (2016). Three-year report of population based cancer registries 2012–2014: Incidence, distribution, trends in incidence rates and projection of burden of cancer. In Indian Council of Medical Research (ICMR) Report. Bangalore.Google Scholar
  47. Ogrodzinski, M. P., Bernard, J. J., & Lunt, S. Y. (2017). Deciphering metabolic rewiring in breast cancer subtypes. Translational Research, 189, 105–122.CrossRefPubMedGoogle Scholar
  48. Pearce, J. M., Mahoney, M. C., Lee, J.-H., Chu, W.-J., Cecil, K. M., Strakowski, S. M., & Komoroski, R. A. (2013). 1H NMR analysis of choline metabolites in fine-needle-aspirate biopsies of breast cancer. Magnetic Resonance Materials in Physics, Biology and Medicine, 26, 337–343.CrossRefGoogle Scholar
  49. Przybytkowski, E. (2007). 'Fatty acid metabolism and modulation of human breast cancer cell survival.' Ph.D. Thesis. Université de Montréal, Montreal, Canada.Google Scholar
  50. Qiu, Y., Zhou, B., Su, M., Baxter, S., Zheng, X., Zhao, X., Yen, Y., & Jia, W. (2013). Mass spectrometry-based quantitative metabolomics revealed a distinct lipid profile in breast cancer patients. International Journal of Molecular Sciences, 14, 8047–8061.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Reshef, L., Olswang, Y., Cassuto, H., Blum, B., Croniger, C. M., Kalhan, S. C., Tilghman, S. M., & Hanson, R. W. (2003). Glyceroneogenesis and the triglyceride/fatty acid cycle. Journal of Biological Chemistry, 278, 30413–30416.CrossRefPubMedGoogle Scholar
  52. Robbins, G. F., Brothers, J. H., Eberhart, I. I. I.,W. F., & Quan, S. (1954). Is aspiration biopsy of breast cancer dangerous to the patient? Cancer, 7, 774–778.CrossRefPubMedGoogle Scholar
  53. Schrover, I. M., Leiner, T., Klomp, D. W. J., Wijnen, J. P., Uiterwaal, C. S. P. M., Spiering, W., & Visseren, F. L. J. (2014). Feasibility and reproducibility of free fatty acid profiling in abdominal adipose tissue with 1H-magnetic resonance spectroscopy at 3 T: Differences between lean and obese individuals. Journal of Magnetic Resonance Imaging, 40, 423–431.CrossRefPubMedGoogle Scholar
  54. Schug, Z. T., Voorde, J. V., & Gottlieb, E. (2016). The metabolic fate of acetate in cancer. Nature Reviews Cancer, 16, 708.CrossRefPubMedGoogle Scholar
  55. Sennerstam, R. B., Franzén, B. S. H., Wiksell, H. O. T., & Auer, G. U. (2017). Core-needle biopsy of breast cancer is associated with a higher rate of distant metastases 5 to 15 years after diagnosis than FNA biopsy. Cancer Cytopathology, 125, 748–756.CrossRefPubMedGoogle Scholar
  56. Sharma, U., Mehta, A., Seenu, V., & Jagannathan, N. R. (2004). Biochemical characterization of metastatic lymph nodes of breast cancer patients by in vitro 1H magnetic resonance spectroscopy: A pilot study. Magnetic Resonance Imaging, 22, 697–706.CrossRefPubMedGoogle Scholar
  57. Shetty, M. K. (2014). Breast cancer screening and diagnosis: A synopsis. New York: Springer.Google Scholar
  58. Sitter, B., Lundgren, S., Bathen, T. F., Halgunset, J., Fjosne, H. E., & Gribbestad, I. S. (2006). Comparison of HR MAS MR spectroscopic profiles of breast cancer tissue with clinical parameters. NMR in Biomedicine, 19, 30–40.CrossRefPubMedGoogle Scholar
  59. Sitter, B., Sonnewald, U., Spraul, M., Fjösne, H. E., & Gribbestad, I. S. (2002). High-resolution magic angle spinning MRS of breast cancer tissue. NMR in Biomedicine, 15, 327–337.CrossRefPubMedGoogle Scholar
  60. Srivastava, N. K., Pradhan, S., Gowda, G. A., & Kumar, R. (2010a). In vitro, high-resolution 1H and 31P NMR based analysis of the lipid components in the tissue, serum, and CSF of the patients with primary brain tumors: One possible diagnostic view. NMR in Biomedicine, 23, 113–122.CrossRefPubMedGoogle Scholar
  61. Srivastava, S., Roy, R., Singh, S., Kumar, P., Dalela, D., Sankhwar, S. N., Goel, A., & Sonkar, A. A. (2010b). Taurine—A possible fingerprint biomarker in non-muscle invasive bladder cancer: A pilot study by 1H NMR spectroscopy. Cancer Biomarkers, 6, 11–20.CrossRefPubMedGoogle Scholar
  62. Srivastava, S., Roy, R., Gupta, V., Tiwari, A., Srivastava, A. N., & Sonkar, A. A. (2011). Proton HR-MAS MR spectroscopy of oral squamous cell carcinoma tissues: An ex vivo study to identify malignancy induced metabolic fingerprints. Metabolomics, 7, 278–288.CrossRefGoogle Scholar
  63. Stewart, B., & Wild, C. P. (2017). World cancer report 2014. Health.Google Scholar
  64. Vaidyanathan, S., & Goodacre, R. (2007). Quantitative detection of metabolites using matrix-assisted laser desorption/ionization mass spectrometry with 9-aminoacridine as the matrix. Rapid Communications in Mass Spectrometry, 21, 2072–2078.CrossRefPubMedGoogle Scholar
  65. Vorherr, H. (2012). The breast: Morphology, physiology, and lactation. Saint Louis: Elsevier.Google Scholar
  66. Warner, E. (2011). Breast-cancer screening. New England Journal of Medicine, 365, 1025–1032.CrossRefPubMedGoogle Scholar
  67. Weigelt, B., Geyer, F. C., & Reis-Filho, J. S. (2010). Histological types of breast cancer: How special are they? Molecular Oncology, 4, 192–208.CrossRefPubMedPubMedCentralGoogle Scholar
  68. WHO. (2015). GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence worldwide in 2012.Google Scholar
  69. Wishart, D. S. (2008). Quantitative metabolomics using NMR. TrAC Trends in Analytical Chemistry, 27, 228–237.CrossRefGoogle Scholar
  70. Xia, J., & Wishart, D. S. (2011). Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols, 6, 743.CrossRefPubMedGoogle Scholar
  71. Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0—Making metabolomics more meaningful. Nucleic Acids Research, 43, W251–W257.CrossRefPubMedPubMedCentralGoogle Scholar
  72. Xie, G., Zhou, B., Zhao, A., Qiu, Y., Zhao, X., Garmire, L., Shvetsov, Y. B., Yu, H., Yen, Y., & Jia, W. (2015). Lowered circulating aspartate is a metabolic feature of human breast cancer. Oncotarget, 6, 33369.PubMedPubMedCentralGoogle Scholar
  73. Zarghami, N., Giai, M., Yu, H., Roagna, R., Ponzone, R., Katsaros, D., Sismondi, P., & Diamandis, E. P. (1996). Creatine kinase BB isoenzyme levels in tumour cytosols and survival of breast cancer patients. British Journal of Cancer, 73, 386.CrossRefPubMedPubMedCentralGoogle Scholar
  74. Zhang, F., & Du, G. (2012). Dysregulated lipid metabolism in cancer. World Journal of Biological Chemistry, 3, 167.CrossRefPubMedPubMedCentralGoogle Scholar
  75. Zhang, J., Pavlova, N. N., & Thompson, C. B. (2017). Cancer cell metabolism: The essential role of the nonessential amino acid, glutamine. The EMBO Journal. Scholar
  76. Zhu, Z. R., Ågren, J., Männistö, S., Pietinen, P., Eskelinen, M., Syrjänen, K., & Uusitupa, M. (1995). Fatty acid composition of breast adipose tissue in breast cancer patients and in patients with benign breast disease. Nutrition and Cancer, 24, 151–160.CrossRefPubMedGoogle Scholar

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Authors and Affiliations

  1. 1.Centre of Biomedical Research, Formerly Centre of Biomedical Magnetic Resonance (CBMR)Sanjay Gandhi Postgraduate Institute of Medical Sciences CampusLucknowIndia
  2. 2.Department of General SurgeryKings George’s Medical University (KGMU)LucknowIndia
  3. 3.Department of ChemistryUniversity of LucknowLucknowIndia
  4. 4.Department of PathologyKing George’s Medical UniversityLucknowIndia

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