Association of altered metabolic profiles and long non-coding RNAs expression with disease severity in breast cancer patients: analysis by 1H NMR spectroscopy and RT-q-PCR

• Published in: Metabolomics Vol. 19; no. 2; p. 8
• Main Authors: Shekher, Anusmita, Puneet, Awasthee, Nikee, Kumar, Umesh, Raj, Ritu, Kumar, Dinesh, Gupta, Subash Chandra
• Format: Journal Article
• Language: English
• Published: New York Springer US 30.01.2023; Springer Nature B.V
• Subjects: Biochemistry; Biomedical and Life Sciences; Biomedicine; Breast cancer; Cell Biology; Developmental Biology; Energy metabolism; Glycerol; Lactic acid; Life Sciences; Lipid metabolism; Lipoproteins; Magnetic resonance spectroscopy; Metabolism; Metabolites; Molecular Medicine; NMR; Non-coding RNA; Nuclear magnetic resonance; Original Article; Spectrum analysis; H NMR spectroscopy; Metabolomics; Prognosis; LncRNA; Breast cancer; Diagnosis; Multivariate analysis
• ISSN: 1573-3890; 1573-3882; 1573-3890
• DOI: 10.1007/s11306-023-01972-5

Introduction 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. Objectives The aim of this study was to analyze the metabolic profile in the serum from 43 breast cancer patients and 13 healthy individuals. Materials & methods We used 1 H NMR spectroscopy for the identification and quantification of metabolites. q-RT-PCR was used to examine the relative expression of lncRNAs. Results 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. Conclusion 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.