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Firdous S, Nawaz Z, Abid R, Cheng LL, Musharraf SG, Sadaf S. Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas. Interdiscip Sci 2024:10.1007/s12539-024-00642-x. [PMID: 39331335 DOI: 10.1007/s12539-024-00642-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 09/28/2024]
Abstract
Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.
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Affiliation(s)
- Safia Firdous
- Biopharmaceuticals and Biomarkers Discovery Lab, School of Biochemistry and Biotechnology, University of the Punjab, Lahore, 54590, Pakistan
- Riphah College of Rehabilitation and Allied Health Sciences, Riphah International University, Lahore, 54770, Pakistan
| | - Zubair Nawaz
- Department of Data Science, Punjab University College of Information Technology, University of the Punjab, Lahore, 54590, Pakistan
| | - Rizwan Abid
- Biopharmaceuticals and Biomarkers Discovery Lab, School of Biochemistry and Biotechnology, University of the Punjab, Lahore, 54590, Pakistan
| | - Leo L Cheng
- Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Syed Ghulam Musharraf
- HEJ Research Institute of Chemistry, International Centre for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Saima Sadaf
- Biopharmaceuticals and Biomarkers Discovery Lab, School of Biochemistry and Biotechnology, University of the Punjab, Lahore, 54590, Pakistan.
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Almalki AH. Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer. Metabolites 2023; 13:1037. [PMID: 37887362 PMCID: PMC10609104 DOI: 10.3390/metabo13101037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.
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Affiliation(s)
- Atiah H. Almalki
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
- Addiction and Neuroscience Research Unit, Health Science Campus, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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3
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Rafalskiy VV, Zyubin AY, Moiseeva EM, Kupriyanova GS, Mershiev IG, Kryukova NO, Kon II, Samusev IG, Belousova YD, Doktorova SA. Application of vibrational spectroscopy and nuclear magnetic resonance methods for drugs pharmacokinetics research. Drug Metab Pers Ther 2023; 38:3-13. [PMID: 36169571 DOI: 10.1515/dmpt-2022-0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/21/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVES The development of new methods for determining the concentration of drugs is an actual topic today. The article contains a detailed review on vibrational spectroscopy and nuclear magnetic resonance methods using for pharmacokinetic research. This study is devoted to the possibility of using vibrational spectroscopy and 1H nuclear magnetic resonance spectroscopy to determine the concentration of drugs and the use of these groups of techniques for therapeutic drug monitoring. CONTENT The study was conducted by using scientific libraries (Scopus, Web of Science Core Collection, Medline, GoogleScholar, eLIBRARY, PubMed) and reference literature. A search was conducted for the period from 2011 to 2021 in Russian and English, by combinations of words: 1H nuclear magnetic resonance (1H NMR), vibrational spectroscopy, Surface-Enhanced Raman spectroscopy, drug concentration, therapeutic drug monitoring. These methods have a number of advantages and are devoid of some of the disadvantages of classical therapeutic drug monitoring (TDM) methods - high performance liquid chromatography and mass spectrometry. This review considers the possibility of using the methods of surface-enhanced Raman scattering (SERS) and 1H NMR-spectroscopy to assess the concentration of drugs in various biological media (blood, urine), as well as to study intracellular metabolism and the metabolism of ophthalmic drugs. 1Н NMR-spectroscopy can be chosen as a TDM method, since it allows analyzing the structure and identifying metabolites of various drugs. 1Н NMR-based metabolomics can provide information on the side effects of drugs, predict response to treatment, and provide key information on the mechanisms of action of known and new drug compounds. SUMMARY AND OUTLOOK SERS and 1Н NMR-spectroscopy have great potential for further study and the possibility of introducing them into clinical practice, including for evaluating the efficacy and safety of drugs.
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Affiliation(s)
- Vladimir V Rafalskiy
- Department of Therapy of the Medical Institute of the IKBFU, Kaliningrad, Russia
| | - Andrey Yu Zyubin
- REC "Fundamental and Applied Photonics, Nanophotonics", IKBFU, Kaliningrad, Russia
| | | | | | | | - Nadezhda O Kryukova
- Department of Fundamental Medicine of the Medical Institute of the IKBFU, Kaliningrad, Russia
| | - Igor I Kon
- REC "Fundamental and Applied Photonics, Nanophotonics", Kaliningrad, Russia
| | - Ilya G Samusev
- REC "Fundamental and Applied Photonics, Nanophotonics", Kaliningrad, Russia
| | | | - Svetlana A Doktorova
- Medical Institute of the IKBFU, Kaliningrad, Russia
- Immanuel Kant Baltic Federal University Institute of Medicine - Clinical Trial Center of IKBFUA, Kaliningrad, Russia
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4
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Ali S, Nedvědová Š, Badshah G, Afridi MS, Abdullah, Dutra LM, Ali U, Faria SG, Soares FL, Rahman RU, Cançado FA, Aoyanagi MM, Freire LG, Santos AD, Barison A, Oliveira CA. NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns. CURRENT RESEARCH IN IMMUNOLOGY 2022; 3:199-214. [PMID: 36032416 PMCID: PMC9393187 DOI: 10.1016/j.crimmu.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/01/2022] [Indexed: 11/16/2022] Open
Abstract
In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future. NMR analysis of various bio-samples can explore disease course and vaccine interaction. Immunogenicity and reactogenicity caused by COVID-19 vaccination can be studied by NMR. Vaccine interaction alters metabolic pathway(s) at a certain stage, and this mechanism can be probed at the metabolic level.
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1H HR-MAS NMR Based Metabolic Profiling of Lung Cancer Cells with Induced and De-Induced Cisplatin Resistance to Reveal Metabolic Resistance Adaptations. Molecules 2021; 26:molecules26226766. [PMID: 34833859 PMCID: PMC8625954 DOI: 10.3390/molecules26226766] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/27/2021] [Accepted: 11/03/2021] [Indexed: 12/01/2022] Open
Abstract
Cisplatin (cisPt) is an important drug that is used against various cancers, including advanced lung cancer. However, drug resistance is still a major ongoing problem and its investigation is of paramount interest. Here, a high-resolution magic angle spinning (HR-MAS) NMR study is presented deciphering the metabolic profile of non-small cell lung cancer (NSCLC) cells and metabolic adaptations at different levels of induced cisPt-resistance, as well as in their de-induced counterparts (cells cultivated in absence of cisPt). In total, fifty-three metabolites were identified and quantified in the 1H-HR-MAS NMR cell spectra. Metabolic adaptations to cisPt-resistance were detected, which correlated with the degree of resistance. Importantly, de-induced cell lines demonstrated similar metabolic adaptations as the corresponding cisPt-resistant cell lines. Metabolites predominantly changed in cisPt resistant cells and their de-induced counterparts include glutathione and taurine. Characteristic metabolic patterns for cisPt resistance may become relevant as biomarkers in cancer medicine.
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Prommajun P, Phetcharaburanin J, Namwat N, Klanrit P, Sa-Ngiamwibool P, Thanee M, Dokduang H, Kittirat Y, Li JV, Loilome W. Metabolic Profiling of Praziquantel-mediated Prevention of Opisthorchis viverrini-induced Cholangiocyte Transformation in the Hamster Model of Cholangiocarcinoma. Cancer Genomics Proteomics 2021; 18:29-42. [PMID: 33419894 DOI: 10.21873/cgp.20239] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/10/2020] [Accepted: 10/13/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Opisthorchis viverrini (Ov) infection-induced cholangiocarcinoma (CCA) is a major public health problem in northeastern Thailand. Praziquantel was shown to prevent CCA development in an Ov-infected hamster model; however, the molecular mechanism remains unknown. MATERIALS AND METHODS In this study, we used a hamster model with Ov and N-nitrosodimethylamine-induced CCA to study the mechanisms of praziquantel action. The liver tissues from the hamsters with and without praziquantel treatment were analyzed using 1H nuclear magnetic resonance spectroscopy. RESULTS A total of 14 metabolites were found to be significantly different between the two groups. Furthermore, the combination of acetate, inosine and sarcosine was shown to exert an anti-inflammatory effect through interleukin-6 inhibition in a macrophage cell line, suggesting a mechanism by which praziquantel may prevent inflammation caused by Ov, cholangiocyte transformation and further CCA develpoment. CONCLUSION These findings might avail the development of a preventive strategy for CCA in high-risk populations.
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Affiliation(s)
- Pattama Prommajun
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
| | - Jutarop Phetcharaburanin
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand.,Khon Kaen University International Phenome Laboratory, Northeastern Science Park, Khon Kaen University, Khon Kaen, Thailand
| | - Nisana Namwat
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
| | - Poramate Klanrit
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
| | - Prakasit Sa-Ngiamwibool
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand.,Department of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Malinee Thanee
- Faculty of Medical Science, Nakhonratchasima College, Nakhon Ratchasima, Thailand
| | - Hasaya Dokduang
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
| | - Yingpinyapat Kittirat
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
| | - Jia V Li
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, South Kensington Campus, London, U.K
| | - Watcharin Loilome
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand; .,Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand.,Khon Kaen University International Phenome Laboratory, Northeastern Science Park, Khon Kaen University, Khon Kaen, Thailand
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Crook AA, Powers R. Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications. Molecules 2020; 25:E5128. [PMID: 33158172 PMCID: PMC7662776 DOI: 10.3390/molecules25215128] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/19/2022] Open
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.
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Affiliation(s)
- Alexandra A. Crook
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA;
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
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Diagnostica per immagini funzionale nell’epilessia. Neurologia 2020. [DOI: 10.1016/s1634-7072(20)43296-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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