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van Holstein Y, Mooijaart SP, van Oevelen M, van Deudekom FJ, Vojinovic D, Bizzarri D, van den Akker EB, Noordam R, Deelen J, van Heemst D, de Glas NA, Holterhues C, Labots G, van den Bos F, Beekman M, Slagboom PE, van Munster BC, Portielje JEA, Trompet S. The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors. GeroScience 2024:10.1007/s11357-024-01261-6. [PMID: 38963649 DOI: 10.1007/s11357-024-01261-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 06/23/2024] [Indexed: 07/05/2024] Open
Abstract
Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.
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Affiliation(s)
- Yara van Holstein
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands.
| | - Simon P Mooijaart
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
- LUMC Center for Medicine for Older People, Leiden University Medical Center, Leiden, The Netherlands
| | - Mathijs van Oevelen
- Department of Internal Medicine, Section of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
| | - Floor J van Deudekom
- Department of Geriatric Medicine, OLVG Hospitals Amsterdam, Amsterdam, The Netherlands
| | - Dina Vojinovic
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, University Medical Centre, Rotterdam, The Netherlands
| | - Daniele Bizzarri
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Erik B van den Akker
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
| | - Joris Deelen
- Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster On Cellular Stress Responses in Ageing-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
| | - Nienke A de Glas
- Department of Medical Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Cynthia Holterhues
- Department of Internal Medicine, Haga Hospital, The Hague, The Netherlands
| | - Geert Labots
- Department of Internal Medicine, Haga Hospital, The Hague, The Netherlands
| | - Frederiek van den Bos
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
| | - Marian Beekman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Barbara C van Munster
- Department of Internal Medicine, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
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2
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Jayakrishnan T, Mariam A, Farha N, Rotroff DM, Aucejo F, Barot SV, Conces M, Nair KG, Krishnamurthi SS, Schmit SL, Liska D, Khorana AA, Kamath SD. Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer. Sci Rep 2024; 14:4294. [PMID: 38383634 PMCID: PMC10881959 DOI: 10.1038/s41598-024-54560-5] [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: 10/19/2023] [Accepted: 02/14/2024] [Indexed: 02/23/2024] Open
Abstract
Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.
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Affiliation(s)
- Thejus Jayakrishnan
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
| | - Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, USA
| | - Nicole Farha
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, USA
| | - Federico Aucejo
- Department of Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, USA
| | - Shimoli V Barot
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
| | - Madison Conces
- Case Comprehensive Cancer Center, Cleveland, USA
- Department of Hematology-Oncology, University Hospital Seidman Cancer Center, Cleveland, USA
| | - Kanika G Nair
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Smitha S Krishnamurthi
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Stephanie L Schmit
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, USA
| | - David Liska
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
- Department of Colorectal Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, USA
| | - Alok A Khorana
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
- Case Comprehensive Cancer Center, Cleveland, USA
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA
| | - Suneel D Kamath
- Department of Hematology and Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA.
- Case Comprehensive Cancer Center, Cleveland, USA.
- Center for Young-Onset Colorectal Cancer, Cleveland Clinic, Cleveland, USA.
- Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA.
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3
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Santos MD, Barros I, Brandão P, Lacerda L. Amino Acid Profiles in the Biological Fluids and Tumor Tissue of CRC Patients. Cancers (Basel) 2023; 16:69. [PMID: 38201497 PMCID: PMC10778074 DOI: 10.3390/cancers16010069] [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: 11/18/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients.
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Affiliation(s)
- Marisa Domingues Santos
- Colorectal Unit, Hospital de Santo António, Centro Hospitalar Universitário de Santo António, 4050-651 Porto, Portugal;
- UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal; (I.B.); (L.L.)
- ITR—Laboratory for Integrative and Translational Research in Population Health, 4050-313 Porto, Portugal
| | - Ivo Barros
- UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal; (I.B.); (L.L.)
| | - Pedro Brandão
- Colorectal Unit, Hospital de Santo António, Centro Hospitalar Universitário de Santo António, 4050-651 Porto, Portugal;
- UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal; (I.B.); (L.L.)
- ITR—Laboratory for Integrative and Translational Research in Population Health, 4050-313 Porto, Portugal
| | - Lúcia Lacerda
- UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal; (I.B.); (L.L.)
- ITR—Laboratory for Integrative and Translational Research in Population Health, 4050-313 Porto, Portugal
- Genetic Laboratory Service, Centro de Genética Médica Jacinto de Magalhães, Centro Hospitalar Universitário de Santo António, 4050-651 Porto, Portugal
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Bull C, Hazelwood E, Bell JA, Tan V, Constantinescu AE, Borges C, Legge D, Burrows K, Huyghe JR, Brenner H, Castellvi-Bel S, Chan AT, Kweon SS, Le Marchand L, Li L, Cheng I, Pai RK, Figueiredo JC, Murphy N, Gunter MJ, Timpson NJ, Vincent EE. Identifying metabolic features of colorectal cancer liability using Mendelian randomization. eLife 2023; 12:RP87894. [PMID: 38127078 PMCID: PMC10735227 DOI: 10.7554/elife.87894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023] Open
Abstract
Background Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival. Methods To investigate whether changes in circulating metabolites characterize the early stages of colorectal cancer (CRC) development, we examined the associations between a genetic risk score (GRS) associated with CRC liability (72 single-nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N = 6221). Linear regression models were applied to examine the associations between genetic liability to CRC and circulating metabolites measured in the same individuals at age 8 y, 16 y, 18 y, and 25 y. Results The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P < 0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N = 118,466, median age 58 y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk. Conclusions These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism and suggest that fatty acids may play an important role in CRC development. Funding This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/.
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Affiliation(s)
- Caroline Bull
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- Translational Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Emma Hazelwood
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Joshua A Bell
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Vanessa Tan
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Carolina Borges
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Danny Legge
- Translational Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer CenterSeattleUnited States
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ)HeidelbergGermany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT)HeidelbergGermany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ)HeidelbergGermany
| | - Sergi Castellvi-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of BarcelonaBarcelonaSpain
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical SchoolBostonUnited States
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical SchoolBostonUnited States
- Broad Institute of Harvard and MITCambridgeUnited States
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard UniversityBostonUnited States
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard UniversityBostonUnited States
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical SchoolGwangjuRepublic of Korea
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun HospitalHwasunRepublic of Korea
| | | | - Li Li
- Department of Family Medicine, University of VirginiaCharlottesvilleUnited States
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San FranciscoSan FranciscoUnited States
- University of California, San Francisco Helen Diller Family Comprehensive Cancer Center, San FranciscoSan FranciscoUnited States
| | - Rish K Pai
- Department of Pathology and Laboratory Medicine, Mayo ClinicScottsdaleUnited States
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical CenterLos AngelesUnited States
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on CancerLyonFrance
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonLondonUnited Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of BristolBristolUnited Kingdom
- Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- Translational Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
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5
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Bull CJ, Hazelwood E, Bell JA, Tan VY, Constantinescu AE, Borges MC, Legge DN, Burrows K, Huyghe JR, Brenner H, Castellví-Bel S, Chan AT, Kweon SS, Marchand LL, Li L, Cheng I, Pai RK, Figueiredo JC, Murphy N, Gunter MJ, Timpson NJ, Vincent EE. Identifying metabolic features of colorectal cancer liability using Mendelian randomization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.10.23287084. [PMID: 36945480 PMCID: PMC10029059 DOI: 10.1101/2023.03.10.23287084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Background Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival. Methods To investigate whether changes in circulating metabolites characterise the early stages of colorectal cancer (CRC) development, we examined associations between a genetic risk score (GRS) associated with CRC liability (72 single nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N=6,221). Linear regression models were applied to examine associations between genetic liability to colorectal cancer and circulating metabolites measured in the same individuals at age 8, 16, 18 and 25 years. Results The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P<0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N=118,466, median age 58y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk. Conclusions These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism, and suggest that fatty acids may play an important role in CRC development. Funding This work was supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, the Wellcome Trust, the Medical Research Council, Diabetes UK, the University of Bristol NIHR Biomedical Research Centre, and Cancer Research UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work used the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bristol.ac.uk/acrc/.
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Affiliation(s)
- Caroline J. Bull
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Translational Health Sciences, Bristol Medical School, University of Bristol, UK
| | - Emma Hazelwood
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Joshua A. Bell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Vanessa Y. Tan
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrei-Emil Constantinescu
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Danny N. Legge
- Translational Health Sciences, Bristol Medical School, University of Bristol, UK
| | - Kimberly Burrows
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jeroen R. Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
- University of California, San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, San Francisco, California, USA
| | - Rish K. Pai
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Arizona, Scottsdale, Arizona, USA
| | - Jane C. Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma E. Vincent
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Translational Health Sciences, Bristol Medical School, University of Bristol, UK
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6
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Ghini V, Meoni G, Vignoli A, Di Cesare F, Tenori L, Turano P, Luchinat C. Fingerprinting and profiling in metabolomics of biosamples. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023; 138-139:105-135. [PMID: 38065666 DOI: 10.1016/j.pnmrs.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 12/18/2023]
Abstract
This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.
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Affiliation(s)
- Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy.
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy; Giotto Biotech S.r.l., Sesto Fiorentino, Italy.
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7
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Vignoli A, Miolo G, Tenori L, Buonadonna A, Lombardi D, Steffan A, Scalone S, Luchinat C, Corona G. Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas. iScience 2023; 26:107678. [PMID: 37752948 PMCID: PMC10518687 DOI: 10.1016/j.isci.2023.107678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 06/23/2023] [Accepted: 08/14/2023] [Indexed: 09/28/2023] Open
Abstract
Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (1H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.
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Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Gianmaria Miolo
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
| | - Angela Buonadonna
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Davide Lombardi
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Agostino Steffan
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Simona Scalone
- Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
- GiottoBiotech s.r.l, Sesto Fiorentino, Italy
| | - Giuseppe Corona
- Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy
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8
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Xu W, Xue W, Zhou Z, Wang J, Qi H, Sun S, Jin T, Yao P, Zhao JY, Lin F. Formate Might Be a Novel Potential Serum Metabolic Biomarker for Type 2 Diabetic Peripheral Neuropathy. Diabetes Metab Syndr Obes 2023; 16:3147-3160. [PMID: 37842336 PMCID: PMC10576463 DOI: 10.2147/dmso.s428933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/04/2023] [Indexed: 10/17/2023] Open
Abstract
Background As one of the most frequent complications of type 2 diabetes mellitus (T2DM), diabetic peripheral neuropathy (DPN) shows a profound impact on 50% of patients with symptoms of neuropathic pain, numbness and other paresthesia. No valid serum biomarkers for the prediction of DPN have been identified in the clinic so far. This study is to investigate the potential serum biomarkers for DPN firstly based on 1H-Nuclear Magnetic Resonance (1H-NMR)-based metabolomics technique. Methods Thirty-six patients enrolled in this study were divided into two groups: 18 T2DM patients without DPN (T2DM group) and 18 T2DM patients with DPN (DPN group). Serum metabolites were measured via 1H-NMR spectroscopy. Bioinformatic approaches including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), independent sample t-test, Fisher's test, Pearson and Spearman correlation analysis, Stepwise multiple linear regression analysis and receiver operating characteristic (ROC) curve analysis were used to identify the potential altered serum biomarkers. Results A total of 20 metabolites were obtained and further analyzed. Formate was identified as the only potential biomarker that decreased in the DPN group with statistical significance after multiple comparisons (p<0.05). Formate also displayed a negative relationship with some elevated clinical markers in DPN. ROC curve analysis showed a good discriminative ability for formate in DPN with an area under the curve (AUC) value of 0.981. Conclusion Formate could be considered a potential serum metabolic biomarker for DPN. The reduced level of formate in DPN may be associated with mitochondrial dysfunction and gut microbiota alteration. Monitoring the level of serum formate would be an important strategy for the early diagnosis of DPN and a supplement of formate may be a promising treatment for DPN in the future.
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Affiliation(s)
- Weisheng Xu
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
- School of Medicine, Tongji University, Shanghai, 200331, People’s Republic of China
| | - Wangsheng Xue
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
| | - Zeyu Zhou
- School of Life Sciences, Fudan University, Shanghai, 200433, People’s Republic of China
| | - Jiying Wang
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
| | - Hui Qi
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
| | - Shiyu Sun
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
| | - Tong Jin
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
| | - Ping Yao
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
| | - Jian-Yuan Zhao
- Institute for Developmental and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200090, People’s Republic of China
| | - Fuqing Lin
- Department of Pain Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, People’s Republic of China
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9
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Gómez-Archila LG, Palomino-Schätzlein M, Zapata-Builes W, Rugeles MT, Galeano E. Plasma metabolomics by nuclear magnetic resonance reveals biomarkers and metabolic pathways associated with the control of HIV-1 infection/progression. Front Mol Biosci 2023; 10:1204273. [PMID: 37457832 PMCID: PMC10339029 DOI: 10.3389/fmolb.2023.1204273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
How the human body reacts to the exposure of HIV-1 is an important research goal. Frequently, HIV exposure leads to infection, but some individuals show natural resistance to this infection; they are known as HIV-1-exposed but seronegative (HESN). Others, although infected but without antiretroviral therapy, control HIV-1 replication and progression to AIDS; they are named controllers, maintaining low viral levels and an adequate count of CD4+ T lymphocytes. Biological mechanisms explaining these phenomena are not precise. In this context, metabolomics emerges as a method to find metabolites in response to pathophysiological stimuli, which can help to establish mechanisms of natural resistance to HIV-1 infection and its progression. We conducted a cross-sectional study including 30 HESN, 14 HIV-1 progressors, 14 controllers and 30 healthy controls. Plasma samples (directly and deproteinized) were analyzed through Nuclear Magnetic Resonance (NMR) metabolomics to find biomarkers and altered metabolic pathways. The metabolic profile analysis of progressors, controllers and HESN demonstrated significant differences with healthy controls when a discriminant analysis (PLS-DA) was applied. In the discriminant models, 13 metabolites associated with HESN, 14 with progressors and 12 with controllers were identified, which presented statistically significant mean differences with healthy controls. In progressors, the metabolites were related to high energy expenditure (creatinine), mood disorders (tyrosine) and immune activation (lipoproteins), phenomena typical of the natural course of the infection. In controllers, they were related to an inflammation-modulating profile (glutamate and pyruvate) and a better adaptive immune system response (acetate) associated with resistance to progression. In the HESN group, with anti-inflammatory (lactate and phosphocholine) and virucidal (lactate) effects which constitute a protective profile in the sexual transmission of HIV. Concerning the significant metabolites of each group, we identified 24 genes involved in HIV-1 replication or virus proteins that were all altered in progressors but only partially in controllers and HESN. In summary, our results indicate that exposure to HIV-1 in HESN, as well as infection in progressors and controllers, affects the metabolism of individuals and that this affectation can be determined using NMR metabolomics.
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Affiliation(s)
- León Gabriel Gómez-Archila
- Grupo de Investigación en Sustancias Bioactivas, Facultad de Ciencias Farmacéuticas y Alimentarias, Universidad de Antioquia (UdeA), Medellín, Colombia
- Grupo de Investigación en Ciencias Farmacéuticas ICIF-CES, Facultad de Ciencias y Biotecnología, Universidad CES, Medellín, Colombia
| | | | - Wildeman Zapata-Builes
- Grupo Inmunovirología, Facultad de Medicina, Universidad de Antioquia (UdeA), Medellín, Colombia
- Grupo Infettare, Facultad de Medicina, Universidad Cooperativa de Colombia, Medellín, Colombia
| | - Maria T. Rugeles
- Grupo Inmunovirología, Facultad de Medicina, Universidad de Antioquia (UdeA), Medellín, Colombia
| | - Elkin Galeano
- Grupo de Investigación en Sustancias Bioactivas, Facultad de Ciencias Farmacéuticas y Alimentarias, Universidad de Antioquia (UdeA), Medellín, Colombia
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10
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Ose J, Gigic B, Brezina S, Lin T, Peoples AR, Schobert PP, Baierl A, van Roekel E, Robinot N, Gicquiau A, Achaintre D, Scalbert A, van Duijnhoven FJB, Holowatyj AN, Gumpenberger T, Schrotz-King P, Ulrich AB, Ulvik A, Ueland PM, Weijenberg MP, Habermann N, Keski-Rahkonen P, Gsur A, Kok DE, Ulrich CM. Higher Plasma Creatinine Is Associated with an Increased Risk of Death in Patients with Non-Metastatic Rectal but Not Colon Cancer: Results from an International Cohort Consortium. Cancers (Basel) 2023; 15:3391. [PMID: 37444500 PMCID: PMC10340258 DOI: 10.3390/cancers15133391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/30/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Colorectal cancer (CRC) is increasingly recognized as a heterogeneous disease. No studies have prospectively examined associations of blood metabolite concentrations with all-cause mortality in patients with colon and rectal cancer separately. Targeted metabolomics (Biocrates AbsoluteIDQ p180) and pathway analyses (MetaboAnalyst 4.0) were performed on pre-surgery collected plasma from 674 patients with non-metastasized (stage I-III) colon (n = 394) or rectal cancer (n = 283). Metabolomics data and covariate information were received from the international cohort consortium MetaboCCC. Cox proportional hazards models were computed to investigate associations of 148 metabolite levels with all-cause mortality adjusted for age, sex, tumor stage, tumor site (whenever applicable), and cohort; the false discovery rate (FDR) was used to account for multiple testing. A total of 93 patients (14%) were deceased after an average follow-up time of 4.4 years (60 patients with colon cancer and 33 patients with rectal cancer). After FDR adjustment, higher plasma creatinine was associated with a 39% increase in all-cause mortality in patients with rectal cancer. HR: 1.39, 95% CI 1.23-1.72, pFDR = 0.03; but not colon cancer: pFDR = 0.96. Creatinine is a breakdown product of creatine phosphate in muscle and may reflect changes in skeletal muscle mass. The starch and sucrose metabolisms were associated with increased all-cause mortality in colon cancer but not in rectal cancer. Genes in the starch and sucrose metabolism pathways were previously linked to worse clinical outcomes in CRC. In summary, our findings support the hypothesis that colon and rectal cancer have different etiological and clinical outcomes that need to be considered for targeted treatments.
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Affiliation(s)
- Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Biljana Gigic
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, 69117 Heidelberg, Germany; (B.G.)
| | - Stefanie Brezina
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, 23, 1090 Vienna, Austria; (S.B.)
| | - Tengda Lin
- Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Anita R. Peoples
- Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Pauline P. Schobert
- Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
- School of Medicine, Ludwig-Maximilians University, 80539 Munich, Germany
- School of Medicine, Technical University of Munich, 80333 Munich, Germany
| | - Andreas Baierl
- Department of Statistics and Operations Research, University of Vienna, 1, 1010 Wien, Austria
| | - Eline van Roekel
- Department of Epidemiology, GROW-School of Oncology and Developmental Biology, Maastricht University, 30, 6229 Maastricht, The Netherlands
| | - Nivonirina Robinot
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, WHO, 69366 Lyon, France
| | - Audrey Gicquiau
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, WHO, 69366 Lyon, France
| | - David Achaintre
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, WHO, 69366 Lyon, France
| | - Augustin Scalbert
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, WHO, 69366 Lyon, France
| | | | - Andreana N. Holowatyj
- Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
| | - Tanja Gumpenberger
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, 23, 1090 Vienna, Austria; (S.B.)
| | - Petra Schrotz-King
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Alexis B. Ulrich
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, 69117 Heidelberg, Germany; (B.G.)
- Klinik für Allgemein-, Viszeral-, Thorax- und Gefäßchirurgie, Städtische Kliniken Neuss, 84, 41464 Neuss, Germany
| | | | | | - Matty P. Weijenberg
- Department of Epidemiology, GROW-School of Oncology and Developmental Biology, Maastricht University, 30, 6229 Maastricht, The Netherlands
| | - Nina Habermann
- Genome Biology, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, WHO, 69366 Lyon, France
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, 23, 1090 Vienna, Austria; (S.B.)
| | - Dieuwertje E. Kok
- Division of Human Nutrition and Health, Wageningen University & Research, 6708 Wageningen, The Netherlands
| | - Cornelia M. Ulrich
- Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
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11
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Abdel-Shafy EA, Melak T, MacIntyre DA, Zadra G, Zerbini LF, Piazza S, Cacciatore S. MetChem: a new pipeline to explore structural similarity across metabolite modules. BIOINFORMATICS ADVANCES 2023; 3:vbad053. [PMID: 37424942 PMCID: PMC10322652 DOI: 10.1093/bioadv/vbad053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/28/2023] [Accepted: 04/19/2023] [Indexed: 07/11/2023]
Abstract
Summary Computational analysis and interpretation of metabolomic profiling data remains a major challenge in translational research. Exploring metabolic biomarkers and dysregulated metabolic pathways associated with a patient phenotype could offer new opportunities for targeted therapeutic intervention. Metabolite clustering based on structural similarity has the potential to uncover common underpinnings of biological processes. To address this need, we have developed the MetChem package. MetChem is a quick and simple tool that allows to classify metabolites in structurally related modules, thus revealing their functional information. Availabilityand implementation MetChem is freely available from the R archive CRAN (http://cran.r-project.org). The software is distributed under the GNU General Public License (version 3 or later).
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Affiliation(s)
| | | | - David A MacIntyre
- March of Dimes Prematurity Research Centre, Imperial College London, London SW7 2AZ, UK
- Imperial College Parturition Research Group, Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Giorgia Zadra
- Institute of Molecular Genetics, National Research Council, Pavia 27100, Italy
| | - Luiz F Zerbini
- Cancer Genomics, International Centre for Genetic Engineering and Biotechnology, Cape Town 7925, South Africa
| | - Silvano Piazza
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste 34149, Italy
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12
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Wu S, Wang J, Fu Z, Familiari G, Relucenti M, Aschner M, Li X, Chen H, Chen R. Matairesinol Nanoparticles Restore Chemosensitivity and Suppress Colorectal Cancer Progression in Preclinical Models: Role of Lipid Metabolism Reprogramming. NANO LETTERS 2023; 23:1970-1980. [PMID: 36802650 DOI: 10.1021/acs.nanolett.3c00035] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Oncogenic-driven lipogenic metabolism is a common hallmark of colorectal cancer (CRC) progression. Therefore, there is an urgent need to develop novel therapeutic strategies for metabolic reprogramming. Herein, the metabolic profiles in the plasma between CRC patients and paired healthy controls were compared using metabolomics assays. Matairesinol downregulation was evident in CRC patients, and matairesinol supplementation significantly represses CRC tumorigenesis in azoxymethane/dextran sulfate sodium (AOM/DSS) colitis-associated CRC mice. Matairesinol rewired lipid metabolism to improve the therapeutic efficacy in CRC by inducing mitochondrial damage and oxidative damage and blunting ATP production. Finally, matairesinol-loaded liposomes significantly promoted the enhanced antitumor activity of 5-Fu/leucovorin combined with oxaliplatin (FOLFOX) in CDX and PDX mouse models by restoring chemosensitivity to the FOLFOX regimen. Collectively our findings highlight matairesinol-mediated lipid metabolism reprogramming as a novel druggable strategy to restore CRC chemosensitivity, and this nanoenabled approach for matairesinol will improve the chemotherapeutic efficacy with good biosafety.
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Affiliation(s)
- Shenshen Wu
- School of Public Health, Capital Medical University, Beijing 100069, P.R. China
| | - Jiajia Wang
- School of Public Health, Capital Medical University, Beijing 100069, P.R. China
| | - Zan Fu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P.R. China
| | - Giuseppe Familiari
- Department of Anatomical, Histological, Medical and Legal Locomotive Apparatus, Section of Human Anatomy Via Alfonso Borelli, Sapienza University of Rome, Roma 5000161, Italia
| | - Michela Relucenti
- Department of Anatomical, Histological, Forensic Medicine and Orthopedic Science, Sapienza University of Rome, Roma 5000161, Italia
| | - Michael Aschner
- Department of Molecular Pharmacology, Albert Einstein College of Medicine, Forchheimer 209, 1300 Morris Park Avenue, Bronx, New York 10461, United States
| | - Xiaobo Li
- School of Public Health, Capital Medical University, Beijing 100069, P.R. China
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, P.R. China
| | - Hanqing Chen
- Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, P.R. China
| | - Rui Chen
- School of Public Health, Capital Medical University, Beijing 100069, P.R. China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100069, P. R. China
- Beijing Laboratory of Allergic Diseases, Capital Medical University, Beijing 100069, P.R. China
- Institute for Chemical Carcinogenesis, Guangzhou Medical University, Guangzhou 511436, P.R. China
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13
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Costantini S, Di Gennaro E, Capone F, De Stefano A, Nasti G, Vitagliano C, Setola SV, Tatangelo F, Delrio P, Izzo F, Avallone A, Budillon A. Plasma metabolomics, lipidomics and cytokinomics profiling predict disease recurrence in metastatic colorectal cancer patients undergoing liver resection. Front Oncol 2023; 12:1110104. [PMID: 36713567 PMCID: PMC9875807 DOI: 10.3389/fonc.2022.1110104] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Purpose In metastatic colorectal cancer (mCRC) patients (pts), treatment strategies integrating liver resection with induction chemotherapy offer better 5-year survival rates than chemotherapy alone. However, liver resection is a complex and costly procedure, and recurrence occurs in almost 2/3rds of pts, suggesting the need to identify those at higher risk. The aim of this work was to evaluate whether the integration of plasma metabolomics and lipidomics combined with the multiplex analysis of a large panel of plasma cytokines can be used to predict the risk of relapse and other patient outcomes after liver surgery, beyond or in combination with clinical morphovolumetric criteria. Experimental design Peripheral blood metabolomics and lipidomics were performed by 600 MHz NMR spectroscopy on plasma from 30 unresectable mCRC pts treated with bevacizumab plus oxaliplatin-based regimens within the Obelics trial (NCT01718873) and subdivided into responder (R) and non-R (NR) according to 1-year disease-free survival (DFS): ≥ 1-year (R, n = 12) and < 1-year (NR, n = 18). A large panel of cytokines, chemokines, and growth factors was evaluated on the same plasma using Luminex xMAP-based multiplex bead-based immunoassay technology. A multiple biomarkers model was built using a support vector machine (SVM) classifier. Results Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing metabolomics profiles of samples collected at the time of response evaluation when resectability was established showed significantly different levels of metabolites between the two groups. Two metabolites, 3-hydroxybutyrate and histidine, significantly predicted DFS and overall survival. Lipidomics analysis confirmed clear differences between the R and NR pts, indicating a statistically significant increase in lipids (cholesterol, triglycerides and phospholipids) in NR pts, reflecting a nonspecific inflammatory response. Indeed, a significant increase in proinflammatory cytokines was demonstrated in NR pts plasma. Finally, a multiple biomarkers model based on the combination of presurgery plasma levels of 3-hydroxybutyrate, cholesterol, phospholipids, triglycerides and IL-6 was able to correctly classify patients by their DFS with good accuracy. Conclusion Overall, this exploratory study suggests the potential of these combined biomarker approaches to predict outcomes in mCRC patients who are candidates for liver metastasis resection after induction treatment for defining personalized management and treatment strategies.
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Affiliation(s)
- Susan Costantini
- Experimental Pharmacology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Elena Di Gennaro
- Experimental Pharmacology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Francesca Capone
- Experimental Pharmacology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Alfonso De Stefano
- Experimental Clinical Abdominal Oncology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Guglielmo Nasti
- Innovative Therapy for Abdominal Metastases Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Carlo Vitagliano
- Experimental Pharmacology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Sergio Venanzio Setola
- Radiology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Fabiana Tatangelo
- Pathology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Paolo Delrio
- Colorectal Oncological Surgery Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Francesco Izzo
- Hepatobiliary Surgery Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Antonio Avallone
- Experimental Clinical Abdominal Oncology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy
| | - Alfredo Budillon
- Experimental Pharmacology Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Napoli, Italy,*Correspondence: Alfredo Budillon,
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14
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Sun H, Zhang C, Zheng Y, Liu C, Wang X, Cong X. Glutamine deficiency promotes recurrence and metastasis in colorectal cancer through enhancing epithelial–mesenchymal transition. J Transl Med 2022; 20:330. [PMID: 35869517 PMCID: PMC9308325 DOI: 10.1186/s12967-022-03523-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Abstract
Background
Glutamine is the most abundant amino acid in the body and plays a vital role in colorectal cancer (CRC) cell metabolism. However, limited studies have investigated the clinical and prognostic significance of preoperative serum glutamine levels in patients with colorectal cancer, and the underlying mechanism has not been explored.
Methods
A total of 121 newly diagnosed CRC patients between 2012 and 2016 were enrolled in this study. Serum glutamine levels were detected, and their associations with clinicopathological characteristics, systemic inflammation markers, carcinoembryonic antigen (CEA) and prognosis were analysed. In addition, the effect of glutamine depletion on recurrence and metastasis was examined in SW480 and DLD1 human CRC cell lines, and epithelial–mesenchymal transition (EMT)-related markers were detected to reveal the possible mechanism.
Results
A decreased preoperative serum level of glutamine was associated with a higher T-class and lymph node metastasis (P < 0.05). A higher serum level of glutamine correlated with a lower CEA level (r = − 0.25, P = 0.02). Low glutamine levels were correlated with shorter overall survival (OS) and disease-free survival (DFS). Multivariate Cox regression analysis showed that serum glutamine was an independent prognostic factor for DFS (P = 0.018), and a nomogram predicting the probability of 1-, 3- and 5-year DFS after radical surgery was built. In addition, glutamine deficiency promoted the migration and invasion of CRC cells. E-cadherin, a vital marker of EMT, was decreased, and EMT transcription factors, including zeb1and zeb2, were upregulated in this process.
Conclusions
This study elucidated that preoperative serum glutamine is an independent prognostic biomarker to predict CRC progression and suggested that glutamine deprivation might promote migration and invasion in CRC cells by inducing the EMT process.
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Rani-AGARWAL N, Sarovar BHAVESH N, KACHHAWA G, Fatai OYEYEMI B. Metabolic profiling of Serum and urine in preeclampsia and gestational diabetes in early pregnancy. MEDICINE IN DRUG DISCOVERY 2022. [DOI: 10.1016/j.medidd.2022.100143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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16
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Barco S, Lavarello C, Cangelosi D, Morini M, Eva A, Oneto L, Uva P, Tripodi G, Garaventa A, Conte M, Petretto A, Cangemi G. Untargeted LC-HRMS Based-Plasma Metabolomics Reveals 3-O-Methyldopa as a New Biomarker of Poor Prognosis in High-Risk Neuroblastoma. Front Oncol 2022; 12:845936. [PMID: 35756625 PMCID: PMC9231354 DOI: 10.3389/fonc.2022.845936] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroblastoma (NB) is the most common extracranial malignant tumor in children. Although the survival rate of NB has improved over the years, the outcome of NB still remains poor for over 30% of cases. A more accurate risk stratification remains a key point in the study of NB and the availability of novel prognostic biomarkers of “high-risk” at diagnosis could help improving patient stratification and predicting outcome. In this paper we show a biomarker discovery approach applied to the plasma of 172 NB patients. Plasma samples from a first cohort of NB patients and age-matched healthy controls were used for untargeted metabolomics analysis based on high-resolution mass spectrometry (HRMS). Differential expression analysis highlighted a number of metabolites annotated with a high degree of identification. Among them, 3-O-methyldopa (3-O-MD) was validated in a second cohort of NB patients using a targeted metabolite profiling approach and its prognostic potential was also analyzed by survival analysis on patients with 3 years follow-up. High expression of 3-O-MD was associated with worse prognosis in the subset of patients with stage M tumor (log-rank p < 0.05) and, among them, it was confirmed as a prognostic factor able to stratify high-risk patients older than 18 months. 3-O-MD might be thus considered as a novel prognostic biomarker of NB eligible to be included at diagnosis among catecholamine metabolite panels in prospective clinical studies. Further studies are warranted to exploit other potential biomarkers highlighted using our approach.
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Affiliation(s)
- Sebastiano Barco
- Chromatography and Mass Spectrometry Section, Central Laboratory of Analysis, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Chiara Lavarello
- Core Facilities Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Davide Cangelosi
- Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Martina Morini
- Laboratory of Molecular Biology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Alessandra Eva
- Laboratory of Molecular Biology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Luca Oneto
- DIBRIS, University of Genoa, Genoa, Italy
| | - Paolo Uva
- Clinical Bioinformatics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Gino Tripodi
- Chromatography and Mass Spectrometry Section, Central Laboratory of Analysis, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Alberto Garaventa
- Department of Pediatric Oncology and Hematology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Massimo Conte
- Department of Pediatric Oncology and Hematology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Andrea Petretto
- Core Facilities Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Giuliana Cangemi
- Chromatography and Mass Spectrometry Section, Central Laboratory of Analysis, IRCCS Istituto Giannina Gaslini, Genoa, Italy
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17
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Wang G, Wang JJ, Xu XN, Shi F, Fu XL. Targeting cellular energy metabolism- mediated ferroptosis by small molecule compounds for colorectal cancer therapy. J Drug Target 2022; 30:819-832. [PMID: 35481396 DOI: 10.1080/1061186x.2022.2071909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Alterations in cellular energy metabolism, including glycolysis, glutamine and lipid metabolism that affects ferroptosis in the tumour microenvironment (TME), play a critical role in the development and progression of colorectal cancer (CRC) and offer evolutionary advantages to tumour cells and even enhance their aggressive phenotype. This review summarises the findings on the dysregulated energy metabolism pathways, including lipid and fatty acid metabolism especially for regulating the ferroptosis in TME. Moreover, the cellular energy metabolism and tumour ferroptosis to be regulated by small molecule compounds, which targeting the different aspects of metabolic pathways of energy production as well as metabolic enzymes that connect with the tumour cell growth and ferroptosis in CRC are also discussed. In this review, we will provide a comprehensive summary on small molecule compounds regulatory function of different energy metabolic routes on ferroptosis in tumour cells and discuss those metabolic vulnerabilities for the development of potential ferroptosis-based tumour therapies for colorectal cancer.
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Affiliation(s)
- Gang Wang
- Department of Pharmaceutics, Shanghai Eighth People's Hospital, Jiangsu University, Shanghai, China
| | - Jun-Jie Wang
- Department of Pharmaceutics, Shanghai Eighth People's Hospital, Jiangsu University, Shanghai, China
| | - Xiao-Na Xu
- Department of Medicine, Jiangsu University, Zhenjiang City, China
| | - Feng Shi
- Department of Medicine, Jiangsu University, Zhenjiang City, China
| | - Xing-Li Fu
- Department of Medicine, Jiangsu University, Zhenjiang City, China
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18
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Wang J, Qi S, Zhu YB, Ding L. Prognostic value of YKL-40 in colorectal carcinoma patients: A meta-analysis. World J Clin Cases 2022; 10:2184-2193. [PMID: 35321165 PMCID: PMC8895163 DOI: 10.12998/wjcc.v10.i7.2184] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 10/25/2021] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In recent years, the predictive role of YKL-40 for long-term survival in colorectal cancer patients has been gradually investigated. However, whether it is a reliable and valuable prognostic indicator for patients with colorectal carcinoma has not been verified.
AIM To identify the prognostic value of serum/plasma concentration of YKL-40 or expression status of YKL-40 in tumor cells in colorectal carcinoma patients.
METHODS Several electronic databases including the PubMed, EMBASE, Web of Science, CNKI, VIP and WanFang were searched for relevant studies. The hazard ratios (HR) and 95% confidence intervals (CI) were combined and the primary and secondary outcomes were overall survival (OS) and progression-free survival (PFS), respectively. All statistical analysis were conducted by STATA 15.0 software.
RESULTS A total of nine studies involving 2545 patients were included. The pooled results indicated that YKL-40 was significantly associated with poor OS (HR = 1.80, 95%CI: 1.32-2.45, P < 0.001) and PFS (HR = 1.62, 95%CI: 1.22-2.16, P = 0.001). Subgroup analysis stratified by the treatment, tumor type and source of YKL-40 showed similar results.
CONCLUSION Elevated serum/plasma concentration of YKL-40 or positive expression in tumor cells was related with worse prognosis of colorectal carcinoma patients. YKL-40 might serve as a novel and reliable indicator for the evaluation of prognosis in colorectal cancer.
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Affiliation(s)
- Jian Wang
- Colorectal Cancer Center, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
| | - Shi Qi
- Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
| | - Yu-Bing Zhu
- Colorectal Cancer Center, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
| | - Lei Ding
- Colorectal Cancer Center, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
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19
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Metabolomic Pathway Activity with Genomic Single-Nucleotide Polymorphisms Associated with Colorectal Cancer Recurrence and 5-Year Overall Survival. J Gastrointest Cancer 2022; 54:247-258. [PMID: 35239102 DOI: 10.1007/s12029-022-00813-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/12/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Metabolomic analysis in colorectal cancer (CRC) is an emerging research area with both prognostic and therapeutic targeting potential. We aimed to identify metabolomic pathway activity prognostic for CRC recurrence and overall survival and cross-reference such metabolomic data with prognostic genomic single-nucleotide polymorphisms (SNPs). METHODS A systematic search of PubMed, Embase and Cochrane Library was performed for studies reporting prognostic metabolomic pathway activity in CRC in keeping with PRISMA guidelines. The QUADOMICS tool was used to assess study quality. MetaboAnalyst software (version4.0) was used to map metabolites that were associated with recurrence and survival in CRC to recognise metabolic pathways and identify genomic SNPs associated with CRC prognosis, referencing the following databases: Human Metabolome Database (HMDB), the Small Molecule Pathway Database (SMPDB), PubChem and Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathway Database. RESULTS Nine studies met the inclusion criteria, reporting on 1117 patients. Increased metabolic activity in the urea cycle (p = 0.002, FDR = 0.198), ammonia recycling (p = 0.004, FDR = 0.359) and glycine and serine metabolism (p = 0.004, FDR = 0.374) was prognostic of CRC recurrence. Increased activity in aspartate metabolism (p < 0.001, FDR = 0.079) and ammonia recycling (p = 0.004, FDR = 0.345) was prognostic of survival. Eight resulting SNPs were prognostic for CRC recurrence (rs2194980, rs1392880, rs2567397, rs715, rs169712, rs2300701, rs313408, rs7018169) and three for survival (rs2194980, rs169712, rs12106698) of which two overlapped with recurrence (rs2194980, rs169712). CONCLUSIONS With a caveat on study heterogeneity, specific metabolites and metabolic pathway activity appear evident in the setting of poor prognostic colorectal cancers and such metabolic signatures are associated with specific genomic SNPs.
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20
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Su MW, Chang CK, Lin CW, Chu HW, Tsai TN, Su WC, Chen YC, Chang TK, Huang CW, Tsai HL, Wu CC, Chou HC, Shiu BH, Wang JY. Genomic and Metabolomic Landscape of Right-Sided and Left-Sided Colorectal Cancer: Potential Preventive Biomarkers. Cells 2022; 11:cells11030527. [PMID: 35159336 PMCID: PMC8834628 DOI: 10.3390/cells11030527] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 01/28/2022] [Accepted: 01/30/2022] [Indexed: 12/27/2022] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide. The incidence and mortality rates of CRC are significantly higher in Taiwan than in other developed countries. Genes involved in CRC tumorigenesis differ depending on whether the tumor occurs on the left or right side of the colon, and genomic analysis is a keystone in the study and treatment of CRC subtypes. However, few studies have focused on the genetic landscape of Taiwanese patients with CRC. This study comprehensively analyzed the genomes of 141 Taiwanese patients with CRC through whole-exome sequencing. Significant genomic differences related to the site of CRC development were observed. Blood metabolomic profiling and polygenic risk score analysis were performed to identify potential biomarkers for the early identification and prevention of CRC in the Taiwanese population. Our findings provide vital clues for establishing population-specific treatments and health policies for CRC prevention in Taiwan.
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Affiliation(s)
- Ming-Wei Su
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan; (M.-W.S.); (C.-K.C.); (C.-W.L.); ho (H.-W.C.)
| | - Chung-Ke Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan; (M.-W.S.); (C.-K.C.); (C.-W.L.); ho (H.-W.C.)
| | - Chien-Wei Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan; (M.-W.S.); (C.-K.C.); (C.-W.L.); ho (H.-W.C.)
| | - Hou-Wei Chu
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan; (M.-W.S.); (C.-K.C.); (C.-W.L.); ho (H.-W.C.)
| | - Tsen-Ni Tsai
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; (T.-N.T.); (W.-C.S.); (Y.-C.C.); (T.-K.C.); (C.-W.H.); (H.-L.T.)
| | - Wei-Chih Su
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; (T.-N.T.); (W.-C.S.); (Y.-C.C.); (T.-K.C.); (C.-W.H.); (H.-L.T.)
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Yen-Cheng Chen
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; (T.-N.T.); (W.-C.S.); (Y.-C.C.); (T.-K.C.); (C.-W.H.); (H.-L.T.)
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Tsung-Kun Chang
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; (T.-N.T.); (W.-C.S.); (Y.-C.C.); (T.-K.C.); (C.-W.H.); (H.-L.T.)
| | - Ching-Wen Huang
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; (T.-N.T.); (W.-C.S.); (Y.-C.C.); (T.-K.C.); (C.-W.H.); (H.-L.T.)
- Department of Surgery, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Hsiang-Lin Tsai
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; (T.-N.T.); (W.-C.S.); (Y.-C.C.); (T.-K.C.); (C.-W.H.); (H.-L.T.)
- Department of Surgery, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Chang-Chieh Wu
- Department of Surgery, Tri-Service General Hospital Keelung Branch, National Defense Medical Center, Keelung 20042, Taiwan;
| | - Huang-Chi Chou
- School of Medicine, Chung Shan Medical University, Taichung 402306, Taiwan; (H.-C.C.); (B.-H.S.)
- Division of Colon and Rectal Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 402306, Taiwan
| | - Bei-Hao Shiu
- School of Medicine, Chung Shan Medical University, Taichung 402306, Taiwan; (H.-C.C.); (B.-H.S.)
- Division of Colon and Rectal Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 402306, Taiwan
| | - Jaw-Yuan Wang
- Division of Colorectal Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; (T.-N.T.); (W.-C.S.); (Y.-C.C.); (T.-K.C.); (C.-W.H.); (H.-L.T.)
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Surgery, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Pingtung Hospital, Ministry of Health and Welfare, Pingtung 900, Taiwan
- Correspondence: & ; Tel.: +886-7-312-2805
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21
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Rattner JI, Kopciuk KA, Vogel HJ, Tang PA, Shapiro JD, Tu D, Jonker DJ, Siu LL, O'Callaghan CJ, Bathe OF. Early detection of treatment futility in patients with metastatic colorectal cancer. Oncotarget 2022; 13:61-72. [PMID: 35028011 PMCID: PMC8746015 DOI: 10.18632/oncotarget.28165] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/10/2021] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Chemotherapy options for treating CRC have rapidly expanded in recent years, and few have predictive biomarkers. Oncologists are challenged with evidence-based selection of treatments, and response is evaluated retrospectively based on serial imaging beginning after 2-3 months. As a result, cumulative toxicities may appear in patients who will not benefit. Early recognition of non-benefit would reduce cumulative toxicities. Our objective was to determine treatment-related changes in the circulating metabolome corresponding to treatment futility. METHODS Metabolomic studies were performed on serial plasma samples from patients with CRC in a randomized controlled trial of cetuximab vs. cetuximab + brivanib (N = 188). GC-MS quantified named 94 metabolites and concentrations were evaluated at baseline, Weeks 1, 4 and 12 after treatment initiation. In a discovery cohort (N = 68), a model distinguishing changes in metabolites associated with radiographic disease progression and response was generated using OPLS-DA. A cohort of 120 patients was used for validation of the model. RESULTS By one week after treatment, a stable model of 21 metabolites could distinguish between progression and partial response (R2Y = 0.859; Q2Y = 0.605; P = 5e-4). In the validation cohort, patients with the biomarker had a significantly shorter OS (P < 0.0001). In a separate cohort of patients with HCC on axitinib, appearance of the biomarker also signified a shorter PFS (1.7 months vs. 9.2 months, P = 0.001). CONCLUSION We have identified changes in the metabolome that appear within 1 week of starting treatment associated with treatment futility. The novel approach described is applicable to future efforts in developing a biomarker for early assessment of treatment efficacy.
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Affiliation(s)
- Jodi I Rattner
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Karen A Kopciuk
- Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, Canada
| | - Hans J Vogel
- Department Biological Sciences, Faculty of Science, University of Calgary, Calgary, Canada
| | - Patricia A Tang
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Jeremy D Shapiro
- Department of Medical Oncology, Monash University, Melbourne, Victoria, Australia
| | - Dongsheng Tu
- Department of Community Health and Epidemiology, Queens University, Kingston, Canada
| | - Derek J Jonker
- Division of Medical Oncology, Ottawa Hospital Cancer Centre, Ottawa, Canada
| | - Lillian L Siu
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada
| | - Chris J O'Callaghan
- Department of Community Health and Epidemiology, Queens University, Kingston, Canada
| | - Oliver F Bathe
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada
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22
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Salmerón AM, Tristán AI, Abreu AC, Fernández I. Serum Colorectal Cancer Biomarkers Unraveled by NMR Metabolomics: Past, Present, and Future. Anal Chem 2022; 94:417-430. [PMID: 34806875 PMCID: PMC8756394 DOI: 10.1021/acs.analchem.1c04360] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Ana M. Salmerón
- Department of Chemistry and
Physics, Research Centre CIAIMBITAL, University
of Almería, Ctra. Sacramento, s/n, 04120 Almería, Spain
| | - Ana I. Tristán
- Department of Chemistry and
Physics, Research Centre CIAIMBITAL, University
of Almería, Ctra. Sacramento, s/n, 04120 Almería, Spain
| | - Ana C. Abreu
- Department of Chemistry and
Physics, Research Centre CIAIMBITAL, University
of Almería, Ctra. Sacramento, s/n, 04120 Almería, Spain
| | - Ignacio Fernández
- Department of Chemistry and
Physics, Research Centre CIAIMBITAL, University
of Almería, Ctra. Sacramento, s/n, 04120 Almería, Spain
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23
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Zinga MM, Abdel-Shafy E, Melak T, Vignoli A, Piazza S, Zerbini LF, Tenori L, Cacciatore S. KODAMA exploratory analysis in metabolic phenotyping. Front Mol Biosci 2022; 9:1070394. [PMID: 36733493 PMCID: PMC9887019 DOI: 10.3389/fmolb.2022.1070394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
KODAMA is a valuable tool in metabolomics research to perform exploratory analysis. The advanced analytical technologies commonly used for metabolic phenotyping, mass spectrometry, and nuclear magnetic resonance spectroscopy push out a bunch of high-dimensional data. These complex datasets necessitate tailored statistical analysis able to highlight potentially interesting patterns from a noisy background. Hence, the visualization of metabolomics data for exploratory analysis revolves around dimensionality reduction. KODAMA excels at revealing local structures in high-dimensional data, such as metabolomics data. KODAMA has a high capacity to detect different underlying relationships in experimental datasets and correlate extracted features with accompanying metadata. Here, we describe the main application of KODAMA exploratory analysis in metabolomics research.
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Affiliation(s)
- Maria Mgella Zinga
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Department of Medical Parasitology and Entomology, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Ebtesam Abdel-Shafy
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- National Research Centre, Cairo, Egypt
| | - Tadele Melak
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Department of clinical chemistry, University of Gondar, Gondar, Ethiopia
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Silvano Piazza
- Computation Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
| | - Luiz Fernando Zerbini
- Cancer Genomics, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry “Ugo Schiff”, University of Florence, Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Stefano Cacciatore
- Bioinformatics Unit, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Institute of Reproductive and Developmental Biology, Imperial College London, London, United Kingdom
- *Correspondence: Stefano Cacciatore,
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Metabolomic Profiling Identified Serum Metabolite Biomarkers and Related Metabolic Pathways of Colorectal Cancer. DISEASE MARKERS 2021; 2021:6858809. [PMID: 34917201 PMCID: PMC8670981 DOI: 10.1155/2021/6858809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 11/13/2021] [Accepted: 11/18/2021] [Indexed: 12/21/2022]
Abstract
Background The screening and early detection of colorectal cancer (CRC) still remain a challenge due to the lack of reliable and effective serum biomarkers. Thus, this study is aimed at identifying serum biomarkers of CRC that could be used to distinguish CRC from healthy controls. Methods A prospective 1 : 2 individual matching case-control study was performed which included 50 healthy control subjects and 98 CRC patients. Untargeted metabolomic profiling was conducted with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify CRC-related metabolites and metabolic pathways. Results In total, 178 metabolites were detected, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish CRC patients from healthy controls. Nine metabolites showed significantly differential serum levels in CRC patients under the conditions of variable importance in projection (VIP) > 1, p < 0.05 using Student's t-test, and fold change (FC) ≥ 1.2 or ≤0.5. The above nine metabolites were 3-hydroxybutyric acid, hexadecanedioic acid, succinic acid semialdehyde, 4-dodecylbenzenesulfonic acid, prostaglandin B2, 2-pyrocatechuic acid, xanthoxylin, 12-hydroxydodecanoic acid, and formylanthranilic acid. Four potential biomarkers were identified to diagnose CRC through ROC curves: hexadecanedioic acid, 4-dodecylbenzenesulfonic acid, 2-pyrocatechuic acid, and formylanthranilic acid. All AUC values of these four serum biomarkers were above 0.70. In addition, the exploratory analysis of metabolic pathways revealed the activated states for the vitamin B metabolic pathway and the alanine, aspartate, and glutamate metabolic pathways associated with CRC. Conclusion The 4 identified potential metabolic biomarkers could discriminate CRC patients from healthy controls, and the 2 metabolic pathways may be activated in the CRC tissues.
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25
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Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background: Colorectal cancer (CRC) is the fourth most commonly diagnosed and third most deadly cancer worldwide. Surgery is the main treatment option for early disease; however, a relevant proportion of CRC patients relapse. Here, variations among preoperative and postoperative serum metabolomic fingerprint of CRC patients were studied, and possible associations between metabolic variations and cancer relapse were explored. Methods: A total of 41 patients with stage I-III CRC, planned for radical resection, were enrolled. Serum samples, collected preoperatively (t0) and 4–6 weeks after surgery before the start of any treatment (t1), were analyzed via NMR spectroscopy. NMR data were analyzed using multivariate and univariate statistical approaches. Results: Serum metabolomic fingerprints show differential clustering between t0 and t1 (82–85% accuracy). Pyruvate, HDL-related parameters, acetone, and 3-hydroxybutyrate appear to be the major players in this discrimination. Eight out of the 41 CRC patients enrolled developed cancer relapse. Postoperative, relapsed patients show an increase of pyruvate and HDL-related parameters, and a decrease of Apo-A1 Apo-B100 ratio and VLDL-related parameters. Conclusions: Surgery significantly alters the metabolomic fingerprint of CRC patients. Some metabolic changes seem to be associated with the development of cancer relapse. These data, if validated in a larger cohort, open new possibilities for risk stratification in patients with early-stage CRC.
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Nannini G, Meoni G, Tenori L, Ringressi MN, Taddei A, Niccolai E, Baldi S, Russo E, Luchinat C, Amedei A. Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps. World J Gastroenterol 2021; 27:6430-6441. [PMID: 34720532 PMCID: PMC8517777 DOI: 10.3748/wjg.v27.i38.6430] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/17/2021] [Accepted: 08/25/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors.
AIM To characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP).
METHODS Proton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index.
RESULTS NMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified.
CONCLUSION In conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments.
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Affiliation(s)
- Giulia Nannini
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Gaia Meoni
- Department of Chemistry “Ugo Schiff”, University of Florence, Florence 50134, Italy
| | - Leonardo Tenori
- Department of Chemistry “Ugo Schiff”, University of Florence, Florence 50134, Italy
| | - Maria Novella Ringressi
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Antonio Taddei
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Elena Niccolai
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Simone Baldi
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Edda Russo
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
| | - Claudio Luchinat
- Department of Chemistry & Magnetic Resonance Center (CERM), University of Florence, Florence 50134, Italy
| | - Amedeo Amedei
- Department of Clinical and Experimental Medicine, University of Florence, Florence 50134, Italy
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Serum Metabolomic and Lipoprotein Profiling of Pancreatic Ductal Adenocarcinoma Patients of African Ancestry. Metabolites 2021; 11:metabo11100663. [PMID: 34677378 PMCID: PMC8540259 DOI: 10.3390/metabo11100663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/06/2021] [Accepted: 09/08/2021] [Indexed: 12/12/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with a characteristic dysregulated metabolism. Abnormal clinicopathological features linked to defective metabolic and inflammatory response pathways can induce PDAC development and progression. In this study, we investigated the metabolites and lipoproteins profiles of PDAC patients of African ancestry. Nuclear Magnetic Resonance (NMR) spectroscopy was conducted on serum obtained from consenting individuals (34 PDAC, 6 Chronic Pancreatitis, and 6 healthy participants). Seventy-five signals were quantified from each NMR spectrum. The Liposcale test was used for lipoprotein characterization. Spearman's correlation and Kapan Meier tests were conducted for correlation and survival analyses, respectively. In our patient cohort, the results demonstrated that levels of metabolites involved in the glycolytic pathway increased with the tumour stage. Raised ethanol and 3-hydroxybutyrate were independently correlated with a shorter patient survival time, irrespective of tumour stage. Furthermore, increased levels of bilirubin resulted in an abnormal lipoprotein profile in PDAC patients. Additionally, we observed that the levels of a panel of metabolites (such as glucose and lactate) and lipoproteins correlated with those of inflammatory markers. Taken together, the metabolic phenotype can help distinguish PDAC severity and be used to predict patient survival and inform treatment intervention.
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Cacciatore S, Wium M, Licari C, Ajayi-Smith A, Masieri L, Anderson C, Salukazana AS, Kaestner L, Carini M, Carbone GM, Catapano CV, Loda M, Libermann TA, Zerbini LF. Inflammatory metabolic profile of South African patients with prostate cancer. Cancer Metab 2021; 9:29. [PMID: 34344464 PMCID: PMC8336341 DOI: 10.1186/s40170-021-00265-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 07/21/2021] [Indexed: 12/24/2022] Open
Abstract
Background Men with African ancestry are more likely to develop aggressive prostate cancer (PCa) and to die from this disease. The study of PCa in the South African population represents an opportunity for biomedical research due to the high prevalence of aggressive PCa. While inflammation is known to play a significant role in PCa progression, its association with tumor stage in populations of African descent has not been explored in detail. Identification of new metabolic biomarkers of inflammation may improve diagnosis of patients with aggressive PCa. Methods Plasma samples were profiled from 41 South African men with PCa using nuclear magnetic resonance (NMR) spectroscopy. A total of 41 features, including metabolites, lipid classes, total protein, and the inflammatory NMR markers, GlycA, and GlycB, were quantified from each NMR spectrum. The Bruker’s B.I.-LISA protocols were used to characterize 114 parameters related to the lipoproteins. The unsupervised KODAMA method was used to stratify the patients of our cohort based on their metabolic profile. Results We found that the plasma of patients with very high risk, aggressive PCa and high level of C-reactive protein have a peculiar metabolic phenotype (metabotype) characterized by extremely high levels of GlycA and GlycB. The inflammatory processes linked to the higher level of GlycA and GlycB are characterized by a deep change of the plasma metabolome that may be used to improve the stratification of patients with PCa. We also identified a not previously known relationship between high values of VLDL and low level of GlycB in a different metabotype of patients characterized by lower-risk PCa. Conclusions For the first time, a portrait of the metabolic changes in African men with PCa has been delineated indicating a strong association between inflammation and metabolic profiles. Our findings indicate how the metabolic profile could be used to identify those patients with high level of inflammation, characterized by aggressive PCa and short life expectancy. Integrating a metabolomic analysis as a tool for patient stratification could be important for opening the door to the development of new therapies. Further investigations are needed to understand the prevalence of an inflammatory metabotype in patients with aggressive PCa. Supplementary Information The online version contains supplementary material available at 10.1186/s40170-021-00265-6.
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Affiliation(s)
- Stefano Cacciatore
- Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa.,Institute for Reproductive and Developmental Biology, Imperial College, London, UK
| | - Martha Wium
- Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Cristina Licari
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy
| | - Aderonke Ajayi-Smith
- Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | - Lorenzo Masieri
- Department of Urology, Clinica Urologica I, Azienda Ospedaliera Careggi, University of Florence, Florence, Italy.,Pediatric Urology Unit, Meyer Children Hospital, University of Florence, Florence, Italy
| | - Chanelle Anderson
- Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
| | | | - Lisa Kaestner
- Division of Urology, University of Cape Town, Groote Schuur Hospital, Cape Town, South Africa
| | - Marco Carini
- Department of Urology, Clinica Urologica I, Azienda Ospedaliera Careggi, University of Florence, Florence, Italy
| | - Giuseppina M Carbone
- Institute of Oncology Research (IOR), Università della Svizzera italiana, Bellinzona, Switzerland
| | - Carlo V Catapano
- Institute of Oncology Research (IOR), Università della Svizzera italiana, Bellinzona, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,Department of Oncology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Massimo Loda
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.,Harvard Medical School, MA, Boston, USA
| | - Towia A Libermann
- Harvard Medical School, MA, Boston, USA.,BIDMC Genomics, Proteomics, Bioinformatics and Systems Biology Center, Beth Israel Deaconess Medical Center, MA, Boston, USA
| | - Luiz F Zerbini
- Cancer Genomics Group, International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa.
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Gao P, Huang X, Fang XY, Zheng H, Cai SL, Sun AJ, Zhao L, Zhang Y. Application of metabolomics in clinical and laboratory gastrointestinal oncology. World J Gastrointest Oncol 2021; 13:536-549. [PMID: 34163571 PMCID: PMC8204353 DOI: 10.4251/wjgo.v13.i6.536] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/09/2021] [Accepted: 05/19/2021] [Indexed: 02/06/2023] Open
Abstract
Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.
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Affiliation(s)
- Peng Gao
- Department ofClinical Laboratory, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Xin Huang
- Department of Internal Medicine, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Xue-Yan Fang
- Department of Nursing, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Hui Zheng
- Clinical Research Center, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Shu-Ling Cai
- Clinical Research Center, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Ai-Jun Sun
- Clinical Research Center, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Liang Zhao
- Department of Internal Medicine, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
| | - Yong Zhang
- Department of Surgery, Dalian Sixth People's Hospital, Dalian 116031, Liaoning Province, China
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30
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Di Donato S, Vignoli A, Biagioni C, Malorni L, Mori E, Tenori L, Calamai V, Parnofiello A, Di Pierro G, Migliaccio I, Cantafio S, Baraghini M, Mottino G, Becheri D, Del Monte F, Miceli E, McCartney A, Di Leo A, Luchinat C, Biganzoli L. A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting. Cancers (Basel) 2021; 13:cancers13112762. [PMID: 34199435 PMCID: PMC8199587 DOI: 10.3390/cancers13112762] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/14/2021] [Accepted: 05/29/2021] [Indexed: 12/26/2022] Open
Abstract
Simple Summary Around 30–40% of patients with early stage colorectal cancer (eCRC) experience relapse after surgery. Current recommendations for adjuvant therapy are based on suboptimal risk-stratification tools. In elderly patients, risk of relapse assessment is particularly important to ultimately avoid unnecessary chemotherapy-related toxicity in this frailer population. Serum metabolomics via NMR spectroscopy may improve risk stratification by identifying patients with residual micrometastases after surgery and thus at higher risk of relapse. We evaluated the serum metabolomic fingerprints of 94 elderly patients with eCRC (65 relapse free and 29 relapsed), and of 75 elderly patients with metastatic disease. Metabolomics efficiently discriminated patients with relapse-free eCRC from those with metastatic disease, correctly predicting relapse in 69% of relapsed eCRC patients. The metabolomic score was strongly and independently associated with prognosis. Our data suggest metabolomics as a valid addition to standard tools to refine risk stratification for eCRC and warrant further investigation. Abstract Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan–Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.
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Affiliation(s)
- Samantha Di Donato
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
- Correspondence: ; Tel.: +39-057-480-2520
| | - Alessia Vignoli
- Magnetic Resonance Center, University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.); (C.L.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Chiara Biagioni
- Bioinformatics Unit, Medical Oncology Department, New Hospital of Prato S. Stefano, 59100 Prato, Italy;
| | - Luca Malorni
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
- “Sandro Pitigliani” Translational Research Unit, New Hospital of Prato, Stefano, 59100 Prato, Italy;
| | - Elena Mori
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
| | - Leonardo Tenori
- Magnetic Resonance Center, University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.); (C.L.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Vanessa Calamai
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
| | - Annamaria Parnofiello
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
- Department of Medicine (DAME), University of Udine, 33100 Udine, Italy
| | - Giulia Di Pierro
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
| | - Ilenia Migliaccio
- “Sandro Pitigliani” Translational Research Unit, New Hospital of Prato, Stefano, 59100 Prato, Italy;
| | - Stefano Cantafio
- Department of Surgery, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (S.C.); (M.B.)
| | - Maddalena Baraghini
- Department of Surgery, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (S.C.); (M.B.)
| | - Giuseppe Mottino
- Department of Geriatrics, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (G.M.); (D.B.)
| | - Dimitri Becheri
- Department of Geriatrics, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (G.M.); (D.B.)
| | - Francesca Del Monte
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
| | - Elisangela Miceli
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
| | - Amelia McCartney
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
- School of Clinical Sciences, Monash University, 3168 Clayton, Australia
| | - Angelo Di Leo
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
| | - Claudio Luchinat
- Magnetic Resonance Center, University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.); (C.L.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
| | - Laura Biganzoli
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (L.M.); (E.M.); (V.C.); (A.P.); (G.D.P.); (F.D.M.); (E.M.); (A.M.); (A.D.L.); (L.B.)
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Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance. Cancers (Basel) 2021; 13:cancers13112634. [PMID: 34072010 PMCID: PMC8197817 DOI: 10.3390/cancers13112634] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Metabolic flexibility is one of the key hallmarks of cancer and metabolites are the final products of this adaptation, reflecting the aberrant changes of tumors. However, the metabolic plasticity of each cancer type is still unknown, and specifically to date, there are no data on metabolic profile in neuroendocrine tumors. The aim of our retrospective study was to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. We provided, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use, selecting a reduced set of metabolites of high diagnostic accuracy. We have identified 32 novel enriched metabolic pathways in NETs related with the TCA cycle, and with arginine, pyruvate or glutathione metabolism, which have distinct implications in oncogenesis and may open innovative avenues of clinical research. Abstract Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.
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Vignoli A, Risi E, McCartney A, Migliaccio I, Moretti E, Malorni L, Luchinat C, Biganzoli L, Tenori L. Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer. Int J Mol Sci 2021; 22:ijms22094687. [PMID: 33925233 PMCID: PMC8124948 DOI: 10.3390/ijms22094687] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/22/2022] Open
Abstract
Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).
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Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Emanuela Risi
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Amelia McCartney
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
- School of Clinical Sciences, Monash University, Melbourne 3800, Australia
| | - Ilenia Migliaccio
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Erica Moretti
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Luca Malorni
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
- Correspondence: ; Tel.: +39-055-457-4296
| | - Laura Biganzoli
- Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy; (E.R.); (A.M.); (I.M.); (E.M.); (L.M.); (L.B.)
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy; (A.V.); (L.T.)
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
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Serum metabolomic profiling correlated with ISS and clinical outcome for multiple myeloma patients treated with high-dose melphalan and autologous stem cell transplantation. Exp Hematol 2021; 97:79-88.e8. [PMID: 33609593 DOI: 10.1016/j.exphem.2021.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/27/2021] [Accepted: 02/13/2021] [Indexed: 11/20/2022]
Abstract
The metabolome, which is the final down-stream global product of metabolic processes in organisms, is not sufficiently described in multiple myeloma (MM) patients. The aim of this study was, therefore, to study the serum metabolomic profile using proton nuclear magnetic resonance (1H-NMR) spectroscopy, and its relationship to clinical characteristics and patient outcome. Serum samples, which were taken at diagnosis, from 201 MM patients who underwent high-dose melphalan followed by autologous stem cell transplantation as the first-line therapy, were analyzed. We found that the metabolomic profile differed between patients with different MM International Staging System (ISS) stages. The profile revealed increased levels of cholesterol, phospholipids, high-density lipoprotein, low-density lipoprotein, apolipoproteins A1 and A2, valine, and leucine in ISS I patients compared with ISS III patients. The metabolomic profile also differed between patients with IgA and IgG paraproteins, predominantly because of higher levels of high- and low-density lipoprotein subfractions in IgA patients. The exact pathway of metabolism leading to accumulation of these metabolites is still elusive, but this study indicates an area of interest for further investigation in the search for new therapy targets and prognostic markers for this disease.
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Metabolomic/lipidomic profiling of COVID-19 and individual response to tocilizumab. PLoS Pathog 2021; 17:e1009243. [PMID: 33524041 PMCID: PMC7877736 DOI: 10.1371/journal.ppat.1009243] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/11/2021] [Accepted: 12/18/2020] [Indexed: 02/07/2023] Open
Abstract
The current pandemic emergence of novel coronavirus disease (COVID-19) poses a relevant threat to global health. SARS-CoV-2 infection is characterized by a wide range of clinical manifestations, ranging from absence of symptoms to severe forms that need intensive care treatment. Here, plasma-EDTA samples of 30 patients compared with age- and sex-matched controls were analyzed via untargeted nuclear magnetic resonance (NMR)-based metabolomics and lipidomics. With the same approach, the effect of tocilizumab administration was evaluated in a subset of patients. Despite the heterogeneity of the clinical symptoms, COVID-19 patients are characterized by common plasma metabolomic and lipidomic signatures (91.7% and 87.5% accuracy, respectively, when compared to controls). Tocilizumab treatment resulted in at least partial reversion of the metabolic alterations due to SARS-CoV-2 infection. In conclusion, NMR-based metabolomic and lipidomic profiling provides novel insights into the pathophysiological mechanism of human response to SARS-CoV-2 infection and to monitor treatment outcomes. The current COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is markedly affecting the world population. Here we report about the small-molecule profile of patients hospitalized during the first wave of the COVID-19 pandemic in Florence (Italy). Using magnetic resonance spectroscopy, we showed that the infection induces profound changes in the metabolome. The analysis of the specific metabolite changes and correlations with clinical data enabled the identification of potential biochemical determinants of the disease fingerprint. We also followed how metabolic alterations revert towards those of the control group upon treatment with tocilizumab, a recombinant humanized monoclonal antibody against the interleukin-6 receptor. These results open up possibilities for the monitoring of novel patients and their individual response to treatment.
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Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods. Sci Rep 2021; 11:2282. [PMID: 33500529 PMCID: PMC7838178 DOI: 10.1038/s41598-021-81945-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 01/08/2021] [Indexed: 12/20/2022] Open
Abstract
Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.
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Yang B, Zhang C, Cheng S, Li G, Griebel J, Neuhaus J. Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine. Diagnostics (Basel) 2021; 11:149. [PMID: 33498542 PMCID: PMC7909529 DOI: 10.3390/diagnostics11020149] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/13/2021] [Accepted: 01/16/2021] [Indexed: 12/16/2022] Open
Abstract
Prostate cancer (PC) is one of the most common male cancers worldwide. Until now, there is no consensus about using urinary metabolomic profiling as novel biomarkers to identify PC. In this study, urine samples from 50 PC patients and 50 non-cancerous individuals (control group) were collected. Based on 1H nuclear magnetic resonance (1H-NMR) analysis, 20 metabolites were identified. Subsequently, principal component analysis (PCA), partial least squares-differential analysis (PLS-DA) and ortho-PLS-DA (OPLS-DA) were applied to find metabolites to distinguish PC from the control group. Furthermore, Wilcoxon test was used to find significant differences between the two groups in metabolite urine levels. Guanidinoacetate, phenylacetylglycine, and glycine were significantly increased in PC, while L-lactate and L-alanine were significantly decreased. The receiver operating characteristics (ROC) analysis revealed that the combination of guanidinoacetate, phenylacetylglycine, and glycine was able to accurately differentiate 77% of the PC patients with sensitivity = 80% and a specificity = 64%. In addition, those three metabolites showed significant differences in patients stratified for Gleason score 6 and Gleason score ≥7, indicating potential use to detect significant prostate cancer. Pathway enrichment analysis using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and the SMPDB (The Small Molecule Pathway Database) revealed potential involvement of KEGG "Glycine, Serine, and Threonine metabolism" in PC. The present study highlights that guanidinoacetate, phenylacetylglycine, and glycine are potential candidate biomarkers of PC. To the best knowledge of the authors, this is the first study identifying guanidinoacetate, and phenylacetylglycine as potential novel biomarkers in PC.
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Affiliation(s)
- Bo Yang
- Department of Urology, University of Leipzig, 04103 Leipzig, Germany; (B.Y.); (C.Z.)
- Department of Urology, Zhoupu Hospital, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
| | - Chuan Zhang
- Department of Urology, University of Leipzig, 04103 Leipzig, Germany; (B.Y.); (C.Z.)
| | - Sheng Cheng
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China;
| | - Gonghui Li
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China;
| | - Jan Griebel
- Leibniz Institute of Surface Engineering (IOM), Permoserstraße 15, 04318 Leipzig, Germany;
| | - Jochen Neuhaus
- Department of Urology, University of Leipzig, 04103 Leipzig, Germany; (B.Y.); (C.Z.)
- Department of Urology, Zhoupu Hospital, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
- Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China;
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Siddiqui MA, Pandey S, Azim A, Sinha N, Siddiqui MH. Metabolomics: An emerging potential approach to decipher critical illnesses. Biophys Chem 2020; 267:106462. [PMID: 32911125 PMCID: PMC9986419 DOI: 10.1016/j.bpc.2020.106462] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/18/2020] [Accepted: 08/23/2020] [Indexed: 12/15/2022]
Abstract
Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.
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Affiliation(s)
- Mohd Adnan Siddiqui
- Centre of Biomedical Research, SGPGIMS Campus, Lucknow 226014, India; Department of Bioengineering, Integral University, Lucknow 226026, India
| | - Swarnima Pandey
- Centre of Biomedical Research, SGPGIMS Campus, Lucknow 226014, India; Department of Zoology, Banaras Hindu University, Banaras 221005, India
| | - Afzal Azim
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Raebareli Road, Lucknow 226014, India.
| | - Neeraj Sinha
- Centre of Biomedical Research, SGPGIMS Campus, Lucknow 226014, India.
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Ghini V, Laera L, Fantechi B, del Monte F, Benelli M, McCartney A, Tenori L, Luchinat C, Pozzessere D. Metabolomics to Assess Response to Immune Checkpoint Inhibitors in Patients with Non-Small-Cell Lung Cancer. Cancers (Basel) 2020; 12:cancers12123574. [PMID: 33265926 PMCID: PMC7760033 DOI: 10.3390/cancers12123574] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 11/25/2020] [Accepted: 11/27/2020] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Recently, immunotherapy has presented new opportunities for clinical development in the treatment of non-small cell lung cancer (NSCLC). Although effective in sustaining overall survival in several clinical trials, not all the NSCLC patients respond to these treatments. Thus, a better patient selection, as well as the identification of predictive biomarkers of treatment efficacy, are of paramount importance. In this work, metabolomics was used with the aim of identifying responder with respect to non-responder subjects. We show that the metabolomic fingerprint of serum samples, collected before therapy, acts as a predictive biomarker to treatment response. Prospective identification of subjects that will benefit from immunotherapy could improve patient stratification, thus optimizing the treatment and avoiding unsuccessful strategies. Abstract In the treatment of advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitors have shown remarkable results. However, not all patients with NSCLC respond to this drug treatment or receive durable benefits. Thus, patient stratification and selection, as well as the identification of predictive biomarkers, represent pivotal aspects to address. In this framework, metabolomics can be used to support the discrimination between responders and non-responders. Here, metabolomics was used to analyze the sera samples from 50 patients with NSCL treated with immune checkpoint inhibitors. All the samples were collected before the beginning of the treatment and were analyzed by NMR spectroscopy and multivariate statistical analyses. Significantly, we show that the metabolomic fingerprint of serum acts as a predictive “collective” biomarker to immune checkpoint inhibitors response, being able to predict individual therapy outcome with > 80% accuracy. Metabolomics represents a potential strategy for the real-time selection and monitoring of patients treated with immunotherapy. The prospective identification of responders and non-responders could improve NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.
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Affiliation(s)
- Veronica Ghini
- Cirmmp, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy;
- Magnetic Resonance Center, CERM, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy;
| | - Letizia Laera
- Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy; (L.L.); (B.F.); (F.d.M.); (A.M.)
- Department of Oncology, Miulli hospital, Acquaviva delle Fonti, 70021 Bari, Italy
| | - Beatrice Fantechi
- Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy; (L.L.); (B.F.); (F.d.M.); (A.M.)
| | - Francesca del Monte
- Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy; (L.L.); (B.F.); (F.d.M.); (A.M.)
| | - Matteo Benelli
- Bioinformatics Unit, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy;
| | - Amelia McCartney
- Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy; (L.L.); (B.F.); (F.d.M.); (A.M.)
| | - Leonardo Tenori
- Magnetic Resonance Center, CERM, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy;
- Department of Chemistry, University of Florence, via della Lastruccia 3, 50019 Sesto Fiorentino, Florence, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center, CERM, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy;
- Department of Chemistry, University of Florence, via della Lastruccia 3, 50019 Sesto Fiorentino, Florence, Italy
- Correspondence: (C.L.); (D.P.); Tel.: +39-0554-574-296 (C.L.); +39-0574-802-520 (D.P.)
| | - Daniele Pozzessere
- Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy; (L.L.); (B.F.); (F.d.M.); (A.M.)
- Correspondence: (C.L.); (D.P.); Tel.: +39-0554-574-296 (C.L.); +39-0574-802-520 (D.P.)
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Dalal N, Jalandra R, Sharma M, Prakash H, Makharia GK, Solanki PR, Singh R, Kumar A. Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives. Biomed Pharmacother 2020; 131:110648. [PMID: 33152902 DOI: 10.1016/j.biopha.2020.110648] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/09/2020] [Accepted: 08/16/2020] [Indexed: 12/11/2022] Open
Abstract
Colorectal cancer (CRC) ranks third among the most commonly occurring cancers worldwide, and it causes half a million deaths annually. Alongside mechanistic study for CRC detection and treatment by conventional techniques, new technologies have been developed to study CRC. These technologies include genomics, transcriptomics, proteomics, and metabolomics which elucidate DNA markers, RNA transcripts, protein and, metabolites produced inside the colon and rectum part of the gut. All these approaches form the omics arena, which presents a remarkable opportunity for the discovery of novel prognostic, diagnostic and therapeutic biomarkers and also delineate the underlying mechanism of CRC causation, which may further help in devising treatment strategies. This review also mentions the latest developments in metagenomics and culturomics as emerging evidence suggests that metagenomics of gut microbiota has profound implications in the causation, prognosis, and treatment of CRC. A majority of bacteria cannot be studied as they remain unculturable, so culturomics has also been strengthened to develop culture conditions suitable for the growth of unculturable bacteria and identify unknown bacteria. The overall purpose of this review is to succinctly evaluate the application of omics technologies in colorectal cancer research for improving the diagnosis and treatment strategies.
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Affiliation(s)
- Nishu Dalal
- Gene Regulation Laboratory, National Institute of Immunology, New Delhi 110067, India; Department of Environmental Science, Satyawati College, Delhi University, Delhi 110052, India
| | - Rekha Jalandra
- Gene Regulation Laboratory, National Institute of Immunology, New Delhi 110067, India; Department of Zoology, Maharshi Dayanand University, Rohtak 124001, India
| | - Minakshi Sharma
- Department of Zoology, Maharshi Dayanand University, Rohtak 124001, India
| | - Hridayesh Prakash
- Amity Institute of Virology and Immunology, Amity University, Sector 125, Noida 201313, Uttar Pradesh, India
| | - Govind K Makharia
- Department of Gastroenterology and Human Nutrition, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Pratima R Solanki
- Special Centre for Nanoscience, Jawaharlal Nehru University, New Delhi 110067, India
| | - Rajeev Singh
- Department of Environmental Science, Satyawati College, Delhi University, Delhi 110052, India.
| | - Anil Kumar
- Gene Regulation Laboratory, National Institute of Immunology, New Delhi 110067, India.
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The tip of the iceberg for diagnostic dilemmas: Performance of current diagnostics and future complementary screening approaches. Eur J Med Genet 2020; 63:104089. [PMID: 33069933 DOI: 10.1016/j.ejmg.2020.104089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 09/15/2020] [Accepted: 10/12/2020] [Indexed: 11/24/2022]
Abstract
Genetic testing is currently the leading edge of clinical care when it comes to diagnostics. However, many questions remain unanswered even when employing next-generation sequencing techniques due to our inability to decode genetic variations and our limited repertoire of available diagnoses. Accordingly, diagnostic yields for current genomic screenings are <50% and fail to provide the whole picture, leaving the remaining patients without a definitive diagnosis. Human phenotypic/disease expression is explained by alterations not only at the genome, but also at the transcriptome, proteome and metabolome levels. These "higher" complexity levels represent at wealth of information, and diagnostic screenings tests at these levels have been shown to significantly improve diagnostic yields in specific populations compared to conventional diagnostic workup or gold standards in use (7-30% increase in diagnostic yields, depending on the population, approach and gold standard being compared against). However, these are not yet routinely available to clinicians. Due to their dynamic and modifiable nature, tapping into data from different omics will improve our understanding of the pathophysiological bases underlying (many yet to characterize) human disorders. We herein review how alterations at these levels (e.g. post-transcriptional and post-translational) may be pathogenic, how such tests may be implemented and in which situations they are of significant utility.
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Deng Y, Yao H, Chen W, Wei H, Li X, Zhang F, Gao S, Man H, Chen J, Tao X, Li M, Chen W. Profiling of polar urine metabolite extracts from Chinese colorectal cancer patients to screen for potential diagnostic and adverse-effect biomarkers. J Cancer 2020; 11:6925-6938. [PMID: 33123283 PMCID: PMC7592006 DOI: 10.7150/jca.47631] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 08/27/2020] [Indexed: 02/07/2023] Open
Abstract
Background: Metabolomics has demonstrated its potential in the early diagnosis, drug safety evaluation and personalized toxicology research of various cancers. Objectives: We aim to screen for potential diagnostic and capecitabine-related adverse effect (CRAE) biomarkers from urinary endogenous metabolites in Chinese colorectal cancer (CRC) patients. Methods: The metabolic profiles of 139 CRC patients and 50 non-neoplastic controls were analyzed using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry. Results: There were 41 metabolites identified between the CRC patients and the non-neoplastic controls, and 19 metabolites were identified between CRC patients with and without CRAE. Based on these identified metabolites, bioinformatic analysis and prediction model construction were completed. Most of these differential metabolites have important roles in cell proliferation and differentiation and the immune system. Based on binary logistic regression, a CRC prediction model, composed of 3-methylhistidine, N-heptanoylglycine, N1,N12-diacetylspermine and hippurate, was established, with an area under curve (AUC) of 0.980 (95% CI: 0.953-1.000; sensitivity: 94.3%; specificity: 92.0%) in the training set, and an AUC of 0.968 (95% CI: 0.933-1.000; sensitivity: 89.9%; specificity: 92.0%) in the testing set. In addition, methionine and 4-pyridoxic acid can be combined to predict hand foot syndrome, with an AUC of 0.884; ubiquinone-1 and 4-pyridoxic acid can be combined to predict anemia, with an AUC of 0.889; and 5-acetamidovalerate and 3,4-methylenesebacic acid can be combined to predict neutropenia, with an AUC of 0.882. Conclusion: The profiling of urine polar metabolites has great potential in the early detection of CRC and the prediction of CRAE.
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Affiliation(s)
- Yi Deng
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Houshan Yao
- Department of Surgery, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Wei Chen
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Hua Wei
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Xinxing Li
- Department of Surgery, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Feng Zhang
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Shouhong Gao
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Huan Man
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
- College of Chemical and Biological Engineering, Yichun University, Jiangxi Province, China, 336000
| | - Jing Chen
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
- College of Chemical and Biological Engineering, Yichun University, Jiangxi Province, China, 336000
| | - Xia Tao
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Mingming Li
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
| | - Wansheng Chen
- Department of Pharmacy, Changzheng Hospital, Secondary Military Medical University, Shanghai, China, 200003
- Research and Development Center of Chinese Medicine Resources and Biotechnology, Shanghai University of Traditional Chinese Medicine, Shanghai, China, 201203
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Di Giovanni N, Meuwis MA, Louis E, Focant JF. Specificity of metabolic colorectal cancer biomarkers in serum through effect size. Metabolomics 2020; 16:88. [PMID: 32789702 DOI: 10.1007/s11306-020-01707-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 07/30/2020] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Colorectal cancer is one of the most diagnosed cancers, leading to numerous deaths. In addition to existing screening methods, metabolic profiling could help both to diagnose and to understand the various states of the disease. OBJECTIVES Find specific candidate biomarkers (CB) in serum of patients with colorectal cancer (CRC), in comparison to the situation after remission (R-CRC), evaluated on distinct patients. METHODS All serum samples were analyzed using comprehensive two-dimensional gas chromatography (GC × GC) coupled to high resolution time of flight mass spectrometry (TOF-MS) through an optimized and validated untargeted analytical method regulated by a quality control (QC) system. First, we used a specific multi-approaches data (pre)processing workflow to highlight, annotate and assess the performances of the most altered metabolites between CRC patients (n = 18) and healthy control samples (HC, n = 19) specifically matched for age and gender, two of the most influential confounding factors. On the contrary, due to the difficulty to control for all clinical and demographic traits when sampling small cohorts, the samples from patients in remission (n = 17) were not matched. Because of the consequent risk of bias, the usual null hypothesis significance tests (NHST) could not be applied reliably. Therefore, we compared the R-CRC samples to another specifically matched group of healthy controls (R-HC, n = 17), and used this comparison to indirectly address the difference between patients with colorectal cancer and patients in remission through a measure called effect size (ES) whose methodological aspects were investigated. RESULTS 24 candidate biomarkers were found significantly altered and able to discriminate the CRC and HC samples efficiently (Receiver Operating Characteristic (ROC) area under the curve (AUC) of 0.86, sensitivity and specificity of 0.72 and 0.78). 10 of those were found to have signals close to healthy levels in the R-CRC samples and were therefore specific to colorectal cancer. In the point-biserial case studied here, r-like (strength of association) and d-like (standardized mean difference) ES were directly convertible and only linear and rank-based ES were different. We therefore used and recommend Hedges' g, Spearman's rho and Kendall's tau, along with an unstandardized ES. The confidence intervals, that quantify the uncertainty of the measure, were well represented through scatterplots and distribution curves. CONCLUSION The candidate biomarkers found, along with their specificity, could help for the detection of colorectal cancer, the diagnosis of remission, and for the understanding of its pathophysiology, after proper validation on independent cohorts. The effect size, here applied on a MS global profiling data set, is an ideal complement to NHST and a useful tool to compare and combine distinct cohorts, within a study as well as between studies (meta-analysis).
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Affiliation(s)
- Nicolas Di Giovanni
- Department of Chemistry, Organic & Biological Analytical Chemistry Group, University of Liège, Allée du 6 août, B6c, B-4000, Liège (Sart Tilman), Belgium
| | - Marie-Alice Meuwis
- GIGA Institute, Translational Gastroenterology and CHU de Liège, Hepato-Gastroenterology and Digestive Oncology, University of Liège, Quartier Hôpital, Avenue de l'hôpital 13, B34-35, B-4000, Liège, Belgium
| | - Edouard Louis
- GIGA Institute, Translational Gastroenterology and CHU de Liège, Hepato-Gastroenterology and Digestive Oncology, University of Liège, Quartier Hôpital, Avenue de l'hôpital 13, B34-35, B-4000, Liège, Belgium
| | - Jean-François Focant
- Department of Chemistry, Organic & Biological Analytical Chemistry Group, University of Liège, Allée du 6 août, B6c, B-4000, Liège (Sart Tilman), Belgium.
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Tang J, Wang Y, Luo Y, Fu J, Zhang Y, Li Y, Xiao Z, Lou Y, Qiu Y, Zhu F. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J 2020; 18:2012-2025. [PMID: 32802273 PMCID: PMC7403885 DOI: 10.1016/j.csbj.2020.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.
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Key Words
- ANN, Artificial Neural Network
- ANOVA, Analysis of Variance
- CFS, Correlation-based Feature Selection
- Cancer proteomics
- Computational methods
- DAPC, Discriminant Analysis of Principal Component
- DT, Decision Trees
- EDA, Estimation of Distribution Algorithm
- FC, Fold Change
- GA, Genetic Algorithms
- GR, Gain Ratio
- HC, Hill Climbing
- HCA, Hierarchical Cluster Analysis
- IG, Information Gain
- LDA, Linear Discriminant Analysis
- LIMMA, Linear Models for Microarray Data
- MBF, Markov Blanket Filter
- MWW, Mann–Whitney–Wilcoxon test
- OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis
- PCA, Principal Component Analysis
- PLS-DA, Partial Least Square Discriminant Analysis
- RF, Random Forest
- RF-RFE, Random Forest with Recursive Feature Elimination
- SA, Simulated Annealing
- SAM, Significance Analysis of Microarrays
- SBE, Sequential Backward Elimination
- SFS, and Sequential Forward Selection
- SOM, Self-organizing Map
- SU, Symmetrical Uncertainty
- SVM, Support Vector Machine
- SVM-RFE, Support Vector Machine with Recursive Feature Elimination
- Sample classification
- Tumor marker selection
- sPLSDA, Sparse Partial Least Squares Discriminant Analysis
- t-SNE, Student t Distribution
- χ2, Chi-square
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Affiliation(s)
- Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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44
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Nannini G, Meoni G, Amedei A, Tenori L. Metabolomics profile in gastrointestinal cancers: Update and future perspectives. World J Gastroenterol 2020; 26:2514-2532. [PMID: 32523308 PMCID: PMC7265149 DOI: 10.3748/wjg.v26.i20.2514] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/11/2020] [Accepted: 05/15/2020] [Indexed: 02/06/2023] Open
Abstract
Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.
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Affiliation(s)
- Giulia Nannini
- Department of Experimental and Clinical Medicine, University of Florence, Florence 50134, Italy
| | - Gaia Meoni
- Giotto Biotech Srl, and CERM (University of Florence), Florence 50019, Italy
| | - Amedeo Amedei
- Department of Experimental and Clinical Medicine, University of Florence, Florence 50134, Italy
- SOD of Interdisciplinary Internal Medicine, Azienda Ospedaliera Universitaria Careggi, Florence 50134, Italy
| | - Leonardo Tenori
- Consorzio Interuniversitario Risonanze Magnetiche di Metalloproteine, Florence 50019, Italy
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45
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Vignoli A, Paciotti S, Tenori L, Eusebi P, Biscetti L, Chiasserini D, Scheltens P, Turano P, Teunissen C, Luchinat C, Parnetti L. Fingerprinting Alzheimer's Disease by 1H Nuclear Magnetic Resonance Spectroscopy of Cerebrospinal Fluid. J Proteome Res 2020; 19:1696-1705. [PMID: 32118444 DOI: 10.1021/acs.jproteome.9b00850] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In this study, we sought for a cerebrospinal fluid (CSF) metabolomic fingerprint in Alzheimer's disease (AD) patients characterized, according to the clinical picture and CSF AD core biomarkers (Aβ42, p-tau, and t-tau), both at pre-dementia (mild cognitive impairment due to AD, MCI-AD) and dementia stages (ADdem) and in a group of patients with a normal CSF biomarker profile (non-AD) using untargeted 1H nuclear magnetic resonance (NMR) spectroscopy-based metabolomics. This is a retrospective study based on two independent cohorts: a Dutch cohort, which comprises 20 ADdem, 20 MCI-AD, and 20 non-AD patients, and an Italian cohort, constituted by 14 ADdem and 12 non-AD patients. 1H NMR CSF spectra were analyzed using OPLS-DA. Metabolomic fingerprinting in the Dutch cohort provides a significant discrimination (86.1% accuracy) between ADdem and non-AD. MCI-AD patients show a good discrimination with respect to ADdem (70.0% accuracy) but only slight differences when compared with non-AD (59.6% accuracy). Acetate, valine, and 3-hydroxyisovalerate result to be altered in ADdem patients. Valine correlates with cognitive decline at follow-up (R = 0.53, P = 0.0011). The discrimination between ADdem and non-AD was confirmed in the Italian cohort. The CSF metabolomic fingerprinting shows a signature characteristic of ADdem patients with respect to MCI-AD and non-AD patients.
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Affiliation(s)
- Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Sesto Fiorentino 50019, Italy
| | - Silvia Paciotti
- Laboratory of Clinical Neurochemistry, Department of Medicine, Section of Neurology, University of Perugia, Perugia 06123, Italy.,Department of Experimental Medicine, Section of Physiology and Biochemistry, University of Perugia, Perugia 06123, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Sesto Fiorentino 50019, Italy
| | - Paolo Eusebi
- Laboratory of Clinical Neurochemistry, Department of Medicine, Section of Neurology, University of Perugia, Perugia 06123, Italy
| | - Leonardo Biscetti
- Laboratory of Clinical Neurochemistry, Department of Medicine, Section of Neurology, University of Perugia, Perugia 06123, Italy
| | - Davide Chiasserini
- Department of Experimental Medicine, Section of Physiology and Biochemistry, University of Perugia, Perugia 06123, Italy
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, the Netherlands
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
| | - Charlotte Teunissen
- Department of Clinical Chemistry, Neurochemistry Lab and Biobank, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, the Netherlands
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Sesto Fiorentino 50019, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
| | - Lucilla Parnetti
- Laboratory of Clinical Neurochemistry, Department of Medicine, Section of Neurology, University of Perugia, Perugia 06123, Italy
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46
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Quintás G, Yáñez Y, Gargallo P, Juan Ribelles A, Cañete A, Castel V, Segura V. Metabolomic profiling in neuroblastoma. Pediatr Blood Cancer 2020; 67:e28113. [PMID: 31802629 DOI: 10.1002/pbc.28113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 10/14/2019] [Accepted: 11/11/2019] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND OBJECTIVES Previous studies on several cancer types show that metabolomics provides a potentially useful noninvasive screening approach for outcome prediction and accurate response to treatment assessment. Neuroblastoma (NB) accounts for at least 15% of cancer-related deaths in children. Although current risk-based treatment approaches in NB have resulted in improved outcome, survival for high-risk patients remains poor. This study aims to evaluate the use of metabolomics for improving patients' risk-group stratification and outcome prediction in NB. DESIGN AND METHODS Plasma samples from 110 patients with NB were collected at diagnosis prior to starting therapy and at the end of treatment if available. Metabolomic analysis of samples was carried out by ultra-performance liquid chromatography-time of flight mass spectrometry (UPLC-MS). RESULTS The metabolomic analysis was able to identify different plasma metabolic profiles in high-risk and low-risk NB patients at diagnosis. The metabolic model correctly classified 16 high-risk and 15 low-risk samples in an external validation set providing 84.2% sensitivity (60.4-96.6, 95% CI) and 93.7% specificity (69.8-99.8, 95% CI). Metabolomic profiling could also discriminate high-risk patients with active disease from those in remission. Notably, a plasma metabolomic signature at diagnosis identified a subset of high-risk NB patients who progressed during treatment. CONCLUSIONS To the best of our knowledge, this is the largest NB study investigating the prognostic power of plasma metabolomics. Our results support the potential of metabolomic profiling for improving NB risk-group stratification and outcome prediction. Additional validating studies with a large cohort are needed.
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Affiliation(s)
- Guillermo Quintás
- Leitat Technological Center, Health and Biomedicine Division, Barcelona, Spain.,Unidad Analítica, Instituto de Investigación Sanitaria Hospital La Fe, Valencia, Spain
| | - Yania Yáñez
- Pediatric Oncology Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Pablo Gargallo
- Pediatric Oncology Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Antonio Juan Ribelles
- Pediatric Oncology Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Adela Cañete
- Pediatric Oncology Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Victoria Castel
- Pediatric Oncology Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Vanessa Segura
- Pediatric Oncology Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
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Abstract
BACKGROUND Formate is a one-carbon molecule at the crossroad between cellular and whole body metabolism, between host and microbiome metabolism, and between nutrition and toxicology. This centrality confers formate with a key role in human physiology and disease that is currently unappreciated. SCOPE OF REVIEW Here we review the scientific literature on formate metabolism, highlighting cellular pathways, whole body metabolism, and interactions with the diet and the gut microbiome. We will discuss the relevance of formate metabolism in the context of embryonic development, cancer, obesity, immunometabolism, and neurodegeneration. MAJOR CONCLUSIONS We will conclude with an outlook of some open questions bringing formate metabolism into the spotlight.
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Affiliation(s)
| | - Johannes Meiser
- Department of Oncology, Luxembourg Institute of Health, L-1526 Luxembourg, Luxembourg
| | - Alexei Vazquez
- Cancer Research UK Beatson Institute, Glasgow, UK; Institute of Cancer Sciences, University of Glasgow, Glasgow, UK.
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48
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Garza DR, Taddese R, Wirbel J, Zeller G, Boleij A, Huynen MA, Dutilh BE. Metabolic models predict bacterial passengers in colorectal cancer. Cancer Metab 2020; 8:3. [PMID: 32055399 PMCID: PMC7008539 DOI: 10.1186/s40170-020-0208-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 01/07/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment. METHODS We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers. RESULTS We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome. CONCLUSION Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.
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Affiliation(s)
- Daniel R. Garza
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Postbus 9101, 6500 HB Nijmegen, The Netherlands
| | - Rahwa Taddese
- Department of Pathology, Radboud University Medical Center, Postbus 9101, 6500 Nijmegen, HB Netherlands
| | - Jakob Wirbel
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117 Heidelberg, Germany
| | - Georg Zeller
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117 Heidelberg, Germany
| | - Annemarie Boleij
- Department of Pathology, Radboud University Medical Center, Postbus 9101, 6500 Nijmegen, HB Netherlands
| | - Martijn A. Huynen
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Postbus 9101, 6500 HB Nijmegen, The Netherlands
| | - Bas E. Dutilh
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Postbus 9101, 6500 HB Nijmegen, The Netherlands
- Theoretical Biology and Bioinformatics, Sience4Life, Utrecht University, Hugo R. Kruytgebouw, Room Z-509, Padualaan 8, Utrecht, The Netherlands
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49
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Demarque DP, Dusi RG, de Sousa FDM, Grossi SM, Silvério MRS, Lopes NP, Espindola LS. Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products. Sci Rep 2020; 10:1051. [PMID: 31974423 PMCID: PMC6978511 DOI: 10.1038/s41598-020-58046-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 01/09/2020] [Indexed: 01/02/2023] Open
Abstract
Metabolomics is a powerful tool in the analysis and identification of metabolites responsible for biological properties. Regarding natural product chemistry, it constitutes a potential strategy to streamline the classic and laborious process of isolating natural products, which often involves the re-isolation and identification of known compounds. In this contribution, we establish a mass spectrometry-based metabolomics strategy to discover compounds with larvicidal activity against Aedes aegypti. We analyse the Brazilian plant Annona crassiflora using different platforms to annotate the active compounds in different extracts/fractions of various plant parts. The MetaboAnalyst and GNPS platforms, which consider LC-MS and LC-MS/MS data, respectively, were chosen to identify compounds that differentiate active and inactive samples. Bio-guided isolation was subsequently performed to confirm compound activity. Results proved the capacity of metabolomics to predict metabolite differences between active and inactive samples using LC-MS and LC-MS/MS data. Moreover, we discuss the limitations, possibilities, and strategies to have a broad view of vast data.
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Affiliation(s)
- Daniel P Demarque
- Laboratório de Farmacognosia, Universidade de Brasília, Brasília, Brazil
| | - Renata G Dusi
- Laboratório de Farmacognosia, Universidade de Brasília, Brasília, Brazil
| | | | - Sophia M Grossi
- Laboratório de Farmacognosia, Universidade de Brasília, Brasília, Brazil
| | - Maira R S Silvério
- Núcleo de Pesquisa em Produtos Naturais e Sintéticos, Departamento de Física e Química, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Norberto P Lopes
- Núcleo de Pesquisa em Produtos Naturais e Sintéticos, Departamento de Física e Química, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Laila S Espindola
- Laboratório de Farmacognosia, Universidade de Brasília, Brasília, Brazil.
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50
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Anaraki MT, Lysak DH, Soong R, Simpson MJ, Spraul M, Bermel W, Heumann H, Gundy M, Boenisch H, Simpson AJ. NMR assignment of the in vivo daphnia magna metabolome. Analyst 2020; 145:5787-5800. [DOI: 10.1039/d0an01280g] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Daphnia (freshwater fleas) are among the most widely used organisms in regulatory aquatic toxicology/ecology, while their recent listing as an NIH model organism is stimulating research for understanding human diseases and processes.
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Affiliation(s)
| | | | - Ronald Soong
- Department of Physical and Environmental Sciences
- University of Toronto Scarborough
- Toronto
- Canada
| | - Myrna J. Simpson
- Department of Physical and Environmental Sciences
- University of Toronto Scarborough
- Toronto
- Canada
- Department of Chemistry
| | | | | | | | | | | | - André J. Simpson
- Department of Physical and Environmental Sciences
- University of Toronto Scarborough
- Toronto
- Canada
- Department of Chemistry
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