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Parnas M, McLane-Svoboda AK, Cox E, McLane-Svoboda SB, Sanchez SW, Farnum A, Tundo A, Lefevre N, Miller S, Neeb E, Contag CH, Saha D. Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor. Biosens Bioelectron 2024; 261:116466. [PMID: 38850736 DOI: 10.1016/j.bios.2024.116466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 05/24/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.
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Affiliation(s)
- Michael Parnas
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Autumn K McLane-Svoboda
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Elyssa Cox
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Summer B McLane-Svoboda
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Simon W Sanchez
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Alexander Farnum
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Anthony Tundo
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Noël Lefevre
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Sydney Miller
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Emily Neeb
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Christopher H Contag
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA; Department of Microbiology, Genetics & Immunology, Michigan State University, East Lansing, MI, USA
| | - Debajit Saha
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA; Neuroscience Program, Michigan State University, East Lansing, MI, USA.
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2
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Espelage L, Wagner N, Placke JM, Ugurel S, Tasdogan A. The Interplay between Metabolic Adaptations and Diet in Cancer Immunotherapy. Clin Cancer Res 2024; 30:3117-3127. [PMID: 38771898 DOI: 10.1158/1078-0432.ccr-22-3468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/07/2023] [Accepted: 04/15/2024] [Indexed: 05/23/2024]
Abstract
Over the past decade, cancer immunotherapy has significantly advanced through the introduction of immune checkpoint inhibitors and the augmentation of adoptive cell transfer to enhance the innate cancer defense mechanisms. Despite these remarkable achievements, some cancers exhibit resistance to immunotherapy, with limited patient responsiveness and development of therapy resistance. Metabolic adaptations in both immune cells and cancer cells have emerged as central contributors to immunotherapy resistance. In the last few years, new insights emphasized the critical role of cancer and immune cell metabolism in animal models and patients. During therapy, immune cells undergo important metabolic shifts crucial for their acquired effector function against cancer cells. However, cancer cell metabolic rewiring and nutrient competition within tumor microenvironment (TME) alters many immune functions, affecting their fitness, polarization, recruitment, and survival. These interactions have initiated the development of novel therapies targeting tumor cell metabolism and favoring antitumor immunity within the TME. Furthermore, there has been increasing interest in comprehending how diet impacts the response to immunotherapy, given the demonstrated immunomodulatory and antitumor activity of various nutrients. In conclusion, recent advances in preclinical and clinical studies have highlighted the capacity of immune-based cancer therapies. Therefore, further exploration into the metabolic requirements of immune cells within the TME holds significant promise for the development of innovative therapeutic approaches that can effectively combat cancer in patients.
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Affiliation(s)
- Lena Espelage
- Department of Dermatology, University Hospital Essen and German Cancer Consortium (DKTK), Essen, Germany
| | - Natalie Wagner
- Department of Dermatology, University Hospital Essen and German Cancer Consortium (DKTK), Essen, Germany
| | - Jan-Malte Placke
- Department of Dermatology, University Hospital Essen and German Cancer Consortium (DKTK), Essen, Germany
| | - Selma Ugurel
- Department of Dermatology, University Hospital Essen and German Cancer Consortium (DKTK), Essen, Germany
| | - Alpaslan Tasdogan
- Department of Dermatology, University Hospital Essen and German Cancer Consortium (DKTK), Essen, Germany
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3
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Murcia-Mejía M, Canela-Capdevila M, García-Pablo R, Jiménez-Franco A, Jiménez-Aguilar JM, Badía J, Benavides-Villarreal R, Acosta JC, Arguís M, Onoiu AI, Castañé H, Camps J, Arenas M, Joven J. Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy. Biomolecules 2024; 14:898. [PMID: 39199286 PMCID: PMC11353221 DOI: 10.3390/biom14080898] [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] [Received: 06/14/2024] [Revised: 07/22/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC patients versus healthy controls and examined the effects of conventionally fractionated radiation therapy (CFRT) and stereotactic body radiation therapy (SBRT). We enrolled 91 NSCLC patients (38 CFRT and 53 SBRT) and 40 healthy controls. Plasma metabolite levels were assessed using semi-targeted metabolomics, revealing 32 elevated and 18 reduced metabolites in patients. Key discriminatory metabolites included ethylmalonic acid, maltose, 3-phosphoglyceric acid, taurine, glutamic acid, glycocolic acid, and d-arabinose, with a combined Receiver Operating Characteristics curve indicating perfect discrimination between patients and controls. CFRT and SBRT affected different metabolites, but both changes suggested a partial normalization of energy and amino acid metabolism pathways. In conclusion, metabolomics identified distinct metabolic signatures in NSCLC patients with potential as diagnostic biomarkers. The differing metabolic responses to CFRT and SBRT reflect their unique therapeutic impacts, underscoring the utility of this technique in enhancing NSCLC diagnosis and treatment monitoring.
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Affiliation(s)
- Mauricio Murcia-Mejía
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (M.M.-M.); (M.C.-C.); (R.G.-P.); (R.B.-V.); (J.C.A.); (M.A.)
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Marta Canela-Capdevila
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (M.M.-M.); (M.C.-C.); (R.G.-P.); (R.B.-V.); (J.C.A.); (M.A.)
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Raquel García-Pablo
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (M.M.-M.); (M.C.-C.); (R.G.-P.); (R.B.-V.); (J.C.A.); (M.A.)
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Andrea Jiménez-Franco
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Juan Manuel Jiménez-Aguilar
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Joan Badía
- Statistical Support Platform, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain;
| | - Rocío Benavides-Villarreal
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (M.M.-M.); (M.C.-C.); (R.G.-P.); (R.B.-V.); (J.C.A.); (M.A.)
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Johana C. Acosta
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (M.M.-M.); (M.C.-C.); (R.G.-P.); (R.B.-V.); (J.C.A.); (M.A.)
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Mónica Arguís
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (M.M.-M.); (M.C.-C.); (R.G.-P.); (R.B.-V.); (J.C.A.); (M.A.)
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Alina-Iuliana Onoiu
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Helena Castañé
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Jordi Camps
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Meritxell Arenas
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (M.M.-M.); (M.C.-C.); (R.G.-P.); (R.B.-V.); (J.C.A.); (M.A.)
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
| | - Jorge Joven
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain; (A.J.-F.); (J.M.J.-A.); (A.-I.O.); (H.C.); (J.J.)
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4
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Klupczynska-Gabryszak A, Daskalaki E, Wheelock CE, Kasprzyk M, Dyszkiewicz W, Grabicki M, Brajer-Luftmann B, Pawlak M, Kokot ZJ, Matysiak J. Metabolomics-based search for lung cancer markers among patients with different smoking status. Sci Rep 2024; 14:15444. [PMID: 38965272 PMCID: PMC11224321 DOI: 10.1038/s41598-024-65835-2] [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] [Received: 04/07/2024] [Accepted: 06/24/2024] [Indexed: 07/06/2024] Open
Abstract
Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.
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Affiliation(s)
| | - Evangelia Daskalaki
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
| | - Mariusz Kasprzyk
- Department of Thoracic Surgery, Poznan University of Medical Sciences, Poznan, Poland
| | - Wojciech Dyszkiewicz
- Department of Thoracic Surgery, Poznan University of Medical Sciences, Poznan, Poland
| | - Marcin Grabicki
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, Poznan, Poland
| | - Beata Brajer-Luftmann
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, Poznan, Poland
| | - Magdalena Pawlak
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Poznan, Poland
| | - Zenon J Kokot
- Faculty of Health Sciences, Calisia University, Kalisz, Poland
| | - Jan Matysiak
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Poznan, Poland
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5
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Meynen J, Adriaensens P, Criel M, Louis E, Vanhove K, Thomeer M, Mesotten L, Derveaux E. Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs. Int J Mol Sci 2024; 25:4690. [PMID: 38731909 PMCID: PMC11083579 DOI: 10.3390/ijms25094690] [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: 03/21/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.
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Affiliation(s)
- Jill Meynen
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium; (J.M.); (M.C.); (K.V.); (L.M.)
| | - Peter Adriaensens
- Applied and Analytical Chemistry, NMR Group, Institute for Materials Research (Imo-Imomec), Hasselt University, Agoralaan 1, B-3590 Diepenbeek, Belgium;
| | - Maarten Criel
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium; (J.M.); (M.C.); (K.V.); (L.M.)
- Department of Respiratory Medicine, Ziekenhuis Oost-Limburg, Synaps Park 1, B-3600 Genk, Belgium;
| | - Evelyne Louis
- Department of Respiratory Medicine, University Hospital Leuven, Herestraat 49, B-3000 Leuven, Belgium;
| | - Karolien Vanhove
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium; (J.M.); (M.C.); (K.V.); (L.M.)
- Department of Respiratory Medicine, University Hospital Leuven, Herestraat 49, B-3000 Leuven, Belgium;
- Department of Respiratory Medicine, Algemeen Ziekenhuis Vesalius, Hazelereik 51, B-3700 Tongeren, Belgium
| | - Michiel Thomeer
- Department of Respiratory Medicine, Ziekenhuis Oost-Limburg, Synaps Park 1, B-3600 Genk, Belgium;
| | - Liesbet Mesotten
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium; (J.M.); (M.C.); (K.V.); (L.M.)
- Department of Nuclear Medicine, Ziekenhuis Oost-Limburg, Synaps Park 1, B-3600 Genk, Belgium
| | - Elien Derveaux
- Applied and Analytical Chemistry, NMR Group, Institute for Materials Research (Imo-Imomec), Hasselt University, Agoralaan 1, B-3590 Diepenbeek, Belgium;
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6
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Li Q, Wei Z, Zhang Y, Zheng C. Causal role of metabolites in non-small cell lung cancer: Mendelian randomization (MR) study. PLoS One 2024; 19:e0300904. [PMID: 38517880 PMCID: PMC10959361 DOI: 10.1371/journal.pone.0300904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/06/2024] [Indexed: 03/24/2024] Open
Abstract
On a global scale, lung cancer(LC) is the most commonly occurring form of cancer. Nonetheless, the process of screening and detecting it in its early stages presents significant challenges. Earlier research endeavors have recognized metabolites as potentially reliable biomarkers for LC. However, the majority of these studies have been limited in scope, featuring inconsistencies in terms of the relationships and levels of association observed.Moreover, there has been a lack of consistency in the types of biological samples utilized in previous studies. Therefore, the main objective of our research was to explore the correlation between metabolites and Non-small cell lung cancer (NSCLC).Thorough two-sample Mendelian randomization (TSMR) analysis, we investigated potential cause-and-effect relationships between 1400 metabolites and the risk of NSCLC.The analysis of TSMR revealed a significant causal impact of 61 metabolites on NSCLC.To ensure the reliability and validity of our findings, we perform FDR correction for P-values by Benjaminiand Hochberg(BH) method, Our results indicate that Oleate/vaccenate (18:1) levels and Caffeine to paraxanthine ratio may be causally associated with an increased risk of NSCLC [Oleate/vaccenate(18:1)levels: OR = 1.171,95%CI: 1.085-1.265, FDR = 0.036; Caffeine to paraxanthine ratio: OR = 1.386, 95%CI:1.191-1.612,FDR = 0.032].
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Affiliation(s)
- Qian Li
- Department of Cardiac Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China
| | - Zedong Wei
- Department of Thoracic Surgery, Qianjiang Central Hospital, Qianjiang, 433100, China
| | - Yonglun Zhang
- Department of Thoracic Surgery, Qianjiang Central Hospital, Qianjiang, 433100, China
| | - Chongqing Zheng
- Department of Thoracic Surgery, Qianjiang Central Hospital, Qianjiang, 433100, China
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7
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Shi W, Cheng Y, Zhu H, Zhao L. Metabolomics and lipidomics in non-small cell lung cancer. Clin Chim Acta 2024; 555:117823. [PMID: 38325713 DOI: 10.1016/j.cca.2024.117823] [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: 12/18/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/09/2024]
Abstract
Due to its insidious nature, lung cancer remains a leading cause of cancer-related deaths worldwide. Therefore, there is an urgent need to identify sensitive/specific biomarkers for early diagnosis and monitoring. The current study was designed to provide a current metabolic profile of non-small cell lung cancer (NSCLC) by systematically reviewing and summarizing various metabolomic/ lipidomic studies based on NSCLC blood samples, attempting to find biomarkers in human blood that can predict or diagnose NSCLC, and investigating the involvement of key metabolites in the pathogenesis of NSCLC. We searched all articles on lung cancer published in Elsevier, PubMed, Web of Science and the Cochrane Library between January 2012 and December 2022. After critical selection, a total of 31 studies (including 2768 NSCLC patients and 9873 healthy individuals) met the inclusion criteria, and 22 were classified as "high quality". Forty-six metabolites related to NSCLC were repeatedly identified, involving glucose metabolism, amino acid metabolism, lipid metabolism and nucleotide metabolism. Pyruvic acid, carnitine, phenylalanine, isoleucine, kynurenine and 3-hydroxybutyrate showed upward trends in all studies, citric acid, glycine, threonine, cystine, alanine, histidine, inosine, betaine and arachidic acid showed downward trends in all studies. This review summarizes the existing metabolomic/lipidomic studies related to the identification of blood biomarkers in NSCLC, examines the role of key metabolites in the pathogenesis of NSCLC, and provides an important reference for the clinical diagnosis and treatment of NSCLC. Due to the limited size and design heterogeneity of the existing studies, there is an urgent need for standardization of future studies, while validating existing findings with more studies.
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Affiliation(s)
- Wei Shi
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China
| | - Yizhen Cheng
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China
| | - Haihua Zhu
- Betta Pharmaceuticals Co., Ltd, 24 Wuzhou Road Yuhang Economic and Technological Development Area, Hangzhou, Zhejiang Province, PR China
| | - Longshan Zhao
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China.
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8
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Liang S, Cao X, Wang Y, Leng P, Wen X, Xie G, Luo H, Yu R. Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types. Int J Med Sci 2024; 21:234-252. [PMID: 38169594 PMCID: PMC10758149 DOI: 10.7150/ijms.85704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 10/14/2023] [Indexed: 01/05/2024] Open
Abstract
Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.
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Affiliation(s)
- Simin Liang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Xiujun Cao
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Yingshuang Wang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Ping Leng
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Xiaoxia Wen
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Guojing Xie
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
| | - Huaichao Luo
- Department of Clinical Laboratory, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Rong Yu
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
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9
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Sanchez-Espirilla S, Pereira-Vega A, Callejón-Leblic B, Díaz-Olivares I, Santana R, Gotera Rivera C, Gómez-Ariza JL, López-Campos JL, Blanco-Orozco AI, Seijo L, Rodríguez M, Padrón Fraysse LA, Herrera-Chilla Á, Peces-Barba G, Barrera TG. Untargeted Metabolomic Study of Lung Cancer Patients after Surgery with Curative Intent. J Proteome Res 2023; 22:3499-3507. [PMID: 37843028 PMCID: PMC10629266 DOI: 10.1021/acs.jproteome.3c00356] [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] [Received: 06/20/2023] [Indexed: 10/17/2023]
Abstract
Lung cancer (LC) is a leading cause of mortality, claiming more than 1.8 million deaths per year worldwide. Surgery is one of the most effective treatments when the disease is in its early stages. The study of metabolic alterations after surgical intervention with curative intent could be used to assess the response to treatment or the detection of cancer recurrence. In this study, we have evaluated the metabolomic profile of serum samples (n = 110) from preoperative (PRE) and postoperative (POST) LC patients collected at two different time points (1 month, A; 3-6 months, B) with respect to healthy people. An untargeted metabolomic platform based on reversed phase (RP) and hydrophilic interaction chromatography (HILIC), using ultra-high performance liquid chromatography (UHPLC) and mass spectrometry (MS), was applied (MassIVE ID MSV000092213). Twenty-two altered metabolites were annotated by comparing all the different studied groups. DG(14,0/22:1), stearamide, proline, and E,e-carotene-3,3'-dione were found altered in PRE, and their levels returned to those of a baseline control group 3-6 months after surgery. Furthermore, 3-galactosyllactose levels remained altered after intervention in some patients. This study provides unique insights into the metabolic profiles of LC patients after surgery at two different time points by combining complementary analytical methods.
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Affiliation(s)
- Saida Sanchez-Espirilla
- Department
of Chemistry, Research Center for Natural Resources, Health and the
Environment (RENSMA), Faculty
of Experimental Sciences, University of
Huelva, Campus El Carmen, Fuerzas Armadas Ave., 21007 Huelva, Spain
- Department
of Chemistry, Faculty of Sciences, National
University of San Antonio Abad of Cusco, Av. de La Cultura, 773 Cusco, Peru
| | - Antonio Pereira-Vega
- Pneumology
Area of the Juan Ramón Jiménez Hospital, Ronda Norte, s/n, 21005 Huelva, Spain
| | - Belén Callejón-Leblic
- Department
of Chemistry, Research Center for Natural Resources, Health and the
Environment (RENSMA), Faculty
of Experimental Sciences, University of
Huelva, Campus El Carmen, Fuerzas Armadas Ave., 21007 Huelva, Spain
| | - Isabel Díaz-Olivares
- Department
of Chemistry, Research Center for Natural Resources, Health and the
Environment (RENSMA), Faculty
of Experimental Sciences, University of
Huelva, Campus El Carmen, Fuerzas Armadas Ave., 21007 Huelva, Spain
- Department
of Chemistry, Faculty of Sciences, National
University of San Antonio Abad of Cusco, Av. de La Cultura, 773 Cusco, Peru
- Pneumology
Area of the Juan Ramón Jiménez Hospital, Ronda Norte, s/n, 21005 Huelva, Spain
- IIS
Jiménez Díaz Foundation, ISCIII-CIBERES, Reyes Católicos Ave., 28040 Madrid, Spain
- Medical-Surgical
Unit of Respiratory Diseases, Institute
of Biomedicine of Seville (IBiS), Antonio Maura Montaner, 41013 Seville, Spain
- Virgen del
Rocío University Hospital/University of Seville, Manuel Siurot, s/n, 41013 Sevilla, Spain
- Center
for Biomedical Research in Respiratory Diseases Network (CIBERES), Carlos III Health Institute, Monforte de Lemos Ave., 28029 Madrid, Spain
- University
Clinic of Navarra, Marquesado
de Santa Marta Street, 1, 28027 Madrid, Spain
| | - Rafael Santana
- IIS
Jiménez Díaz Foundation, ISCIII-CIBERES, Reyes Católicos Ave., 28040 Madrid, Spain
| | | | - José Luis Gómez-Ariza
- Department
of Chemistry, Research Center for Natural Resources, Health and the
Environment (RENSMA), Faculty
of Experimental Sciences, University of
Huelva, Campus El Carmen, Fuerzas Armadas Ave., 21007 Huelva, Spain
| | - José Luis López-Campos
- Medical-Surgical
Unit of Respiratory Diseases, Institute
of Biomedicine of Seville (IBiS), Antonio Maura Montaner, 41013 Seville, Spain
- Virgen del
Rocío University Hospital/University of Seville, Manuel Siurot, s/n, 41013 Sevilla, Spain
- Center
for Biomedical Research in Respiratory Diseases Network (CIBERES), Carlos III Health Institute, Monforte de Lemos Ave., 28029 Madrid, Spain
| | - Ana Isabel Blanco-Orozco
- Medical-Surgical
Unit of Respiratory Diseases, Institute
of Biomedicine of Seville (IBiS), Antonio Maura Montaner, 41013 Seville, Spain
- Virgen del
Rocío University Hospital/University of Seville, Manuel Siurot, s/n, 41013 Sevilla, Spain
| | - Luis Seijo
- University
Clinic of Navarra, Marquesado
de Santa Marta Street, 1, 28027 Madrid, Spain
| | - María Rodríguez
- University
Clinic of Navarra, Marquesado
de Santa Marta Street, 1, 28027 Madrid, Spain
| | | | - Ángeles Herrera-Chilla
- Pneumology
Area of the Juan Ramón Jiménez Hospital, Ronda Norte, s/n, 21005 Huelva, Spain
| | - Germán Peces-Barba
- IIS
Jiménez Díaz Foundation, ISCIII-CIBERES, Reyes Católicos Ave., 28040 Madrid, Spain
| | - Tamara García Barrera
- Department
of Chemistry, Research Center for Natural Resources, Health and the
Environment (RENSMA), Faculty
of Experimental Sciences, University of
Huelva, Campus El Carmen, Fuerzas Armadas Ave., 21007 Huelva, Spain
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10
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Almalki AH. Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer. Metabolites 2023; 13:1037. [PMID: 37887362 PMCID: PMC10609104 DOI: 10.3390/metabo13101037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.
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Affiliation(s)
- Atiah H. Almalki
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
- Addiction and Neuroscience Research Unit, Health Science Campus, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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11
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Tardito S, MacKay C. Rethinking our approach to cancer metabolism to deliver patient benefit. Br J Cancer 2023; 129:406-415. [PMID: 37340094 PMCID: PMC10403540 DOI: 10.1038/s41416-023-02324-9] [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] [Received: 02/28/2023] [Revised: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 06/22/2023] Open
Abstract
Altered cellular metabolism is a major mechanism by which tumours support nutrient consumption associated with increased cellular proliferation. Selective dependency on specific metabolic pathways provides a therapeutic vulnerability that can be targeted in cancer therapy. Anti-metabolites have been used clinically since the 1940s and several agents targeting nucleotide metabolism are now well established as standard of care treatment in a range of indications. However, despite great progress in our understanding of the metabolic requirements of cancer and non-cancer cells within the tumour microenvironment, there has been limited clinical success for novel agents targeting pathways outside of nucleotide metabolism. We believe that there is significant therapeutic potential in targeting metabolic processes within cancer that is yet to be fully realised. However, current approaches to identify novel targets, test novel therapies and select patient populations most likely to benefit are sub-optimal. We highlight recent advances in technologies and understanding that will support the identification and validation of novel targets, re-evaluation of existing targets and design of optimal clinical positioning strategies to deliver patient benefit.
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Affiliation(s)
- Saverio Tardito
- The Cancer Research UK Beatson Institute, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Craig MacKay
- Cancer Research Horizons, The Cancer Research UK Beatson Institute, Glasgow, UK.
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12
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Xu Y, Dong X, Qin C, Wang F, Cao W, Li J, Yu Y, Zhao L, Tan F, Chen W, Li N, He J. Metabolic biomarkers in lung cancer screening and early diagnosis (Review). Oncol Lett 2023; 25:265. [PMID: 37216157 PMCID: PMC10193366 DOI: 10.3892/ol.2023.13851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/29/2023] [Indexed: 05/24/2023] Open
Abstract
Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.
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Affiliation(s)
- Yongjie Xu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Xuesi Dong
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Chao Qin
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Fei Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Wei Cao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Jiang Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Yiwen Yu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Liang Zhao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Wanqing Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China
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Kannampuzha S, Mukherjee AG, Wanjari UR, Gopalakrishnan AV, Murali R, Namachivayam A, Renu K, Dey A, Vellingiri B, Madhyastha H, Ganesan R. A Systematic Role of Metabolomics, Metabolic Pathways, and Chemical Metabolism in Lung Cancer. Vaccines (Basel) 2023; 11:vaccines11020381. [PMID: 36851259 PMCID: PMC9960365 DOI: 10.3390/vaccines11020381] [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: 01/10/2023] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Lung cancer (LC) is considered as one of the leading causes of cancer-associated mortalities. Cancer cells' reprogrammed metabolism results in changes in metabolite concentrations, which can be utilized to identify a distinct metabolic pattern or fingerprint for cancer detection or diagnosis. By detecting different metabolic variations in the expression levels of LC patients, this will help and enhance early diagnosis methods as well as new treatment strategies. The majority of patients are identified at advanced stages after undergoing a number of surgical procedures or diagnostic testing, including the invasive procedures. This could be overcome by understanding the mechanism and function of differently regulated metabolites. Significant variations in the metabolites present in the different samples can be analyzed and used as early biomarkers. They could also be used to analyze the specific progression and type as well as stages of cancer type making it easier for the treatment process. The main aim of this review article is to focus on rewired metabolic pathways and the associated metabolite alterations that can be used as diagnostic and therapeutic targets in lung cancer diagnosis as well as treatment strategies.
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Affiliation(s)
- Sandra Kannampuzha
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore 632014, India
| | - Anirban Goutam Mukherjee
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore 632014, India
| | - Uddesh Ramesh Wanjari
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore 632014, India
| | - Abilash Valsala Gopalakrishnan
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore 632014, India
- Correspondence: (A.V.G.); (R.G.)
| | - Reshma Murali
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore 632014, India
| | - Arunraj Namachivayam
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore 632014, India
| | - Kaviyarasi Renu
- Centre of Molecular Medicine and Diagnostics (COMManD), Department of Biochemistry, Saveetha Dental College & Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India
| | - Abhijit Dey
- Department of Life Sciences, Presidency University, Kolkata 700073, India
| | - Balachandar Vellingiri
- Stem Cell and Regenerative Medicine/Translational Research, Department of Zoology, School of Basic Sciences, Central University of Punjab (CUPB), Bathinda 151401, India
| | - Harishkumar Madhyastha
- Department of Cardiovascular Physiology, Faculty of Medicine, University of Miyazaki, Miyazaki 889-1692, Japan
| | - Raja Ganesan
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
- Correspondence: (A.V.G.); (R.G.)
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14
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Mei L, Zhang Z, Li X, Yang Y, Qi R. Metabolomics profiling in prediction of chemo-immunotherapy efficiency in advanced non-small cell lung cancer. Front Oncol 2023; 12:1025046. [PMID: 36733356 PMCID: PMC9887290 DOI: 10.3389/fonc.2022.1025046] [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: 08/22/2022] [Accepted: 12/28/2022] [Indexed: 01/18/2023] Open
Abstract
Background To explore potential metabolomics biomarker in predicting the efficiency of the chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Methods A total of 83 eligible patients were assigned to receive chemo-immunotherapy. Serum samples were prospectively collected before the treatment to perform metabolomics profiling analyses under the application of gas chromatography mass spectrometry (GC-MS). The key metabolites were identified using projection to latent structures discriminant analysis (PLS-DA). The key metabolites were used for predicting the chemo-immunotherapy efficiency in advanced NSCLC patients. Results Seven metabolites including pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate were identified as the key metabolites to the chemo-immunotherapy response. The receiver operating characteristic curves (AUC) were 0.79 (95% CI: 0.69-0.90), 0.60 (95% CI: 0.48-0.73), 0.69 (95% CI: 0.57-0.80), 0.63 (95% CI: 0.51-0.75), 0.60 (95% CI: 0.48-0.72), 0.56 (95% CI: 0.43-0.67), and 0.67 (95% CI: 0.55-0.80) for the key metabolites, respectively. A binary logistic regression was used to construct a combined biomarker model to improve the discriminating efficiency. The AUC was 0.86 (95% CI: 0.77-0.94) for the combined biomarker model. Pathway analyses showed that urea cycle, glucose-alanine cycle, glycine and serine metabolism, alanine metabolism, and glutamate metabolism were the key metabolic pathway to the chemo-immunotherapy response in patients with advanced NSCLC. Conclusion Metabolomics analyses of key metabolites and pathways revealed that GC-MS could be used to predict the efficiency of chemo-immunotherapy. Pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate played a central role in the metabolic of PD patients with advanced NSCLC.
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Affiliation(s)
- Lihong Mei
- Department of Dermatology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Zhihua Zhang
- Department of Echocardiography, Jinshan Hospital, Fudan University, Shanghai, China
| | - Xushuo Li
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, China
| | - Ying Yang
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, China
| | - Ruixue Qi
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, China,*Correspondence: Ruixue Qi,
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15
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Qiu Y, Zhang M, Lai Z, Zhang R, Tian H, Liu S, Li D, Zhou J, Li Z. Profiling of amines in biological samples using polythioester-functionalized magnetic nanoprobe. Front Bioeng Biotechnol 2023; 10:1103995. [PMID: 36686230 PMCID: PMC9846243 DOI: 10.3389/fbioe.2022.1103995] [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: 11/21/2022] [Accepted: 12/19/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction: The metabolic balance of amines is closely related to human health. It remains a great challenge to analyze amines with high-throughput and high-coverage. Methods: Polythioester-functionalized magnetic nanoprobes (PMPs) have been prepared under mild conditions and applied in chemoselective capture of amides. With the introduction of polythioester, PMPs demonstrate remarkably increased capture efficiency, leading to the dramatically improved sensitivity of mass spectrometry detection. Results: The analysis method with PMPs treatment has been applied in rapid detection of more than 100 amines in lung adenocarcinoma cell lines, mouse organ tissues, and 103 human serum samples with high-throughput and high-coverage. Statistical analysis shows that arginine biosynthesis differed between lung adenocarcinoma cell lines. Discussion: Phenylalanine, tyrosine and tryptophan biosynthesis differed between tissues. The combination indicators demonstrate a great diagnostic accuracy for distinguishing between health and lung disease subjects as well as differentiating the patients with benign lung disease and lung cancer. With powerful capture ability, low-cost preparation, and convenient separation, the PMPs demonstrate promising application in the intensive study of metabolic pathways and early diagnosis of disease.high-throughput and high-coverage. Here, polythioester-functionalized magnetic nanoprobes (PMPs) have been prepared under mild conditions and applied in chemoselective capture of amides. With the introduction of polythioester, PMPs demonstrate remarkably increased capture efficiency, leading to the dramatically improved sensitivity of mass spectrometry detection. The analysis method with PMPs treatment has been applied in rapid detection of more than 100 amines in lung adenocarcinoma cell lines, mouse organ tissues, and 103 human serum samples with high-throughput and high-coverage. Statistical analysis shows that arginine biosynthesis differed between lung adenocarcinoma cell lines. Phenylalanine, tyrosine and tryptophan biosynthesis differed between tissues. The combination indicators demonstrate a great diagnostic accuracy for distinguishing between health and lung disease subjects as well as differentiating the patients with benign lung disease and lung cancer. With powerful capture ability, low-cost preparation, and convenient separation, the PMPs demonstrate promising application in the intensive study of metabolic pathways and early diagnosis of disease.
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Affiliation(s)
- Yuming Qiu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mo Zhang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhizhen Lai
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renjun Zhang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongtao Tian
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuai Liu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dan Li
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Zhou
- Analytical Instrumentation Center, College of Chemistry and Molecular Engineering, Peking University, Beijing, China,*Correspondence: Zhili Li, ; Jiang Zhou,
| | - Zhili Li
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,*Correspondence: Zhili Li, ; Jiang Zhou,
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16
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Lin YS, Chen YC, Chen TE, Cheng ML, Lynn KS, Shah P, Chen JS, Huang RFS. Probing Folate-Responsive and Stage-Sensitive Metabolomics and Transcriptional Co-Expression Network Markers to Predict Prognosis of Non-Small Cell Lung Cancer Patients. Nutrients 2022; 15:nu15010003. [PMID: 36615660 PMCID: PMC9823804 DOI: 10.3390/nu15010003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/06/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022] Open
Abstract
Tumour metabolomics and transcriptomics co-expression network as related to biological folate alteration and cancer malignancy remains unexplored in human non-small cell lung cancers (NSCLC). To probe the diagnostic biomarkers, tumour and pair lung tissue samples (n = 56) from 97 NSCLC patients were profiled for ultra-performance liquid chromatography tandem mass spectrometry (UPLC/MS/MS)-analysed metabolomics, targeted transcriptionomics, and clinical folate traits. Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Tumour lactate was identified as the top VIP marker to predict advance NSCLC (AUC = 0.765, Sig = 0.017, CI 0.58-0.95). Low folate (LF)-tumours vs. adjacent lungs displayed higher glycolytic index of lactate and glutamine-associated amino acids in enriched biological pathways of amino sugar and glutathione metabolism specific to advance NSCLCs. WGCNA classified the green module for hub serine-navigated glutamine metabolites inversely associated with tumour and RBC folate, which module metabolites co-expressed with a predominant up-regulation of LF-responsive metabolic genes in glucose transport (GLUT1), de no serine synthesis (PHGDH, PSPH, and PSAT1), folate cycle (SHMT1/2 and PCFR), and down-regulation in glutaminolysis (SLC1A5, SLC7A5, GLS, and GLUD1). The LF-responsive WGCNA markers predicted poor survival rates in lung cancer patients, which could aid in optimizing folate intervention for better prognosis of NSCLCs susceptible to folate malnutrition.
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Affiliation(s)
- Yu-Shun Lin
- Department of Nutritional Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Yen-Chu Chen
- Department of Nutritional Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Tzu-En Chen
- Department of Nutritional Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Mei-Ling Cheng
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ke-Shiuan Lynn
- Department of Mathematics, Fu Jen Catholic University, New Taipei City 242062, Taiwan
| | - Pramod Shah
- Department of Nutritional Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Praexisio Taiwan Inc., New Taipei City 22180, Taiwan
| | - Jin-Shing Chen
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital, Taipei 100225, Taiwan
- Correspondence: (J.-S.C.); (R.-F.S.H.); Tel.: +886-2-2905-2512 (R.-F.S.H.)
| | - Rwei-Fen S. Huang
- Department of Nutritional Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Correspondence: (J.-S.C.); (R.-F.S.H.); Tel.: +886-2-2905-2512 (R.-F.S.H.)
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17
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Zhang Y, Cheng Y, Qin L, Liu Y, Huang S, Dai L, Tao J, Pan J, Su C, Zhang Y. Plasma metabolomics for the assessment of the progression of non-small cell lung cancer. Int J Biol Markers 2022; 38:37-45. [PMID: 36377344 DOI: 10.1177/03936155221137359] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objectives Non-small cell lung cancer (NSCLC) is a leading type of lung cancer with a high mortality rate worldwide. Although many procedures for the diagnosis and prognosis assessment of lung cancer exist, they are often laborious, expensive, and invasive. This study aimed to develop an ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC–MS/MS)-based analysis method for the plasma biomarkers of NSCLC with the potential to indicate the stages and progression of this malignancy conveniently and reliably. Methods A total of 53 patients with NSCLC in early stages (I–III) and advanced stage (IV) were classified into the early and advanced groups based on the tumor node metastasis staging system. A comprehensive metabolomic analysis of plasma from patients with NSCLC was performed via UPLC–MS/MS. Principal component analysis and partial least squares–discriminant analysis were conducted for statistical analysis. Potential biomarkers were evaluated and screened through receiver operating characteristic analyses and correlation analysis. Main differential metabolic pathways were also identified by utilizing metaboanalyst. Results A total of 129 differential metabolites were detected in accordance with the criteria of VIP ≥ 1 and a P-value of ≤ 0.05. The receiver operating characteristic curves indicated that 11 of these metabolites have the potential to be promising markers of disease progression. Apparent correlated metabolites were also filtered out. Furthermore, the 11 most predominant metabolic pathways with alterations involved in NSCLC were identified. Conclusion Our study focused on the plasma metabolomic changes in patients with NSCLC. These changes may be used for the prediction of the stage and progression of NSCLC. Moreover, we discussed the metabolic pathways wherein the altered metabolites were mainly enriched.
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Affiliation(s)
- Yingtian Zhang
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Yaping Cheng
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Liqiang Qin
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, Jiangsu, PR China
| | - Yuanliang Liu
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Sijia Huang
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Liya Dai
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Jialong Tao
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Jie Pan
- Department of Pharmacy, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Cunjin Su
- Department of Pharmacy, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
| | - Yusong Zhang
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, PR China
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Kim JO, Balshaw R, Trevena C, Banerji S, Murphy L, Dawe D, Tan L, Srinathan S, Buduhan G, Kidane B, Qing G, Domaratzki M, Aliani M. Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls. Cancer Metab 2022; 10:16. [PMID: 36224630 PMCID: PMC9559833 DOI: 10.1186/s40170-022-00294-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 09/22/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Metabolomics is a potential means for biofluid-based lung cancer detection. We conducted a non-targeted, data-driven assessment of plasma from early-stage non-small cell lung cancer (ES-NSCLC) cases versus cancer-free controls (CFC) to explore and identify the classes of metabolites for further targeted metabolomics biomarker development. METHODS Plasma from 250 ES-NSCLC cases and 250 CFCs underwent ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive and negative electrospray ionization (ESI) modes. Molecular feature extraction, formula generation, and find-by-ion tools annotated metabolic entities. Analysis was restricted to endogenous metabolites present in ≥ 80% of samples. Unsupervised hierarchical cluster analysis identified clusters of metabolites. The metabolites with the strongest correlation with the principal component of each cluster were included in logistic regression modeling to assess discriminatory performance with and without adjustment for clinical covariates. RESULTS A total of 1900 UHPLC-QTOF-MS assessments identified 1667 and 2032 endogenous metabolites in the ESI-positive and ESI-negative modes, respectively. After data filtration, 676 metabolites remained, and 12 clusters of metabolites were identified from each ESI mode. Multivariable logistic regression using the representative metabolite from each cluster revealed effective classification of cases from controls with overall diagnostic accuracy of 91% (ESI positive) and 94% (ESI negative). Metabolites of interest identified for further targeted analysis include the following: 1b, 3a, 12a-trihydroxy-5b-cholanoic acid, pyridoxamine 5'-phosphate, sphinganine 1-phosphate, gamma-CEHC, 20-carboxy-leukotriene B4, isodesmosine, and 18-hydroxycortisol. CONCLUSIONS Plasma-based metabolomic detection of early-stage NSCLC appears feasible. Further metabolomics studies targeting phospholipid, steroid, and fatty acid metabolism are warranted to further develop noninvasive metabolomics-based detection of early-stage NSCLC.
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Affiliation(s)
- Julian O Kim
- Section of Radiation Oncology, Department of Radiology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada. .,CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada.
| | - Robert Balshaw
- George and Fay Yee Center for Healthcare Innovation, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Connel Trevena
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shantanu Banerji
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada.,Section of Medical Oncology, Department of Internal Medicine, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Leigh Murphy
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada.,Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - David Dawe
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada.,Section of Medical Oncology, Department of Internal Medicine, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Lawrence Tan
- Section of Thoracic Surgery, Department of Surgery, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Sadeesh Srinathan
- Section of Thoracic Surgery, Department of Surgery, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Gordon Buduhan
- Section of Thoracic Surgery, Department of Surgery, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Biniam Kidane
- Section of Thoracic Surgery, Department of Surgery, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Gefei Qing
- Department of Human Pathology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Michael Domaratzki
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Michel Aliani
- Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
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19
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López-López Á, Ciborowski M, Niklinski J, Barbas C, López-Gonzálvez Á. Optimization of capillary electrophoresis coupled to negative mode electrospray ionization-mass spectrometry using polyvinyl alcohol coated capillaries. Application to a study on non-small cell lung cancer. Anal Chim Acta 2022; 1226:340259. [DOI: 10.1016/j.aca.2022.340259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 11/01/2022]
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20
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Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review. Metabolites 2022; 12:metabo12060545. [PMID: 35736478 PMCID: PMC9229104 DOI: 10.3390/metabo12060545] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/28/2022] [Accepted: 06/02/2022] [Indexed: 02/04/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3–62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (18F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.
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21
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Miller HA, Rai SN, Yin X, Zhang X, Chesney JA, van Berkel VH, Frieboes HB. Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival. Metabolomics 2022; 18:31. [PMID: 35567637 PMCID: PMC9724684 DOI: 10.1007/s11306-022-01891-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/19/2022] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive. OBJECTIVES This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS. METHODS Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events. RESULTS Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates). CONCLUSION Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
| | - Shesh N Rai
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
| | - Xinmin Yin
- Department of Chemistry, University of Louisville, Louisville, USA
| | - Xiang Zhang
- Department of Chemistry, University of Louisville, Louisville, USA
| | - Jason A Chesney
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
- Division of Medical Oncology and Hematology, Department of Medicine, University of Louisville, Louisville, USA
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, USA
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, USA.
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22
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Chen S, Gui R, Zhou XH, Zhang JH, Jiang HY, Liu HT, Fu YF. Combined Microbiome and Metabolome Analysis Reveals a Novel Interplay Between Intestinal Flora and Serum Metabolites in Lung Cancer. Front Cell Infect Microbiol 2022; 12:885093. [PMID: 35586253 PMCID: PMC9108287 DOI: 10.3389/fcimb.2022.885093] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/04/2022] [Indexed: 11/17/2022] Open
Abstract
As the leading cause of cancer death, lung cancer seriously endangers human health and quality of life. Although many studies have reported the intestinal microbial composition of lung cancer, little is known about the interplay between intestinal microbiome and metabolites and how they affect the development of lung cancer. Herein, we combined 16S ribosomal RNA (rRNA) gene sequencing and liquid chromatography-mass spectrometry (LC-MS) technology to analyze intestinal microbiota composition and serum metabolism profile in a cohort of 30 lung cancer patients with different stages and 15 healthy individuals. Compared with healthy people, we found that the structure of intestinal microbiota in lung cancer patients had changed significantly (Adonis, p = 0.021). In order to determine how intestinal flora affects the occurrence and development of lung cancer, the Spearman rank correlation test was used to find the connection between differential microorganisms and differential metabolites. It was found that as thez disease progressed, L-valine decreased. Correspondingly, the abundance of Lachnospiraceae_UCG-006, the genus with the strongest association with L-valine, also decreased in lung cancer groups. Correlation analysis showed that the gut microbiome and serum metabolic profile had a strong synergy, and Lachnospiraceae_UCG-006 was closely related to L-valine. In summary, this study described the characteristics of intestinal flora and serum metabolic profiles of lung cancer patients with different stages. It revealed that lung cancer may be the result of the mutual regulation of L-valine and Lachnospiraceae_UCG-006 through the aminoacyl-tRNA biosynthesis pathway, and proposed that L-valine may be a potential marker for the diagnosis of lung cancer.
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Affiliation(s)
- Sai Chen
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Rong Gui
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Xiong-hui Zhou
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Jun-hua Zhang
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Hai-ye Jiang
- Department of Laboratory Medicine, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Hai-ting Liu
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yun-feng Fu
- Department of Blood Transfusion, The Third Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Yun-feng Fu,
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23
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Singh A, Prakash V, Gupta N, Kumar A, Kant R, Kumar D. Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach. ACS OMEGA 2022; 7:5510-5520. [PMID: 35187366 PMCID: PMC8851899 DOI: 10.1021/acsomega.1c06941] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/18/2022] [Indexed: 06/01/2023]
Abstract
Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.
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Affiliation(s)
- Anjana Singh
- All
India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand 249201, India
- Pulmonary
& Critical Care Medicine, King George’s
Medical University, Lucknow, Uttar Pradesh 226003, India
| | - Ved Prakash
- Pulmonary
& Critical Care Medicine, King George’s
Medical University, Lucknow, Uttar Pradesh 226003, India
| | - Nikhil Gupta
- Centre
of Biomedical Research (CBMR), SGPGIMS, Lucknow, Uttar Pradesh 226014, India
- Department
of Chemistry, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
| | - Ashish Kumar
- Department
of Chemistry, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
| | - Ravi Kant
- All
India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand 249201, India
| | - Dinesh Kumar
- Centre
of Biomedical Research (CBMR), SGPGIMS, Lucknow, Uttar Pradesh 226014, India
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24
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1H-NMR-based metabolomics of skin squamous cell carcinoma and peri-tumoral region tissues. J Pharm Biomed Anal 2022; 212:114643. [DOI: 10.1016/j.jpba.2022.114643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/29/2022] [Accepted: 02/01/2022] [Indexed: 11/21/2022]
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25
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Choueiry F, Zhu J. Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time. Anal Chim Acta 2022; 1189:339230. [PMID: 34815037 PMCID: PMC8613447 DOI: 10.1016/j.aca.2021.339230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/05/2021] [Accepted: 10/28/2021] [Indexed: 01/04/2023]
Abstract
Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.
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Affiliation(s)
- Fouad Choueiry
- Department of Human Sciences, The Ohio State University; Columbus, OH 43210
| | - Jiangjiang Zhu
- Department of Human Sciences, The Ohio State University; Columbus, OH 43210, James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210
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26
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Cai C, Liu Y, Li J, Wang L, Zhang K. Serum fingerprinting by slippery liquid-infused porous SERS for non-invasive lung cancer detection. Analyst 2022; 147:4426-4432. [DOI: 10.1039/d2an01325h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Direct and label-free analysis of clinical serum samples using slippery liquid-infused porous-enhanced Raman spectroscopy (SLIPSERS) enables the rapid non-invasive identification of lung cancer.
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Affiliation(s)
- Chenlei Cai
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Yujie Liu
- Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Jiayu Li
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Lei Wang
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Kun Zhang
- Shanghai Institute for Pediatric Research, Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
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27
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Pedersen S, Hansen JB, Maltesen RG, Szejniuk WM, Andreassen T, Falkmer U, Kristensen SR. Identifying metabolic alterations in newly diagnosed small cell lung cancer patients. Metabol Open 2021; 12:100127. [PMID: 34585134 PMCID: PMC8455369 DOI: 10.1016/j.metop.2021.100127] [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: 08/25/2021] [Revised: 09/07/2021] [Accepted: 09/14/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Small cell lung cancer (SCLC) is a malignant disease with poor prognosis. At the time of diagnosis most patients are already in a metastatic stage. Current diagnosis is based on imaging, histopathology, and immunohistochemistry, but no blood-based biomarkers have yet proven to be clinically successful for diagnosis and screening. The precise mechanisms of SCLC are not fully understood, however, several genetic mutations, protein and metabolic aberrations have been described. We aim at identifying metabolite alterations related to SCLC and to expand our knowledge relating to this aggressive cancer. METHODS A total of 30 serum samples of patients with SCLC, collected at the time of diagnosis, and 25 samples of healthy controls were included in this study. The samples were analyzed with nuclear magnetic resonance spectroscopy. Multivariate, univariate and pathways analyses were performed. RESULTS Several metabolites were identified to be altered in the pre-treatment serum samples of small-cell lung cancer patients compared to healthy individuals. Metabolites involved in tricarboxylic acid cycle (succinate: fold change (FC) = 2.4, p = 0.068), lipid metabolism (LDL triglyceride: FC = 1.3, p = 0.001; LDL-1 triglyceride: FC = 1.3, p = 0.012; LDL-2 triglyceride: FC = 1.4, p = 0.009; LDL-6 triglyceride: FC = 1.5, p < 0.001; LDL-4 cholesterol: FC = 0.5, p = 0.007; HDL-3 free cholesterol: FC = 0.7, p = 0.002; HDL-4 cholesterol FC = 0.8, p < 0.001; HDL-4 apolipoprotein-A1: FC = 0.8, p = 0.005; HDL-4 apolipoprotein-A2: FC ≥ 0.7, p ≤ 0.001), amino acids (glutamic acid: FC = 1.7, p < 0.001; glutamine: FC = 0.9, p = 0.007, leucine: FC = 0.8, p < 0.001; isoleucine: FC = 0.8, p = 0.016; valine: FC = 0.9, p = 0.032; lysine: FC = 0.8, p = 0.004; methionine: FC = 0.8, p < 0.001; tyrosine: FC = 0.7, p = 0.002; creatine: FC = 0.9, p = 0.030), and ketone body metabolism (3-hydroxybutyric acid FC = 2.5, p < 0.001; acetone FC = 1.6, p < 0.001), among other, were found deranged in SCLC. CONCLUSIONS This study provides novel insight into the metabolic disturbances in pre-treatment SCLC patients, expanding our molecular understanding of this malignant disease.
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Affiliation(s)
- Shona Pedersen
- Department of Basic Medical Science, College of Medicine, Qatar University, QU Health, Doha, Qatar
| | | | - Raluca Georgiana Maltesen
- Translational Radiation Biology and Oncology Laboratory, Centre for Cancer Research, Westmead Institute of Medical Research, Westmead, 2145, Australia
| | - Weronika Maria Szejniuk
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark
| | - Trygve Andreassen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ursula Falkmer
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark
| | - Søren Risom Kristensen
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Department of Clinical Biochemistry, Aalborg University Hospital, Aalborg, Denmark
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28
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Cao P, Wu S, Guo W, Zhang Q, Gong W, Li Q, Zhang R, Dong X, Xu S, Liu Y, Shi S, Huang Y, Zhang Y. Precise pathological classification of non-small cell lung adenocarcinoma and squamous carcinoma based on an integrated platform of targeted metabolome and lipidome. Metabolomics 2021; 17:98. [PMID: 34729658 DOI: 10.1007/s11306-021-01849-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/12/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common subtypes of NSCLC. Despite genetic differences between LUAD and LUSC have been clarified in depth, the metabolic differences of these two subtypes are still unclear. METHODS Totally, 128 plasma samples of NSCLC patients were collected before initial treatments, followed by determination of LC-ESI-Q TRAP-MS/MS. Differentially expressed metabolites were screened based on a strict standard. RESULTS Based on the integrated platform of targeted metabolome and lipidome, a total of 1141 endogenous metabolites (including 809 lipids) were finally detected in the plasma of NSCLC patients, including 16 increased and 3 decreased endogenous compounds in LUAD group when compared with LUSC group. Thereafter, a logistic regression model integrating four differential metabolites [2-(Methylthio) ethanol, Cortisol, D-Glyceric Acid, and N-Acetylhistamine] was established and could accurately differentiate LUAD and LUSC with an area under the ROC curve of 0.946 (95% CI 0.886-1.000). The cut-off value showed a satisfactory efficacy with 92.0% sensitivity and 92.9% specificity. KEGG functional enrichment analysis showed these differentially expressed metabolites could be further enriched in riboflavin metabolism, steroid hormone biosynthesis, prostate cancer, etc. The endogenous metabolites identified in this study have the potential to be used as novel biomarkers to distinguish LUAD from LUSC. CONCLUSIONS Our research might provide more evidence for exploring the pathogenesis and differentiation of NSCLC. This research could promote a deeper understanding and precise treatment of lung cancer.
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Affiliation(s)
- Peng Cao
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Sanlan Wu
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Wei Guo
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Qilin Zhang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Weijing Gong
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Qiang Li
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Rui Zhang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Xiaorong Dong
- Cancer center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Shuangbing Xu
- Cancer center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yani Liu
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Shaojun Shi
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China
| | - Yifei Huang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China.
| | - Yu Zhang
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan, 430022, China.
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Yu M, Sun R, Zhao Y, Shao F, Zhu W, Aa J. Detection and verification of coexisting diagnostic markers in plasma and serum of patients with non-small-cell lung cancer. Future Oncol 2021; 17:4355-4369. [PMID: 34674559 DOI: 10.2217/fon-2021-0025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Aim: To screen and identify the potential biomarkers co-existing in plasma and serum of patients with non-small-cell lung cancer (NSCLC), and establish appropriate diagnostic models. Methods: A cohort of 195 plasma samples and 180 serum samples were obtained from healthy controls (HCs), adenocarcinoma (AdC) and squamous cell carcinoma (SqCC) patients enrolled from the First Affiliated Hospital of Nanjing Medical University. Metabolites in plasma and serum were analyzed by GC-MS. Results: Hypoxanthine was found to have good performance in the differential diagnosis of NSCLC (including AdC and SqCC) and HC (area under the receiver operating characteristic [AUROC] ≥0.85). Combinations of metabolites could be used for differential diagnosis of NSCLC and HC (AUROC >0.93), AdC and HC (AUROC >0.91), SqCC and HC (AUROC >0.95), AdC and SqCC (AUROC >0.72). Conclusions: Metabolomics based on GC-MS can screen and identify the differential metabolites coexisting in plasma and serum of patients with NSCLC, and prediction models established by this method can be used for the differential diagnosis of patients with NSCLC.
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Affiliation(s)
- Mengjie Yu
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu Province 210009, China
| | - Runbin Sun
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu Province 210009, China
| | - Yuqing Zhao
- Phase I Clinical Trial Unit, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Feng Shao
- Phase I Clinical Trial Unit, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Wei Zhu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Guangzhou Road, Nanjing, Jiangsu 210029, China
| | - Jiye Aa
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu Province 210009, China
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Ding J, Tu Z, Chen H, Liu Z. Identifying modifiable risk factors of lung cancer: Indications from Mendelian randomization. PLoS One 2021; 16:e0258498. [PMID: 34662362 PMCID: PMC8523078 DOI: 10.1371/journal.pone.0258498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/28/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Lung cancer is the major cause of mortality in tumor patients. While its incidence rate has recently declined, it is still far from satisfactory and its potential modifiable risk factors should be explored. METHODS We performed a two-sample Mendelian randomization (MR) study to investigate the causal relationship between potentially modifiable risk factors (namely smoking behavior, alcohol intake, anthropometric traits, blood pressure, lipidemic traits, glycemic traits, and fasting insulin) and lung cancer. Besides, a bi-directional MR analysis was carried out to disentangle the complex relationship between different risk factors. Inverse-variance weighted (IVW) was utilized to combine the estimation for each SNP. Cochrane's Q value was used to evaluate heterogeneity and two methods, including MR-Egger intercept and MR-PRESSO, were adopted to detect horizontal pleiotropy. RESULTS Three kinds of smoking behavior were all causally associated with lung cancer. Overall, smokers were more likely to suffer from lung cancer compared with non-smokers (OR = 2.58 [1.95, 3.40], p-value = 2.07 x 10-11), and quitting smoking could reduce the risk (OR = 4.29[2.60, 7.07], p-value = 1.23 x 10-8). Furthermore, we found a dose-response relationship between the number of cigarettes and lung cancer (OR = 6.10 [5.35, 6.96], p-value = 4.43x10-161). Lower HDL cholesterol could marginally increase the risk of lung cancer, but become insignificant after Bonferroni correction (OR = 0.82 [0.68, 1.00], p-value = 0.045). In addition, we noted no direct causal relationship between other risk factors and lung cancer. Neither heterogeneity nor pleiotropy was observed in this study. However, when treating the smoking behavior as the outcome, we found the increased BMI could elevate the number of cigarettes per day (beta = 0.139[0.104, 0.175], p-value = 1.99x10-14) and a similar effect was observed for the waist circumference and hip circumference. Additionally, the elevation of SBP could also marginally increase the number of cigarettes per day (beta = 0.001 [0.0002, 0.002], p-value = 0.018). CONCLUSION Smoking behavior might be the most direct and effective modifiable way to reduce the risk of lung cancer. Meanwhile, smoking behavior can be affected by other risk factors, especially obesity.
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Affiliation(s)
- Jie Ding
- Cancer Center, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Zhenxing Tu
- Department of Hand Surgery, The Second Hospital of Tangshan, Tangshan, Hebei Province, China
| | - Hongquan Chen
- Department of Bone Surgery, Affiliated Hospital of North China University of Science and Technology, Tangshan, Hebei Province, China
| | - Zhiguang Liu
- Department of Pulmonary and Critical Care Medicine, Affiliated Changzhou Second People’s Hospital affiliated to Nanjing Medical University, Changzhou, Jiangsu Province, China
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Madama D, Martins R, Pires AS, Botelho MF, Alves MG, Abrantes AM, Cordeiro CR. Metabolomic Profiling in Lung Cancer: A Systematic Review. Metabolites 2021; 11:630. [PMID: 34564447 PMCID: PMC8471464 DOI: 10.3390/metabo11090630] [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: 07/20/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer continues to be a significant burden worldwide and remains the leading cause of cancer-associated mortality. Two considerable challenges posed by this disease are the diagnosis of 61% of patients in advanced stages and the reduced five-year survival rate of around 4%. Noninvasively collected samples are gaining significant interest as new areas of knowledge are being sought and opened up. Metabolomics is one of these growing areas. In recent years, the use of metabolomics as a resource for the study of lung cancer has been growing. We conducted a systematic review of the literature from the past 10 years in order to identify some metabolites associated with lung cancer. More than 150 metabolites have been associated with lung cancer-altered metabolism. These were detected in different biological samples by different metabolomic analytical platforms. Some of the published results have been consistent, showing the presence/alteration of specific metabolites. However, there is a clear variability due to lack of a full clinical characterization of patients or standardized patients selection. In addition, few published studies have focused on the added value of the metabolomic profile as a means of predicting treatment response for lung cancer. This review reinforces the need for consistent and systematized studies, which will help make it possible to identify metabolic biomarkers and metabolic pathways responsible for the mechanisms that promote tumor progression, relapse and eventually resistance to therapy.
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Affiliation(s)
- Daniela Madama
- Clinical Academic Center of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal;
| | - Rosana Martins
- Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal;
| | - Ana S. Pires
- Clinical Academic Center of Coimbra (CACC), Center for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Maria F. Botelho
- Clinical Academic Center of Coimbra (CACC), Center for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Marco G. Alves
- Department of Anatomy, Unit for Multidisciplinary Research in Biomedicine (UMIB), Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, 4099-002 Porto, Portugal;
| | - Ana M. Abrantes
- Clinical Academic Center of Coimbra (CACC), Center for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Carlos R. Cordeiro
- Clinical Academic Center of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal;
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Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor. Metabolites 2021; 11:metabo11080537. [PMID: 34436478 PMCID: PMC8401204 DOI: 10.3390/metabo11080537] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 01/03/2023] Open
Abstract
Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).
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Mc Cormack BA, González-Cantó E, Agababyan C, Espinoza-Sánchez NA, Tomás-Pérez S, Llueca A, Marí-Alexandre J, Götte M, Gilabert-Estellés J. miRNAs in the Era of Personalized Medicine: From Biomarkers to Therapeutics. Int J Mol Sci 2021; 22:8154. [PMID: 34360918 PMCID: PMC8348078 DOI: 10.3390/ijms22158154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 07/07/2021] [Indexed: 11/17/2022] Open
Abstract
In recent years, interest in personalized medicine has considerably increased [...].
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Affiliation(s)
- Bárbara A. Mc Cormack
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain; (B.A.M.C.); (E.G.-C.); (C.A.); (S.T.-P.); (J.G.-E.)
| | - Eva González-Cantó
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain; (B.A.M.C.); (E.G.-C.); (C.A.); (S.T.-P.); (J.G.-E.)
| | - Cristina Agababyan
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain; (B.A.M.C.); (E.G.-C.); (C.A.); (S.T.-P.); (J.G.-E.)
- Obstetrics and Gynaecology Service, General University Hospital of Valencia Consortium, 46014 Valencia, Spain
| | - Nancy A. Espinoza-Sánchez
- Research Laboratory, Department of Gynecology and Obstetrics, Münster University Hospital, D-48149 Münster, Germany;
| | - Sarai Tomás-Pérez
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain; (B.A.M.C.); (E.G.-C.); (C.A.); (S.T.-P.); (J.G.-E.)
| | - Antoni Llueca
- Department of Medicine, University Jaume I, 12071 Castellón, Spain;
- Multidisciplinary Unit of Abdominal Pelvic Oncology Surgery (MUAPOS), General University Hospital of Castellón, 12004 Castellón, Spain
| | - Josep Marí-Alexandre
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain; (B.A.M.C.); (E.G.-C.); (C.A.); (S.T.-P.); (J.G.-E.)
| | - Martin Götte
- Research Laboratory, Department of Gynecology and Obstetrics, Münster University Hospital, D-48149 Münster, Germany;
| | - Juan Gilabert-Estellés
- Research Laboratory in Biomarkers in Reproduction, Gynaecology and Obstetrics, Research Foundation of the General University Hospital of Valencia, 46014 Valencia, Spain; (B.A.M.C.); (E.G.-C.); (C.A.); (S.T.-P.); (J.G.-E.)
- Obstetrics and Gynaecology Service, General University Hospital of Valencia Consortium, 46014 Valencia, Spain
- Department of Paediatrics, Obstetrics and Gynaecology, University of Valencia, 46010 Valencia, Spain
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The Lipid Composition of Serum-Derived Small Extracellular Vesicles in Participants of a Lung Cancer Screening Study. Cancers (Basel) 2021; 13:cancers13143414. [PMID: 34298629 PMCID: PMC8307680 DOI: 10.3390/cancers13143414] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/29/2021] [Accepted: 07/06/2021] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Molecular components of extracellular vesicles present in serum are potential biomarkers of lung cancer, however, none of them have been validated in the context of an actual early detection of lung cancer. Here, we compared the lipid profiles of vesicles obtained from participants in a lung cancer screening study, including patients with screening-detected cancer and individuals with benign pulmonary nodules or without pathological changes. A few lipids whose levels were different between compared groups were detected, including ceramide Cer(42:1) upregulated in vesicles from cancer patients. Furthermore, a high heterogeneity of lipid profiles of extracellular vesicles was observed, which impaired the performance of classification models based on specific compounds. Abstract Molecular components of exosomes and other classes of small extracellular vesicles (sEV) present in human biofluids are potential biomarkers with possible applicability in the early detection of lung cancer. Here, we compared the lipid profiles of serum-derived sEV from three groups of lung cancer screening participants: individuals without pulmonary alterations, individuals with benign lung nodules, and patients with screening-detected lung cancer (81 individuals in each group). Extracellular vesicles and particles were purified from serum by size-exclusion chromatography, and a fraction enriched in sEV and depleted of low-density lipoproteins (LDLs) was selected (similar sized vesicles was observed in all groups: 70–100 nm). The targeted mass-spectrometry-based approach enabled the detection of 352 lipids, including 201 compounds used in quantitative analyses. A few compounds, exemplified by Cer(42:1), i.e., a ceramide whose increased plasma/serum level was reported in different pathological conditions, were upregulated in vesicles from cancer patients. On the other hand, the contribution of phosphatidylcholines with poly-unsaturated acyl chains was reduced in vesicles from lung cancer patients. Cancer-related features detected in serum-derived sEV were different than those of the corresponding whole serum. A high heterogeneity of lipid profiles of sEV was observed, which markedly impaired the performance of classification models based on specific compounds (the three-state classifiers showed an average AUC = 0.65 and 0.58 in the training and test subsets, respectively).
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Kowalczyk T, Kisluk J, Pietrowska K, Godzien J, Kozlowski M, Reszeć J, Sierko E, Naumnik W, Mróz R, Moniuszko M, Kretowski A, Niklinski J, Ciborowski M. The Ability of Metabolomics to Discriminate Non-Small-Cell Lung Cancer Subtypes Depends on the Stage of the Disease and the Type of Material Studied. Cancers (Basel) 2021; 13:cancers13133314. [PMID: 34282765 PMCID: PMC8268630 DOI: 10.3390/cancers13133314] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 02/04/2023] Open
Abstract
Identification of the NSCLC subtype at an early stage is still quite sophisticated. Metabolomics analysis of tissue and plasma of NSCLC patients may indicate new, and yet unknown, metabolic pathways active in the NSCLC. Our research characterized the metabolomics profile of tissue and plasma of patients with early and advanced NSCLC stage. Samples were subjected to thorough metabolomics analyses using liquid chromatography-mass spectrometry (LC-MS) technique. Tissue and/or plasma samples from 137 NSCLC patients were analyzed. Based on the early stage tissue analysis, more than 200 metabolites differentiating adenocarcinoma (ADC) and squamous cell lung carcinoma (SCC) subtypes as well as normal tissue, were identified. Most of the identified metabolites were amino acids, fatty acids, carnitines, lysoglycerophospholipids, sphingomyelins, plasmalogens and glycerophospholipids. Moreover, metabolites related to N-acyl ethanolamine (NAE) biosynthesis, namely glycerophospho (N-acyl) ethanolamines (GP-NAE), which discriminated early-stage SCC from ADC, have also been identified. On the other hand, the analysis of plasma of chronic obstructive pulmonary disease (COPD) and NSCLC patients allowed exclusion of the metabolites related to the inflammatory state in lungs and the identification of compounds (lysoglycerophospholipids, glycerophospholipids and sphingomyelins) truly characteristic to cancer. Our results, among already known, showed novel, thus far not described, metabolites discriminating NSCLC subtypes, especially in the early stage of cancer. Moreover, the presented results also indicated the activity of new metabolic pathways in NSCLC. Further investigations on the role of NAE biosynthesis pathways in the early stage of NSCLC may reveal new prognostic and diagnostic targets.
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Affiliation(s)
- Tomasz Kowalczyk
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
| | - Joanna Kisluk
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland; (J.K.); (J.N.)
| | - Karolina Pietrowska
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
| | - Joanna Godzien
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
| | - Miroslaw Kozlowski
- Department of Thoracic Surgery, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland;
| | - Joanna Reszeć
- Department of Medical Patomorphology, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland;
| | - Ewa Sierko
- Department of Oncology, Medical University of Bialystok, Ogrodowa 12, 15-027 Bialystok, Poland;
| | - Wojciech Naumnik
- 1st Department of Lung Diseases and Tuberculosis, Medical University of Bialystok, Żurawia 14, 15-540 Bialystok, Poland;
| | - Robert Mróz
- 2nd Department of Lung Diseases and Tuberculosis, Medical University of Bialystok, Żurawia 14, 15-540 Bialystok, Poland;
| | - Marcin Moniuszko
- Department of Allergology and Internal Medicine, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland;
- Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland
| | - Adam Kretowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland
| | - Jacek Niklinski
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland; (J.K.); (J.N.)
| | - Michal Ciborowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
- Correspondence:
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Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin 2021; 71:333-358. [PMID: 33982817 PMCID: PMC8298088 DOI: 10.3322/caac.21670] [Citation(s) in RCA: 308] [Impact Index Per Article: 102.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/07/2021] [Accepted: 03/09/2021] [Indexed: 12/12/2022] Open
Abstract
Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.
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Affiliation(s)
- Daniel R. Schmidt
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Rutulkumar Patel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - David G. Kirsch
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC 27708 USA
| | - Caroline A. Lewis
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Matthew G. Vander Heiden
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jason W. Locasale
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC 27708 USA
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Qi SA, Wu Q, Chen Z, Zhang W, Zhou Y, Mao K, Li J, Li Y, Chen J, Huang Y, Huang Y. High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis. Sci Rep 2021; 11:11805. [PMID: 34083687 PMCID: PMC8175557 DOI: 10.1038/s41598-021-91276-2] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/19/2021] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.
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Affiliation(s)
- Shi-Ang Qi
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Qian Wu
- Shanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, Shanghai, 201203, China
- Shanghai Fenglin Clinical Laboratory Co., Ltd, Shanghai, 200231, China
| | - Zhenpu Chen
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Wei Zhang
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Yongchun Zhou
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Kaining Mao
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Jia Li
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Yuanyuan Li
- Shanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, Shanghai, 201203, China
| | - Jie Chen
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
| | - Youguang Huang
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China.
| | - Yunchao Huang
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China.
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Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts. Cancers (Basel) 2021; 13:cancers13112714. [PMID: 34072693 PMCID: PMC8198431 DOI: 10.3390/cancers13112714] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 12/27/2022] Open
Abstract
Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.
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Zheng Y, He Z, Kong Y, Huang X, Zhu W, Liu Z, Gong L. Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway. J Proteome Res 2021; 20:3444-3454. [PMID: 34056907 DOI: 10.1021/acs.jproteome.0c01019] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Lung cancer (LC) is one of the most malignant cancers in the world, but currently, it lacks effective noninvasive biomarkers to assist its early diagnosis. Our study aims to discover potential serum diagnostic biomarkers for LC. In our study, untargeted serum metabolomics of a discovery cohort and targeted analysis of a test cohort were performed based on gas chromatography-mass spectrometry. Both univariate and multivariate statistical analyses were employed to screen for differential metabolites between LC and healthy control (HC), followed by the selection of candidate biomarkers through multiple algorithms. The results showed that 15 metabolites were significantly dysregulated between LC and HC, and a panel, comprising cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid, was demonstrated to have excellent differentiating capability for LC based on multiple classification modelings. In addition, the molecular interaction analysis combined with transcriptomics revealed a close correlation between the candidate biomarkers and LC proliferation via a Ca2+ signaling pathway. Our study discovered that cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid in combination could be a promising diagnostic biomarker for LC, and most importantly, our results will shed some light on the pathophysiological mechanism underlying LC to understand it deeply. The data that support the findings of this study are openly available in MetaboLights at https://www.ebi.ac.uk/metabolights/, reference number MTBLS1517.
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Affiliation(s)
- Yuan Zheng
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Zhuoru He
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Yu Kong
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Plant Science Research Centre, Chinese Academy of Sciences, Shanghai Chenshan Botanical Garden, Shanghai 201602, PR China
| | - Xinjie Huang
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Wei Zhu
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Zhongqiu Liu
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Lingzhi Gong
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
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40
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Goldberg E, Ievari-Shariati S, Kidane B, Kim J, Banerji S, Qing G, Srinathan S, Murphy L, Aliani M. Comparative metabolomics studies of blood collected in streck and heparin tubes from lung cancer patients. PLoS One 2021; 16:e0249648. [PMID: 33891605 PMCID: PMC8064553 DOI: 10.1371/journal.pone.0249648] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 03/23/2021] [Indexed: 11/26/2022] Open
Abstract
Metabolomics analysis of blood from patients (n = 42) undergoing surgery for suspected lung cancer was performed in this study. Venous and arterial blood was collected in both Streck and Heparin tubes. A total of 96 metabolites were detected, affected by sex (n = 56), collection tube (n = 33), and blood location (n = 8). These metabolites belonged to a wide array of compound classes including lipids, acids, pharmaceutical agents, signalling molecules, vitamins, among others. Phospholipids and carboxylic acids accounted for 28% of all detected compounds. Out of the 33 compounds significantly affected by collection tube, 18 compounds were higher in the Streck tubes, including allantoin and ketoleucine, and 15 were higher in the Heparin tubes, including LysoPC(P-16:0), PS 40:6, and chenodeoxycholic acid glycine conjugate. Based on our results, it is recommended that replicate blood samples from each patient should be collected in different types of blood collection tubes for a broader range of the metabolome. Several metabolites were found at higher concentrations in cancer patients such as lactic acid in Squamous Cell Carcinoma, and lysoPCs in Adenocarcinoma and Acinar Cell Carcinoma, which may be used to detect early onset and/or to monitor the progress of the cancer patients.
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Affiliation(s)
- Erin Goldberg
- Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, MB, Canada
- The Canadian Centre for Agri-Food Research in Health and Medicine, (CCARM), St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada
| | - Shiva Ievari-Shariati
- The Canadian Centre for Agri-Food Research in Health and Medicine, (CCARM), St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada
- Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
| | - Biniam Kidane
- Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Julian Kim
- Department of Radiology, CancerCare Manitoba, Winnipeg, MB, Canada
| | - Shantanu Banerji
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB, Canada
| | - Gefei Qing
- Department of Pathology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Sadeesh Srinathan
- Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Leigh Murphy
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Department of Biochemistry & Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Michel Aliani
- Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, MB, Canada
- The Canadian Centre for Agri-Food Research in Health and Medicine, (CCARM), St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada
- * E-mail:
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41
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Gómez-Cebrián N, Rojas-Benedicto A, Albors-Vaquer A, Bellosillo B, Besses C, Martínez-López J, Pineda-Lucena A, Puchades-Carrasco L. Polycythemia Vera and Essential Thrombocythemia Patients Exhibit Unique Serum Metabolic Profiles Compared to Healthy Individuals and Secondary Thrombocytosis Patients. Cancers (Basel) 2021; 13:cancers13030482. [PMID: 33513807 PMCID: PMC7865636 DOI: 10.3390/cancers13030482] [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: 12/02/2020] [Revised: 01/15/2021] [Accepted: 01/22/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Current diagnosis of myeloproliferative neoplasms (MPNs), including polycythemia vera (PV) and essential thrombocythemia (ET), is controversial due to limitations associated with the lack of reproducibility, subjectivity and the presence of common somatic mutations in the driver genes. Metabolomics represents a powerful approach to identify altered metabolites that can differentiate between disease status at the time of diagnosis. The objective of this study was to characterize the serum metabolic profile of MPNs patients (PV and ET) and compare it with healthy controls (HC) and secondary thrombocytosis (ST) patients. The analysis revealed metabolites following similar trends between PV and ET patients, as well as unique significant differences in the serum metabolite levels of MNPs patients compared to HC and ST patients. These results could contribute to better differentiate patients with these diseases from HC and ST patients. Abstract Most common myeloproliferative neoplasms (MPNs) include polycythemia vera (PV) and essential thrombocythemia (ET). Accurate diagnosis of these disorders remains a clinical challenge due to the lack of specific clinical or molecular features in some patients enabling their discrimination. Metabolomics has been shown to be a powerful tool for the discrimination between different hematological diseases through the analysis of patients’ serum metabolic profiles. In this pilot study, the potential of NMR-based metabolomics to characterize the serum metabolic profile of MPNs patients (PV, ET), as well as its comparison with the metabolic profile of healthy controls (HC) and secondary thrombocytosis (ST) patients, was assessed. The metabolic profile of PV and ET patients, compared with HC, exhibited higher levels of lysine and decreased levels of acetoacetic acid, glutamate, polyunsaturated fatty acids (PUFAs), scyllo-inositol and 3-hydroxyisobutyrate. Furthermore, ET patients, compared with HC and ST patients, were characterized by decreased levels of formate, N-acetyl signals from glycoproteins (NAC) and phenylalanine, while the serum profile of PV patients, compared with HC, showed increased concentrations of lactate, isoleucine, creatine and glucose, as well as lower levels of choline-containing metabolites. The overall analysis revealed significant metabolic alterations mainly associated with energy metabolism, the TCA cycle, along with amino acid and lipid metabolism. These results underscore the potential of metabolomics for identifying metabolic alterations in the serum of MPNs patients that could contribute to improving the clinical management of these diseases.
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Affiliation(s)
- Nuria Gómez-Cebrián
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain; (N.G.-C.); (A.R.-B.); (A.A.-V.); (A.P.-L.)
| | - Ayelén Rojas-Benedicto
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain; (N.G.-C.); (A.R.-B.); (A.A.-V.); (A.P.-L.)
| | - Arturo Albors-Vaquer
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain; (N.G.-C.); (A.R.-B.); (A.A.-V.); (A.P.-L.)
| | - Beatriz Bellosillo
- Department of Pathology, Hospital Del Mar Medical Research Institute, 08003 Barcelona, Spain;
| | - Carlos Besses
- Department of Hematology, Hospital Del Mar Medical Research Institute, 08003 Barcelona, Spain;
| | - Joaquín Martínez-López
- Department of Hematology, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid, 28040 Madrid, Spain;
| | - Antonio Pineda-Lucena
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain; (N.G.-C.); (A.R.-B.); (A.A.-V.); (A.P.-L.)
- Molecular Therapeutics Program, Centro de Investigación Médica Aplicada, Universidad de Navarra, 28040 Pamplona, Spain
| | - Leonor Puchades-Carrasco
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain; (N.G.-C.); (A.R.-B.); (A.A.-V.); (A.P.-L.)
- Correspondence: ; Tel.: +34-96-124-6713
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42
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Boyacıoğlu Ö, Bilgiç E, Varan C, Bilensoy E, Nemutlu E, Sevim D, Kocaefe Ç, Korkusuz P. ACPA decreases non-small cell lung cancer line growth through Akt/PI3K and JNK pathways in vitro. Cell Death Dis 2021; 12:56. [PMID: 33431819 PMCID: PMC7801394 DOI: 10.1038/s41419-020-03274-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 01/29/2023]
Abstract
Therapeutic agents used for non-small cell lung cancer (NSCLC) have limited curative efficacy and may trigger serious adverse effects. Cannabinoid ligands exert antiproliferative effect and induce apoptosis on numerous epithelial cancers. We confirmed that CB1 receptor (CB1R) is expressed in NSCLC cells in this study. Arachidonoylcyclopropylamide (ACPA) as a synthetic, CB1R-specific ligand decreased proliferation rate in NSCLC cells by WST-1 analysis and real-time proliferation assay (RTCA). The half-maximal inhibitory concentration (IC50) dose of ACPA was calculated as 1.39 × 10-12 M. CB1 antagonist AM281 inhibited the antiproliferative effect of ACPA. Flow cytometry and ultrastructural analyzes revealed significant early and late apoptosis with diminished cell viability. Nano-immunoassay and metabolomics data on activation status of CB1R-mediated pro-apoptotic pathways found that ACPA inhibited Akt/PI3K pathway, glycolysis, TCA cycle, amino acid biosynthesis, and urea cycle and activated JNK pathway. ACPA lost its chemical stability after 24 hours tested by liquid chromatography-mass spectrometry (LC-MS/MS) assay. A novel ACPA-PCL nanoparticle system was developed by nanoprecipitation method and characterized. Sustained release of ACPA-PCL nanoparticles also reduced proliferation of NSCLC cells. Our results demonstrated that low dose ACPA and ACPA-PCL nanoparticle system harbor opportunities to be developed as a novel therapy in NSCLC patients that require further in vivo studies beforehand to validate its anticancer effect.
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Affiliation(s)
- Özge Boyacıoğlu
- Hacettepe University, Graduate School of Science and Engineering, Department of Bioengineering, 06800, Beytepe, Ankara, Turkey
- Atılım University, Faculty of Medicine, Department of Medical Biochemistry, 06830, Gölbaşı, Ankara, Turkey
| | - Elif Bilgiç
- Hacettepe University, Faculty of Medicine, Department of Histology and Embryology, 06100, Sıhhiye, Ankara, Turkey
| | - Cem Varan
- Hacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Technology, 06100, Sıhhiye, Ankara, Turkey
| | - Erem Bilensoy
- Hacettepe University, Faculty of Pharmacy, Department of Pharmaceutical Technology, 06100, Sıhhiye, Ankara, Turkey
| | - Emirhan Nemutlu
- Hacettepe University, Faculty of Pharmacy, Department of Analytical Chemistry, 06100, Sıhhiye, Ankara, Turkey
| | - Duygu Sevim
- Hacettepe University, Faculty of Medicine, Department of Medical Biology, 06100, Sıhhiye, Ankara, Turkey
| | - Çetin Kocaefe
- Hacettepe University, Faculty of Medicine, Department of Medical Biology, 06100, Sıhhiye, Ankara, Turkey
| | - Petek Korkusuz
- Hacettepe University, Faculty of Medicine, Department of Histology and Embryology, 06100, Sıhhiye, Ankara, Turkey.
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43
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Gómez-Cebrián N, García-Flores M, Rubio-Briones J, López-Guerrero JA, Pineda-Lucena A, Puchades-Carrasco L. Targeted Metabolomics Analyses Reveal Specific Metabolic Alterations in High-Grade Prostate Cancer Patients. J Proteome Res 2020; 19:4082-4092. [PMID: 32924497 DOI: 10.1021/acs.jproteome.0c00493] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Prostate cancer (PCa) is a hormone-dependent tumor characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the nuclear magnetic resonance-based metabolomic profile of PCa patients with different tumor grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason score (GS) into low-GS (GS < 7) and high-GS PCa (GS ≥ 7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.
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Affiliation(s)
- Nuria Gómez-Cebrián
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, Valencia 46026, Spain.,Laboratory of Molecular Biology, Fundación Instituto Valenciano de Oncología (FIVO), Valencia 46009, Spain
| | - María García-Flores
- Laboratory of Molecular Biology, Fundación Instituto Valenciano de Oncología (FIVO), Valencia 46009, Spain.,IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Centre (CIPF), Valencia 46012, Spain
| | - José Rubio-Briones
- Department of Urology, Fundación Instituto Valenciano de Oncología (FIVO), Valencia 46009, Spain
| | - José Antonio López-Guerrero
- Laboratory of Molecular Biology, Fundación Instituto Valenciano de Oncología (FIVO), Valencia 46009, Spain.,IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Centre (CIPF), Valencia 46012, Spain.,Department of Basic Medical Sciences, School of Medicine, Catholic University of Valencia 'San Vicente Martir', Valencia 46001, Spain
| | - Antonio Pineda-Lucena
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, Valencia 46026, Spain.,Molecular Therapeutics Program, Centro de Investigación Médica Aplicada, Navarra 31008, Spain
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44
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Altered lung tissue lipidomic profile in caspase-4 positive non-small cell lung cancer (NSCLC) patients. Oncotarget 2020; 11:3515-3525. [PMID: 33014287 PMCID: PMC7517963 DOI: 10.18632/oncotarget.27724] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 08/11/2020] [Indexed: 02/04/2023] Open
Abstract
Lung cancer is by far the leading cause of cancer death. Metabolomic studies have highlighted that both tumor progression and limited curative treatment options are partly due to dysregulated glucose metabolism and its associated signaling pathways. In our previous studies, we identified caspase-4 as a novel diagnostic tool for non-small cell lung cancer (NSCLC). Here, we analyzed the metabolomic profile of both plasma and tumor tissues of NSCLC patients stratified as caspase-4 positive or negative. We found that circulating caspase-4 was correlated to LDH. However, this effect was not observed in caspase-4 positive tumor tissues, where instead, fatty acid biosynthesis was favoured in that the malonic acid and the palmitic acid were higher than in non-cancerous and caspase-4 negative tissues. The glycolytic pathway in caspase-4 positive NSCLC tissues was bypassed by the malonic acid-dependent lipogenesis. On the other hand, the dysregulated glucose metabolism was regulated by a higher presence of succinate dehydrogenase (SDHA) and by the gluconeogenic valine which favoured Krebs’ cycle. In conclusion, we found that the recently identified caspase-4 positive subpopulation of NSCLC patients is characterized by a lipidomic profile accompanied by alternative pathways to guarantee glucose metabolism in favour of tumor cell proliferation.
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45
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Metabolites as Prognostic Markers for Metastatic Non-Small Cell Lung Cancer (NSCLC) Patients Treated with First-Line Platinum-Doublet Chemotherapy. Cancers (Basel) 2020; 12:cancers12071926. [PMID: 32708705 PMCID: PMC7409233 DOI: 10.3390/cancers12071926] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/01/2020] [Accepted: 07/10/2020] [Indexed: 11/16/2022] Open
Abstract
The metabolic requirements of metastatic non-small cell lung (mNSCLC) tumors from patients receiving first-line platinum-doublet chemotherapy are hypothesized to imprint a blood signature suitable for survival prediction. Pre-treatment samples prospectively collected at baseline from a randomized phase III trial were assayed using nuclear magnetic resonance (NMR) spectroscopy (n = 341) and ultra-high performance liquid chromatography – mass spectrometry (UPLC-MS) (n = 297). Distributions of time to event outcomes were estimated by Kaplan-Meier analysis, and baseline characteristics adjusted Cox regression modeling was used to correlate markers’ levels to time to event outcomes. Sixteen polar metabolites were significantly correlated with overall survival (OS) by univariate analysis (p < 0.025). Formate, 2-hydroxybutyrate, glycine and myo-inositol were selected for a multivariate model. The median OS was 6.6 months in the high-risk group compared to 11.4 months in the low risk group HR (Hazard Ratio) = 1.99, 95% C.I. (Confidence Interval) 1.45–2.68; p < 0.0001). Modeling of lipids by class (sphingolipids, acylcarnitines and lysophosphatidylcholines) revealed a median OS = 5.7 months vs. 11. 9 months for the high vs. low risk group. (HR: 2.23, 95% C.I. 1.55–3.20; p < 0.0001). These results demonstrate that metabolic profiles from pre-treatment samples may be useful to stratify clinical outcomes for mNSCLC patients receiving chemotherapy. Genomic and longitudinal measurements pre- and post-treatment may yield addition information to personalize treatment decisions further.
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46
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Callejón-Leblic B, Arias-Borrego A, Rodríguez-Moro G, Navarro Roldán F, Pereira-Vega A, Gómez-Ariza JL, García-Barrera T. Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins. Adv Clin Chem 2020; 100:91-137. [PMID: 33453868 DOI: 10.1016/bs.acc.2020.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.
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Affiliation(s)
- Belén Callejón-Leblic
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Ana Arias-Borrego
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Gema Rodríguez-Moro
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Francisco Navarro Roldán
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Integrated Sciences-Cell Biology, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | | | - José Luis Gómez-Ariza
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Tamara García-Barrera
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain.
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47
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Noreldeen HAA, Liu X, Xu G. Metabolomics of lung cancer: Analytical platforms and their applications. J Sep Sci 2019; 43:120-133. [DOI: 10.1002/jssc.201900736] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/05/2019] [Accepted: 11/15/2019] [Indexed: 12/24/2022]
Affiliation(s)
- Hamada A. A. Noreldeen
- CAS Key Laboratory of Separation Science for Analytical ChemistryDalian Institute of Chemical PhysicsChinese Academy of Sciences Dalian P. R. China
- University of Chinese Academy of Sciences Beijing P. R. China
- Marine Chemistry LabMarine Environment DivisionNational Institute of Oceanography and Fisheries Hurghada Egypt
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical ChemistryDalian Institute of Chemical PhysicsChinese Academy of Sciences Dalian P. R. China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical ChemistryDalian Institute of Chemical PhysicsChinese Academy of Sciences Dalian P. R. China
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48
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Song Z, Wang H, Yin X, Deng P, Jiang W. Application of NMR metabolomics to search for human disease biomarkers in blood. Clin Chem Lab Med 2019; 57:417-441. [PMID: 30169327 DOI: 10.1515/cclm-2018-0380] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Accepted: 07/16/2018] [Indexed: 02/05/2023]
Abstract
Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.
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Affiliation(s)
- Zikuan Song
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Haoyu Wang
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Xiaotong Yin
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Pengchi Deng
- Analytical and Testing Center, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wei Jiang
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China
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Correlations between the metabolic profile and 18F-FDG-Positron Emission Tomography-Computed Tomography parameters reveal the complexity of the metabolic reprogramming within lung cancer patients. Sci Rep 2019; 9:16212. [PMID: 31700108 PMCID: PMC6838313 DOI: 10.1038/s41598-019-52667-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 10/12/2019] [Indexed: 12/15/2022] Open
Abstract
Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by 18F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between 1H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (18F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUVmax), total body metabolic active tumor volumes (MATVWTB) and total body total lesion glycolysis (TLGWTB) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATVWTB and TLGWTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.
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Arroyo‐Crespo JJ, Armiñán A, Charbonnier D, Deladriere C, Palomino‐Schätzlein M, Lamas‐Domingo R, Forteza J, Pineda‐Lucena A, Vicent MJ. Characterization of triple-negative breast cancer preclinical models provides functional evidence of metastatic progression. Int J Cancer 2019; 145:2267-2281. [PMID: 30860605 PMCID: PMC6767480 DOI: 10.1002/ijc.32270] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 01/25/2019] [Accepted: 02/28/2019] [Indexed: 12/13/2022]
Abstract
Triple-negative breast cancer (TNBC), an aggressive, metastatic and recurrent breast cancer (BC) subtype, currently suffers from a lack of adequately described spontaneously metastatic preclinical models that faithfully reproduce the clinical scenario. We describe two preclinical spontaneously metastatic TNBC orthotopic murine models for the development of advanced therapeutics: an immunodeficient human MDA-MB-231-Luc model and an immunocompetent mouse 4T1 model. Furthermore, we provide a broad range of multifactorial analysis for both models that could provide relevant information for the development of new therapies and diagnostic tools. Our comparisons uncovered differential growth rates, stromal arrangements and metabolic profiles in primary tumors, and the presence of cancer-associated adipocyte infiltration in the MDA-MB-231-Luc model. Histopathological studies highlighted the more rapid metastatic spread to the lungs in the 4T1 model following a lymphatic route, while we observed both homogeneous (MDA-MB-231-Luc) and heterogeneous (4T1) metastatic spread to axillary lymph nodes. We encountered unique metabolomic signatures in each model, including crucial amino acids and cell membrane components. Hematological analysis demonstrated severe leukemoid and lymphoid reactions in the 4T1 model with the partial reestablishment of immune responses in the immunocompromised MDA-MB-231-Luc model. Additionally, we discovered β-immunoglobulinemia and increased basal levels of G-CSF correlating with a metastatic switch, with G-CSF also promoting extramedullary hematopoiesis (both models) and causing hepatosplenomegaly (4T1 model). Overall, we believe that the characterization of these preclinical models will foster the development of advanced therapeutic strategies for TNBC treatment, especially for the treatment of patients presenting both, primary tumors and metastatic spread.
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Affiliation(s)
- Juan J. Arroyo‐Crespo
- Polymer Therapeutics LaboratoryCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
| | - Ana Armiñán
- Polymer Therapeutics LaboratoryCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
| | - David Charbonnier
- Polymer Therapeutics LaboratoryCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
- Screening Platform, Centro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
| | - Coralie Deladriere
- Polymer Therapeutics LaboratoryCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
| | - Martina Palomino‐Schätzlein
- Joint Research Unit in Clinical MetabolomicsCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
| | - Rubén Lamas‐Domingo
- Joint Research Unit in Clinical MetabolomicsCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
| | - Jerónimo Forteza
- Unidad Mixta Centro de Investigación Príncipe Felipe‐Instituto Valenciano de PatologíaCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
| | - Antonio Pineda‐Lucena
- Joint Research Unit in Clinical MetabolomicsCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
- Drug Discovery UnitInstituto de Investigación Sanitaria La FeAvda. Fernando Abril Martorell, 106, 46026ValenciaSpain
| | - María J. Vicent
- Polymer Therapeutics LaboratoryCentro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
- Screening Platform, Centro de Investigación Príncipe FelipeAv. Eduardo Primo Yúfera 3Valencia, 46012Spain
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