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López-López Á, López-Gonzálvez Á, Barbas C. Metabolomics for searching validated biomarkers in cancer studies: a decade in review. Expert Rev Mol Diagn 2024; 24:601-626. [PMID: 38904089 DOI: 10.1080/14737159.2024.2368603] [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: 12/27/2023] [Accepted: 06/12/2024] [Indexed: 06/22/2024]
Abstract
INTRODUCTION In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways. AREAS COVERED This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types. EXPERT OPINION Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.
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Affiliation(s)
- Ángeles López-López
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Madrid, Spain
| | - Ángeles López-Gonzálvez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Madrid, Spain
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Jia K, Wang Y, Jiang L, Lai M, Liu W, Wang L, Liu H, Cao X, Li Y, Nie Z. Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO 3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2024; 16:12302-12309. [PMID: 38414269 DOI: 10.1021/acsami.3c19007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.
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Affiliation(s)
- Ke Jia
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- School of Chemistry and Chemical Engineering, Jiangxi Province Engineering Research Center of Ecological Chemical Industry, Jiujiang University, Jiujiang, Jiangxi 332005, China
| | - Lixia Jiang
- Gannan Medical University, Ganzhou 341000, China
| | - Mi Lai
- Gannan Medical University, Ganzhou 341000, China
| | - Wenlan Liu
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Liping Wang
- Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China
| | - Huihui Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohua Cao
- School of Chemistry and Chemical Engineering, Jiangxi Province Engineering Research Center of Ecological Chemical Industry, Jiujiang University, Jiujiang, Jiangxi 332005, China
| | - Yuze Li
- State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering, College of Chemistry and Chemical Engineering, Ningxia University, Yinchuan 750021, China
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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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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Tang E, Liu S, Zhang Z, Zhang R, Huang D, Gao T, Zhang T, Xu G. Therapeutic Potential of Glutamine Pathway in Lung Cancer. Front Oncol 2022; 11:835141. [PMID: 35223460 PMCID: PMC8873175 DOI: 10.3389/fonc.2021.835141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/31/2021] [Indexed: 12/31/2022] Open
Abstract
Cancer cells tend to obtain the substances needed for their development depending on altering metabolic characteristics. Among the reorganized metabolic pathways, Glutamine pathway, reprogrammed to be involved in the physiological process including energy supply, biosynthesis and redox homeostasis, occupies an irreplaceable role in tumor cells and has become a hot topic in recent years. Lung cancer currently maintains a high morbidity and mortality rate among all types of tumors and has been a health challenge that researchers have longed to overcome. Therefore, this study aimed to clarify the essential role of glutamine pathway played in the metabolism of lung cancer and its potential therapeutic value in the interventions of lung cancer.
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Haince JF, Joubert P, Bach H, Ahmed Bux R, Tappia PS, Ramjiawan B. Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome. Int J Mol Sci 2022; 23:ijms23031215. [PMID: 35163138 PMCID: PMC8835988 DOI: 10.3390/ijms23031215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 12/19/2022] Open
Abstract
The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.
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Affiliation(s)
| | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Department of Pathology, Laval University, Quebec, QC G1V 4G5, Canada;
| | - Horacio Bach
- Department of Medicine, Division of Infectious Diseases, University of British Columbia, Vancouver, BC V6H 3Z6, Canada;
| | - Rashid Ahmed Bux
- BioMark Diagnostics Inc., Richmond, BC V6X 2W8, Canada; (J.-F.H.); (R.A.B.)
| | - Paramjit S. Tappia
- Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada;
- Correspondence: ; Tel.: +1-204-258-1230
| | - Bram Ramjiawan
- Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada;
- Department of Pharmacology & Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
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Taunk K, Porto-Figueira P, Pereira JAM, Taware R, da Costa NL, Barbosa R, Rapole S, Câmara JS. Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer. Metabolites 2022; 12:36. [PMID: 35050157 PMCID: PMC8780352 DOI: 10.3390/metabo12010036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 12/24/2022] Open
Abstract
The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.
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Affiliation(s)
- Khushman Taunk
- Proteomics Lab, National Centre for Cell Science (NCCS), Ganeshkhind, SPPU Campus, Pune 411007, India; (K.T.); (R.T.)
| | - Priscilla Porto-Figueira
- CQM—Centro de Química da Madeira, Centro de Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105 Funchal, Portugal; (P.P.-F.); (J.A.M.P.)
| | - Jorge A. M. Pereira
- CQM—Centro de Química da Madeira, Centro de Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105 Funchal, Portugal; (P.P.-F.); (J.A.M.P.)
| | - Ravindra Taware
- Proteomics Lab, National Centre for Cell Science (NCCS), Ganeshkhind, SPPU Campus, Pune 411007, India; (K.T.); (R.T.)
| | - Nattane Luíza da Costa
- Instituto de Informática, Alameda Palmeiras, Quadra D, Campus Samambaia, Universidade Federal de Goiás, Goiânia 74690-900, GO, Brazil; (N.L.d.C.); (R.B.)
| | - Rommel Barbosa
- Instituto de Informática, Alameda Palmeiras, Quadra D, Campus Samambaia, Universidade Federal de Goiás, Goiânia 74690-900, GO, Brazil; (N.L.d.C.); (R.B.)
| | - Srikanth Rapole
- Proteomics Lab, National Centre for Cell Science (NCCS), Ganeshkhind, SPPU Campus, Pune 411007, India; (K.T.); (R.T.)
| | - José S. Câmara
- CQM—Centro de Química da Madeira, Centro de Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105 Funchal, Portugal; (P.P.-F.); (J.A.M.P.)
- Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, Campus Universitário da Penteada, 9020-105 Funchal, Portugal
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Yu X, Wang Z, Chen Y, Yin G, Liu J, Chen W, Zhu L, Xu W, Li X. The Predictive Role of Immune Related Subgroup Classification in Immune Checkpoint Blockade Therapy for Lung Adenocarcinoma. Front Genet 2021; 12:771830. [PMID: 34721552 PMCID: PMC8554034 DOI: 10.3389/fgene.2021.771830] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 09/30/2021] [Indexed: 01/17/2023] Open
Abstract
Background: In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete. Methods: Transcriptomics analysis was performed to evaluate the association between immune landscape and ICB therapy in lung adenocarcinoma and the associated underlying mechanism. First, the least absolute shrinkage and selection operator (LASSO) algorithm and K-means algorithm were used to identify immune related subgroups for LUAD cohort from the Cancer Genome Atlas (TCGA) database (n = 572). Second, the immune associated signatures of the identified subgroups were characterized by evaluating the status of immune checkpoint associated genes and the immune cell infiltration. Then, potential responses to ICB therapy based on the aforementioned immune related subgroup classification were evaluated via tumor immune dysfunction and exclusion (TIDE) algorithm analysis, and survival analysis and further Cox proportional hazards regression analysis were also performed for LUAD. In the end, gene set enrichment analysis (GSEA) was performed to explore the metabolic mechanism potentially responsible for immune related subgroup clustering. Additionally, two LUAD cohorts from the Gene Expression Omnibus (GEO) database were used as validation cohort. Results: A total of three immune related subgroups with different immune-associated signatures were identified for LUAD. Among them, subgroup 1 with higher infiltration scores for effector immune cells and immune checkpoint associated genes exhibited a potential response to IBC therapy and a better survival, whereas subgroup 3 with lower scores for immune checkpoint associated genes but higher infiltration scores for suppressive immune cells tended to be insensitive to ICB therapy and have an unfavorable prognosis. GSEA revealed that the status of glucometabolic reprogramming in LUAD was potentially responsible for the immune-related subgroup classification. Conclusion: In summary, immune related subgroup clustering based on distinct immune associated signatures will enable us to screen potentially responsive LUAD patients for ICB therapy before treatment, and the discovery of metabolism associated mechanism is beneficial to comprehensive therapeutic strategies making involving ICB therapy in combination with metabolism intervention for LUAD.
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Affiliation(s)
- Xiaozhou Yu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Yiwen Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Guotao Yin
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jianjing Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Wei Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Lei Zhu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Yuan Y, Wu Q, Zhao J, Feng Z, Dong J, An M, Wu G, Qin F, Zhao L. Investigation of pathogenesis and therapeutic targets of acute myeloid leukemia based on untargeted plasma metabolomics and network pharmacology approach. J Pharm Biomed Anal 2020; 195:113824. [PMID: 33358300 DOI: 10.1016/j.jpba.2020.113824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 11/23/2020] [Accepted: 12/01/2020] [Indexed: 01/19/2023]
Abstract
Acute myeloid leukemia (AML) is a malignant disease originating from bone marrow hematopoietic stem cells, characterized by anemia, hemorrhage, fever, and infection, with low survival rate. However, the pathogenesis of AML is not fully understood at present. In this work, an integrated approach based untargeted metabolomics and network pharmacology was adopted to elucidate the pathogenesis of AML. Metabolic profiling of plasma samples from 14 patients and 16 healthy individuals were performed based on UHPLC-MS platform. As a result, 23 metabolites were identified by using the human metabolite database based on PLS-DA (partial least squares discriminant analysis) and independent sample test. And metabolic pathways related to AML mainly included fatty acid metabolism, amino acid metabolism, energy metabolism and lipid metabolism. Meanwhile, biomarkers-targets-pathways-disease network was constructed, 75 biomarker targets and 122 disease targets were identified. Furthermore, 30 pathways were predicted, some of which were consistent with these in metabolomics. This is the first time that metabolomics and network pharmacology approach have been combined to investigate the pathogenesis and therapeutic targets of AML. ALDH, CYP2E1 and CYP3A4 were potential therapeutic targets for AML, which provide available way to elucidate the pathogenesis and treatment of AML.
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Affiliation(s)
- Yunxia Yuan
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning Province, PR China
| | - Qiong Wu
- Department of Pediatrics, Shengjing Hospital of China Medical University, PR China
| | - Jing Zhao
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning Province, PR China
| | - Zhiao Feng
- School of Pharmacy, Baotou Medical College, Baotou, Inner Mongolia, PR China
| | - Jiani Dong
- School of Pharmacy, Baotou Medical College, Baotou, Inner Mongolia, PR China
| | - Ming An
- School of Pharmacy, Baotou Medical College, Baotou, Inner Mongolia, PR China
| | - Guodong Wu
- School of Pharmacy, Baotou Medical College, Baotou, Inner Mongolia, PR China
| | - Feng Qin
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning Province, PR China.
| | - Longshan Zhao
- School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning Province, PR China.
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Higashiyama M, Miyazaki R, Yamamoto H, Anayama T, Kikuchi S, Hirohashi K, Okami J, Maniwa T, Kimura T, Orihashi K, Imamura F. Preoperative AminoIndex Cancer Screening (AICS) abnormalities predict postoperative recurrence in patients undergoing curative resection for non-small cell lung cancer. BMC Cancer 2020; 20:1100. [PMID: 33183251 PMCID: PMC7659101 DOI: 10.1186/s12885-020-07575-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/26/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND AminoIndex™ Cancer Screening (AICS (lung)) was developed as a screening test for lung cancer using a multivariate analysis of plasma-free amino acid (PFAA) profiles. According to the developed index composed of PFAA, the probability of lung cancer was categorized into AICS (lung) ranks A, B, and C in order of increasing risk. The aim of the present study was to investigate the relationship between the preoperative AICS (lung) rank and surgical outcomes in patients who underwent curative resection for non-small cell lung cancer (NSCLC). METHODS Preoperative blood samples were collected from 297 patients who underwent curative resection for NSCLC between 2006 and 2015. PFAA concentrations were measured. The relationship between the preoperative AICS (lung) rank and clinicopathological factors was examined. The effects of the preoperative AICS (lung) rank on postoperative outcomes were also analyzed. RESULTS The AICS (lung) rank was A in 93 patients (31.3%), B in 82 (27.6%), and C in 122 (41.1%). The AICS (lung) rank did not correlate with any clinicopathological factors, except for age. Based on follow-up data (median follow-up period of 6 years), postoperative recurrence was observed in 22 rank A patients (23.7%), 15 rank B (18.3%) and 49 rank C (40.2%). In the univariate analysis, preoperative AICS (lung) rank C was a worse factor of recurrence-free survival (p = 0.0002). The multivariate analysis identified preoperative AICS (lung) rank C (HR: 2.17, p = 0.0005) as a significant predictor of postoperative recurrence, particularly in patients with early-stage disease or adenocarcinoma. CONCLUSION Preoperative AICS (lung) rank C is a high-risk predictor of postoperative recurrence in patients undergoing curative resection for NSCLC.
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Affiliation(s)
- Masahiko Higashiyama
- Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan. .,Department of Thoracic Surgery, Higashiosaka City Medical Center, Higashiosaka, Osaka, Japan.
| | - Ryohei Miyazaki
- Division of Thoracic Surgery, Department of Surgery II, Kochi University School of Medicine, Kochi, Japan
| | - Hiroshi Yamamoto
- Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc., Yokohama, Kanagawa, Japan
| | - Takashi Anayama
- Division of Thoracic Surgery, Department of Surgery II, Kochi University School of Medicine, Kochi, Japan
| | - Shinya Kikuchi
- Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc., Yokohama, Kanagawa, Japan
| | - Kentaro Hirohashi
- Division of Thoracic Surgery, Department of Surgery II, Kochi University School of Medicine, Kochi, Japan
| | - Jiro Okami
- Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan
| | - Tomohiro Maniwa
- Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan
| | - Toru Kimura
- Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan
| | - Kazumasa Orihashi
- Division of Thoracic Surgery, Department of Surgery II, Kochi University School of Medicine, Kochi, Japan
| | - Fumio Imamura
- Department of Medical Oncology, Osaka International Cancer Institute, Osaka, Japan
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