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Zhang Y, Zhan L, Zhang L, Shi Q, Li L. Branched-Chain Amino Acids in Liver Diseases: Complexity and Controversy. Nutrients 2024; 16:1875. [PMID: 38931228 PMCID: PMC11206364 DOI: 10.3390/nu16121875] [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: 05/09/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Branched-chain amino acids (BCAAs), as essential amino acids, engage in various physiological processes, such as protein synthesis, energy supply, and cellular signaling. The liver is a crucial site for BCAA metabolism, linking the changes in BCAA homeostasis with the pathogenesis of a variety of liver diseases and their complications. Peripheral circulating BCAA levels show complex trends in different liver diseases. This review delineates the alterations of BCAAs in conditions including non-alcoholic fatty liver disease, hepatocellular carcinoma, cirrhosis, hepatic encephalopathy, hepatitis C virus infection, and acute liver failure, as well as the potential mechanisms underlying these changes. A significant amount of clinical research has utilized BCAA supplements in the treatment of patients with cirrhosis and liver cancer. However, the efficacy of BCAA supplementation in clinical practice remains uncertain and controversial due to the heterogeneity of studies. This review delves into the complicated relationship between BCAAs and liver diseases and tries to untangle what role BCAAs play in the occurrence, development, and outcomes of liver diseases.
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Affiliation(s)
- Yaqi Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou 310003, China
| | - Luqi Zhan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou 310003, China
| | - Lingjian Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou 310003, China
- Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou 310024, China
| | - Qingmiao Shi
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou 310003, China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou 310003, China
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2
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Wang Q, Liu J, Chen Z, Zheng J, Wang Y, Dong J. Targeting metabolic reprogramming in hepatocellular carcinoma to overcome therapeutic resistance: A comprehensive review. Biomed Pharmacother 2024; 170:116021. [PMID: 38128187 DOI: 10.1016/j.biopha.2023.116021] [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: 09/18/2023] [Revised: 11/23/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Hepatocellular carcinoma (HCC) poses a heavy burden on human health with high morbidity and mortality rates. Systematic therapy is crucial for advanced and mid-term HCC, but faces a significant challenge from therapeutic resistance, weakening drug effectiveness. Metabolic reprogramming has gained attention as a key contributor to therapeutic resistance. Cells change their metabolism to meet energy demands, adapt to growth needs, or resist environmental pressures. Understanding key enzyme expression patterns and metabolic pathway interactions is vital to comprehend HCC occurrence, development, and treatment resistance. Exploring metabolic enzyme reprogramming and pathways is essential to identify breakthrough points for HCC treatment. Targeting metabolic enzymes with inhibitors is key to addressing these points. Inhibitors, combined with systemic therapeutic drugs, can alleviate resistance, prolong overall survival for advanced HCC, and offer mid-term HCC patients a chance for radical resection. Advances in metabolic research methods, from genomics to metabolomics and cells to organoids, help build the HCC metabolic reprogramming network. Recent progress in biomaterials and nanotechnology impacts drug targeting and effectiveness, providing new solutions for systemic therapeutic drug resistance. This review focuses on metabolic enzyme changes, pathway interactions, enzyme inhibitors, research methods, and drug delivery targeting metabolic reprogramming, offering valuable references for metabolic approaches to HCC treatment.
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Affiliation(s)
- Qi Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Jilin University, Changchun 130021, China
| | - Juan Liu
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing 100021, China; Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China; Institute for Organ Transplant and Bionic Medicine, Tsinghua University, Beijing 102218, China; Key Laboratory of Digital Intelligence Hepatology (Ministry of Education/Beijing), School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Ziye Chen
- Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Jingjing Zheng
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Yunfang Wang
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing 100021, China; Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China; Institute for Organ Transplant and Bionic Medicine, Tsinghua University, Beijing 102218, China; Clinical Translational Science Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China; Key Laboratory of Digital Intelligence Hepatology (Ministry of Education/Beijing), School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Jiahong Dong
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Jilin University, Changchun 130021, China; Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing 100021, China; Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China; Institute for Organ Transplant and Bionic Medicine, Tsinghua University, Beijing 102218, China; Key Laboratory of Digital Intelligence Hepatology (Ministry of Education/Beijing), School of Clinical Medicine, Tsinghua University, Beijing, China.
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3
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Anh NH, Long NP, Min YJ, Ki Y, Kim SJ, Jung CW, Park S, Kwon SW, Lee SJ. Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis. Metabolites 2023; 13:1112. [PMID: 37999208 PMCID: PMC10672761 DOI: 10.3390/metabo13111112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/20/2023] [Accepted: 10/24/2023] [Indexed: 11/25/2023] Open
Abstract
Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.
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Affiliation(s)
- Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Young Jin Min
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Yujin Ki
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul 08826, Republic of Korea; (Y.K.); (S.P.)
| | - Sun Jo Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Cheol Woon Jung
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Seongoh Park
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul 08826, Republic of Korea; (Y.K.); (S.P.)
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Seul Ji Lee
- College of Pharmacy, Kangwon National University, Chuncheon 24341, Republic of Korea
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4
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Wu X, Wang Z, Luo L, Shu D, Wang K. Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 4:1065506. [PMID: 36688143 PMCID: PMC9845953 DOI: 10.3389/fmedt.2022.1065506] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 12/06/2022] [Indexed: 01/05/2023] Open
Abstract
Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.
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Affiliation(s)
- Xingyun Wu
- West China School of Basic Medical Science & Forensic Medicine, Sichuan University, Chengdu, China
| | - Zihao Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Li Luo
- Center for Reproductive Medicine, Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, China,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Dan Shu
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, China,Correspondence: Kui Wang Dan Shu
| | - Kui Wang
- West China School of Basic Medical Science & Forensic Medicine, Sichuan University, Chengdu, China,Correspondence: Kui Wang Dan Shu
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5
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Wang P, Yuan Y, Qiu M. Identification of Plasma Metabolites Associated with Lung Cancer Survival. Methods Mol Biol 2023; 2695:181-193. [PMID: 37450119 DOI: 10.1007/978-1-0716-3346-5_12] [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] [Indexed: 07/18/2023]
Abstract
Limited knowledge has been reported regarding the performance of plasma metabolomics for predicting lung cancer prognosis. In this chapter, we compared the plasma metabolomics of lung cancer patients with differential disease-free survival (DFS, <3 years vs. >4 years) using liquid chromatography-mass spectrometry. We identified 29 survival-related aqueous metabolites but no lipid metabolites. Amino acids and organic acids constitute the majority of these metabolites. The metabolic pathways of these metabolites were cysteine and methionine metabolism and arginine biosynthesis. The Cox proportional hazards regression models confirmed the predictive values of 18 metabolites for DFS, while the phosphocholine and xanthine showed independent predictive values. Regarding cancer phenotypes, thelephoric acid, phosphocholine, inosine, 3-hydroxyanthranilic acid, hypoxanthine, xanthine, and 4-hydroxybenzoic acid showed good correction with lymph node metastasis. Taken together, plasma metabolomics is a powerful tool for identifying prognostic metabolites of lung cancer.
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Affiliation(s)
- Peiyu Wang
- Thoracic Oncology Institute/Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Yuyao Yuan
- Department of Pathology, School of Basic Medical Sciences, Institute of Systems Biomedicine, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China
| | - Mantang Qiu
- Thoracic Oncology Institute/Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
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6
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Ni Y, He J, Chalise P. Integration of differential expression and network structure for 'omics data analysis. Comput Biol Med 2022; 150:106133. [PMID: 36179515 DOI: 10.1016/j.compbiomed.2022.106133] [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: 04/08/2022] [Revised: 08/23/2022] [Accepted: 09/18/2022] [Indexed: 11/25/2022]
Abstract
Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.
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Affiliation(s)
- Yonghui Ni
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA
| | - Jianghua He
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA
| | - Prabhakar Chalise
- Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA.
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7
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Microbiome-Based Metabolic Therapeutic Approaches in Alcoholic Liver Disease. Int J Mol Sci 2022; 23:ijms23158749. [PMID: 35955885 PMCID: PMC9368757 DOI: 10.3390/ijms23158749] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/21/2022] [Accepted: 08/03/2022] [Indexed: 11/21/2022] Open
Abstract
Alcohol consumption is a global healthcare problem. Chronic alcohol consumption generates a wide spectrum of hepatic lesions, the most characteristic of which are steatosis, hepatitis, fibrosis, and cirrhosis. Alcoholic liver diseases (ALD) refer to liver damage and metabolomic changes caused by excessive alcohol intake. ALD present several clinical stages of severity found in liver metabolisms. With increased alcohol consumption, the gut microbiome promotes a leaky gut, metabolic dysfunction, oxidative stress, liver inflammation, and hepatocellular injury. Much attention has focused on ALD, such as alcoholic fatty liver (AFL), alcoholic steatohepatitis (ASH), alcoholic cirrhosis (AC), hepatocellular carcinoma (HCC), a partnership that reflects the metabolomic significance. Here, we report on the global function of inflammation, inhibition, oxidative stress, and reactive oxygen species (ROS) mechanisms in the liver biology framework. In this tutorial review, we hypothetically revisit therapeutic gut microbiota-derived alcoholic oxidative stress, liver inflammation, inflammatory cytokines, and metabolic regulation. We summarize the perspective of microbial therapy of genes, gut microbes, and metabolic role in ALD. The end stage is liver transplantation or death. This review may inspire a summary of the gut microbial genes, critical inflammatory molecules, oxidative stress, and metabolic routes, which will offer future promising therapeutic compounds in ALD.
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8
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U MRA, Shen EYL, Cartlidge C, Alkhatib A, Thursz MR, Waked I, Gomaa AI, Holmes E, Sharma R, Taylor-Robinson SD. Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma. Cancer Epidemiol Biomarkers Prev 2022; 31:1261-1274. [PMID: 35545293 DOI: 10.1158/1055-9965.epi-21-0687] [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: 05/31/2021] [Revised: 09/17/2021] [Accepted: 05/09/2022] [Indexed: 12/24/2022] Open
Abstract
This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.
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Affiliation(s)
- Mei Ran Abellona U
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Eric Yi-Liang Shen
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Department of Radiation Oncology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | | | - Alzhraa Alkhatib
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- National Liver Unit, Menoufiya University, Shbeen El Kom, Egypt
| | - Mark R Thursz
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Imam Waked
- National Liver Unit, Menoufiya University, Shbeen El Kom, Egypt
| | - Asmaa I Gomaa
- National Liver Unit, Menoufiya University, Shbeen El Kom, Egypt
| | - Elaine Holmes
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Health Futures Institute, Murdoch University, Perth WA, Australia
| | - Rohini Sharma
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Simon D Taylor-Robinson
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
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9
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The Emerging Role of Branched-Chain Amino Acids in Liver Diseases. Biomedicines 2022; 10:biomedicines10061444. [PMID: 35740464 PMCID: PMC9220261 DOI: 10.3390/biomedicines10061444] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/07/2022] [Accepted: 06/16/2022] [Indexed: 02/06/2023] Open
Abstract
Chronic liver diseases pose a substantial health burden worldwide, with approximately two million deaths each year. Branched-chain amino acids (BCAAs)-valine, leucine, and isoleucine-are a group of essential amino acids that are essential for human health. Despite the necessity of a dietary intake of BCAA, emerging data indicate the undeniable correlation between elevated circulating BCAA levels and chronic liver diseases, including non-alcoholic fatty liver diseases (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC). Moreover, circulatory BCAAs were positively associated with a higher cholesterol level, liver fat content, and insulin resistance (IR). However, BCAA supplementation was found to provide positive outcomes in cirrhosis and HCC patients. This review will attempt to address the contradictory claims found in the literature, with a special focus on BCAAs' distribution, key signaling pathways, and the modulation of gut microbiota. This should provide a better understanding of BCAAs' possible contribution to liver health.
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10
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Park C, Kim B, Park T. DeepHisCoM: deep learning pathway analysis using hierarchical structural component models. Brief Bioinform 2022; 23:6590446. [DOI: 10.1093/bib/bbac171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/04/2022] [Accepted: 04/18/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.
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Affiliation(s)
- Chanwoo Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea
| | - Boram Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
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11
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Feng N, Yu F, Yu F, Feng Y, Zhu X, Xie Z, Zhai Y. Metabolomic biomarkers for hepatocellular carcinoma: A systematic review. Medicine (Baltimore) 2022; 101:e28510. [PMID: 35060504 PMCID: PMC8772637 DOI: 10.1097/md.0000000000028510] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 12/16/2021] [Indexed: 01/05/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a highly malignant cancer which lack of effective diagnosis and prognosis biomarkers, therefore surging studies focused on the metabolite candidates for HCC. The current study was designed to systematically review the metabolic studies for HCC, summarize the current available evidence and provide implication for further studies within this area. By systematically screening Pubmed and Embase, and eligibility assessment, we eventually included 55 pieces of studies. After summarized their characteristics, we reviewed them by 3 parts, regarding to the different biofluid they carried out the experiments. By collecting the candidates from all the included studies, we carried out pathway enrichment to see the representative of the reported candidates, as expected the pathway consistent with the current knowledge of HCC. Next, we conduct quality assessment on the included studies. Only 36% of the current evidence grouped as high quality, indicating the quality of metabolic studies needs further improvement.
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Affiliation(s)
- Ningning Feng
- Department of Infection Disease & Hepatology Ward, Zibo Central Hospital, Shandong, China
| | - Fatao Yu
- Department of Infection Disease & Hepatology Ward, Zibo Central Hospital, Shandong, China
| | - Feng Yu
- Oncology Department, Zibo Central Hospital, Shandong, China
| | - Yuling Feng
- Department of Infection Disease & Hepatology Ward, Zibo Central Hospital, Shandong, China
| | - Xiaolin Zhu
- Department of Infection Disease & Hepatology Ward, Zibo Central Hospital, Shandong, China
| | - Zhihui Xie
- Department of Infection Disease & Hepatology Ward, Zibo Central Hospital, Shandong, China
| | - Yi Zhai
- Oncology Department, Zibo Central Hospital, Shandong, China
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12
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Jung MK, Okekunle AP, Lee JE, Sung MK, Lim YJ. Role of Branched-chain Amino Acid Metabolism in Tumor Development and Progression. J Cancer Prev 2021; 26:237-243. [PMID: 35047449 PMCID: PMC8749315 DOI: 10.15430/jcp.2021.26.4.237] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/30/2021] [Accepted: 11/08/2021] [Indexed: 12/14/2022] Open
Abstract
Branched-chain amino acids (BCAAs), isoleucine, leucine and valine, are essential amino acids with vital roles in protein synthesis and energy production. We reviewed the fundamentals of BCAA metabolism in advanced cancer patients. BCAAs and various catabolic products act as signalling molecules, which activate mechanisms ranging from protein synthesis to insulin secretion. Recently, BCAA metabolism has been suggested to contribute to cancer progression. Of particular interest is the modulation of the mTOR activity by BCAAs. There are likely multiple pathways involved in BCAA metabolism implicated in carcinogenesis. Understanding the mechanism(s) underlying altered BCAAs metabolism will significantly advance the current understanding of nutrient involvement in carcinogenesis and direct future studies to unravel the significance of BCCA metabolites in tumor development and progression.
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Affiliation(s)
- Min Kyu Jung
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Korea
| | - Akinkunmi Paul Okekunle
- Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul, Korea.,Research Institute of Human Ecology, Seoul National University, Seoul, Korea
| | - Jung Eun Lee
- Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul, Korea.,Research Institute of Human Ecology, Seoul National University, Seoul, Korea
| | - Mi Kyung Sung
- Department of Food and Nutrition, Sookmyung Women's University, Seoul, Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
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13
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Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions. J Proteomics 2021; 246:104310. [PMID: 34198014 DOI: 10.1016/j.jprot.2021.104310] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/31/2021] [Accepted: 06/25/2021] [Indexed: 12/11/2022]
Abstract
Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.
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Khalil A, Elfert A, Ghanem S, Helal M, Abdelsattar S, Elgedawy G, Obada M, Abdel-Samiee M, El-Said H. The role of metabolomics in hepatocellular carcinoma. EGYPTIAN LIVER JOURNAL 2021. [DOI: 10.1186/s43066-021-00085-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Abstract
Background
Hepatocellular carcinoma is the most common primary liver malignancy, with the highest incidence in the developing world, including Egypt. Hepatocellular carcinoma is usually diagnosed in the terminal stage of the disease because of the low sensitivity of the available screening tests. During the process of carcinogenesis, the cellular metabolism is altered to allow cancer cells to adapt to the hypoxic environment and therefore increase anabolic synthesis and survival and avoid the apoptotic death signals. These changes in metabolic status can be tracked by metabolomics analysis.
Main body
Metabolomics is a comprehensive approach for identifying metabolic signatures towards the screening, prediction, and earlier diagnosis of hepatocellular carcinoma with greater efficiency than the conventional diagnostic biomarker. The identification of metabolic changes associated with hepatocellular carcinoma is essential to the understanding of disease pathophysiology and enables better monitoring of high-risk individuals. However, due to the complexity of the metabolic pathways associated with hepatocellular carcinoma, the details of these perturbations are still not adequately characterized. The current status of biomarkers for hepatocellular carcinoma and their insufficiencies and metabolic pathways linked to hepatocellular carcinogenesis are briefly addressed in this mini-review. The review focused on the significantly changed metabolites and pathways associated with hepatocellular carcinoma such as phospholipids, bile acids, amino acids, reactive oxygen species metabolism, and the metabolic changes related to energy production in a cancer cell. The review briefly discusses the sensitivity of metabolomics in the prediction and prognosis of hepatocellular carcinoma and the effect of coexisting multiple etiologies of the disease.
Conclusions
Metabolomics profiling is a potentially promising tool for better predicting, diagnosis, and prognosis of hepatocellular carcinoma.
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Recent Advances of Microbiome-Associated Metabolomics Profiling in Liver Disease: Principles, Mechanisms, and Applications. Int J Mol Sci 2021; 22:ijms22031160. [PMID: 33503844 PMCID: PMC7865944 DOI: 10.3390/ijms22031160] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/17/2021] [Accepted: 01/22/2021] [Indexed: 02/06/2023] Open
Abstract
Advances in high-throughput screening of metabolic stability in liver and gut microbiota are able to identify and quantify small-molecule metabolites (metabolome) in different cellular microenvironments that are closest to their phenotypes. Metagenomics and metabolomics are largely recognized to be the “-omics” disciplines for clinical therapeutic screening. Here, metabolomics activity screening in liver disease (LD) and gut microbiomes has significantly delivered the integration of metabolomics data (i.e., a set of endogenous metabolites) with metabolic pathways in cellular environments that can be tested for biological functions (i.e., phenotypes). A growing literature in LD and gut microbiomes reports the use of metabolites as therapeutic targets or biomarkers. Although growing evidence connects liver fibrosis, cirrhosis, and hepatocellular carcinoma, the genetic and metabolic factors are still mainly unknown. Herein, we reviewed proof-of-concept mechanisms for metabolomics-based LD and gut microbiotas’ role from several studies (nuclear magnetic resonance, gas/lipid chromatography, spectroscopy coupled with mass spectrometry, and capillary electrophoresis). A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to improve liver health.
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16
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Masarone M, Troisi J, Aglitti A, Torre P, Colucci A, Dallio M, Federico A, Balsano C, Persico M. Untargeted metabolomics as a diagnostic tool in NAFLD: discrimination of steatosis, steatohepatitis and cirrhosis. Metabolomics 2021; 17:12. [PMID: 33458794 DOI: 10.1007/s11306-020-01756-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 12/09/2020] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Non-Alcoholic Fatty Liver Disease encompasses a spectrum of diseases ranging from simple steatosis to steatohepatitis (or NASH), up to cirrhosis and hepatocellular carcinoma (HCC). The challenge is to recognize the more severe and/or progressive pathology. A reliable non-invasive method does not exist. Untargeted metabolomics is a novel method to discover biomarkers and give insights on diseases pathophysiology. OBJECTIVES We applied metabolomics to understand if simple steatosis, steatohepatitis and cirrhosis in NAFLD patients have peculiar metabolites profiles that can differentiate them among each-others and from controls. METHODS Metabolomics signatures were obtained from 307 subjects from two separated enrollments. The first collected samples from 69 controls and 144 patients (78 steatosis, 23 NASH, 15 NASH-cirrhosis, 8 HCV-cirrhosis, 20 cryptogenic cirrhosis). The second, used as validation-set, enrolled 44 controls and 50 patients (34 steatosis, 10 NASH and 6 NASH-cirrhosis).The "Partial-Least-Square Discriminant-Analysis"(PLS-DA) was used to reveal class separation in metabolomics profiles between patients and controls and among each class of patients, and to reveal the metabolites contributing to class differentiation. RESULTS Several metabolites were selected as relevant, in particular:Glycocholic acid, Taurocholic acid, Phenylalanine, branched-chain amino-acids increased at the increase of the severity of the disease from steatosis to NASH, NASH-cirrhosis, while glutathione decreased (p < 0.001 for each). Moreover, an ensemble machine learning (EML) model was built (comprehending 10 different mathematical models) to verify diagnostic performance, showing an accuracy > 80% in NAFLD clinical stages prediction. CONCLUSIONS Metabolomics profiles of NAFLD patients could be a useful tool to non-invasively diagnose NAFLD and discriminate among the various stages of the disease, giving insights into its pathophysiology.
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Affiliation(s)
- Mario Masarone
- Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 84081, Baronissi, SA, Italy
| | - Jacopo Troisi
- Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 84081, Baronissi, SA, Italy
- Theoreo srl, Via degli Ulivi 3, 84090, Montecorvino Pugliano, SA, Italy
- European Biomedical Research Institute of Salerno (EBRIS), Via S. de Renzi, 3, 84125, Salerno, SA, Italy
- Hosmotic srl, Via R. Bosco 178, 80069, Vico Equense, NA, Italy
| | - Andrea Aglitti
- Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 84081, Baronissi, SA, Italy
| | - Pietro Torre
- Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 84081, Baronissi, SA, Italy
| | - Angelo Colucci
- Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 84081, Baronissi, SA, Italy
- Theoreo srl, Via degli Ulivi 3, 84090, Montecorvino Pugliano, SA, Italy
| | - Marcello Dallio
- Hepatogastroenterology Division, University of Campania "Luigi Vanvitelli", Via S Pansini 5, 80131, Naples, Italy
| | - Alessandro Federico
- Hepatogastroenterology Division, University of Campania "Luigi Vanvitelli", Via S Pansini 5, 80131, Naples, Italy
| | - Clara Balsano
- MESVA Department, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila, Italy
- F. Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy
| | - Marcello Persico
- Internal Medicine and Hepatology Unit, Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 84081, Baronissi, SA, Italy.
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17
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Cao LL, Han Y, Wang Y, Pei L, Yue Z, Qin L, Liu B, Cui J, Jia M, Wang H. Metabolic Profiling Identified a Novel Biomarker Panel for Metabolic Syndrome-Positive Hepatocellular Cancer. Front Endocrinol (Lausanne) 2021; 12:816748. [PMID: 35154012 PMCID: PMC8826723 DOI: 10.3389/fendo.2021.816748] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/14/2021] [Indexed: 12/24/2022] Open
Abstract
Metabolic syndrome (MetS) is an independent risk factor for hepatocellular cancer (HCC). Currently, there is no highly sensitive and specific biomarkers for HCC surveillance in MetS population. Metabolomics has been reported as a powerful technology for biomarker discovery. In the present study, we aimed to explore novel biomarkers with high sensitivity and specificity for MetS-positive [MetS(+)] HCC by metabolomic analysis. At first, many serum metabolites were found dysregulated in MetS(+) HCC individuals. Validation of the dysregulated metabolites by targeted metabolite analyses revealed that serum L-glutamic acid (L-glu), pipecolic acid (PA) and 7-methylguanine (7-mG) were increased in MetS(+) HCC compared to MetS group. Then a biomarker panel including L-glu, PA and alpha-fetoprotein (AFP) was identified as a novel biomarker for the diagnosis of MetS(+) HCC. Receiver operating characteristic (ROC) curve was drawn and the area under the ROC curve (AUC) was 0.87 for discriminating MetS(+) HCC from MetS group. The biomarker panel was capable of detecting small (AUC = 0.82) and early-stage (AUC = 0.78) tumors as well. Moreover, it exhibited great diagnostic performance (AUC = 0.93) for discriminating MetS(+) HCC from other MetS-associated cancers, including colorectal cancer and gastric cancer. Collectively, our study establishes a novel diagnostic tool for MetS(+) HCC.
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Affiliation(s)
- Lin-Lin Cao
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
- *Correspondence: Lin-Lin Cao,
| | - Yi Han
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
| | - Yuanxiao Wang
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
| | - Lin Pei
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
| | - Zhihong Yue
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
| | - Li Qin
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
| | - Boyu Liu
- Department of Pharmacy, Peking University People’s Hospital, Beijing, China
| | - Jingwen Cui
- SCIEX Analytical Instrument Trading Co., Shanghai, China
| | - Mei Jia
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
| | - Hui Wang
- Department of Clinical Laboratory, Peking University People’s Hospital, Beijing, China
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18
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Evans ED, Duvallet C, Chu ND, Oberst MK, Murphy MA, Rockafellow I, Sontag D, Alm EJ. Predicting human health from biofluid-based metabolomics using machine learning. Sci Rep 2020; 10:17635. [PMID: 33077825 PMCID: PMC7572502 DOI: 10.1038/s41598-020-74823-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/05/2020] [Indexed: 12/15/2022] Open
Abstract
Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.
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Affiliation(s)
- Ethan D Evans
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Claire Duvallet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Biobot Analytics, Somerville, MA, 02143, USA
| | - Nathaniel D Chu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael K Oberst
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael A Murphy
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Isaac Rockafellow
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Superpedestrian, Cambridge, MA, 02139, USA
| | - David Sontag
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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19
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Lei C, Chen J, Li H, Fan T, Zheng X, Wang H, Zhang N, Liu Y, Luo X, Wang J, Wang A. Role of the Mitochondrial Citrate-malate Shuttle in Hras12V-Induced Hepatocarcinogenesis: A Metabolomics-Based Analysis. Metabolites 2020; 10:metabo10050193. [PMID: 32414018 PMCID: PMC7281175 DOI: 10.3390/metabo10050193] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/15/2022] Open
Abstract
The activation of the Ras signaling pathway is a crucial process in hepatocarcinogenesis. Till now, no reports have scrutinized the role of dynamic metabolic changes in Ras oncogene-induced transition of the normal and precancerous liver cells to hepatocellular carcinoma in vivo. In the current study, we attempted a comprehensive investigation of Hras12V transgenic mice (Ras-Tg) by concatenating nontargeted metabolomics, transcriptomics analysis, and targeted-metabolomics incorporating [U-13C] glucose. A total of 631 peaks were detected, out of which 555 metabolites were screened. Besides, a total of 122 differently expressed metabolites (DEMs) were identified, and they were categorized and subtyped with the help of variation tendency analysis of the normal (W), precancerous (P), and hepatocellular carcinoma (T) liver tissues. Thus, the positive or negative association between metabolites and the hepatocellular carcinoma and Ras oncogene were identified. The bioinformatics analysis elucidated the hepatocarcinogenesis-associated significant metabolic pathways: glycolysis, mitochondrial citrate-malate shuttle, lipid biosynthesis, pentose phosphate pathway (PPP), cholesterol and bile acid biosynthesis, and glutathione metabolism. The key metabolites and enzymes identified in this analysis were further validated. Moreover, we confirmed the PPP, glycolysis, and conversion of pyruvate to cytosol acetyl-CoA by mitochondrial citrate-malate shuttle, in vivo, by incorporating [U-13C] glucose. In summary, the current study presented the comprehensive bioinformatics analysis, depicting the Ras oncogene-induced dynamic metabolite variations in hepatocarcinogenesis. A significant finding of our study was that the mitochondrial citrate-malate shuttle plays a crucial role in detoxification of lactic acid, maintenance of mitochondrial integrity, and enhancement of lipid biosynthesis, which, in turn, promotes hepatocarcinogenesis.
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Affiliation(s)
- Chuanyi Lei
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Jun Chen
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Huiling Li
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Tingting Fan
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Xu Zheng
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Hong Wang
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Nan Zhang
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Yang Liu
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Xiaoqin Luo
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Jingyu Wang
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
| | - Aiguo Wang
- Department of Comparative Medicine, Laboratory Animal Center, Dalian Medical University, Dalian 116044, Liaoning, China
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20
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Sugar Alcohols Have a Key Role in Pathogenesis of Chronic Liver Disease and Hepatocellular Carcinoma in Whole Blood and Liver Tissues. Cancers (Basel) 2020; 12:cancers12020484. [PMID: 32092943 PMCID: PMC7072169 DOI: 10.3390/cancers12020484] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/15/2020] [Accepted: 02/17/2020] [Indexed: 12/14/2022] Open
Abstract
The major risk factors for hepatocellular carcinoma (HCC) are hepatitis C and B viral infections that proceed to Chronic Liver Disease (CLD). Yet, the early diagnosis and treatment of HCC are challenging because the pathogenesis of HCC is not fully defined. To better understand the onset and development of HCC, untargeted GC-TOF MS metabolomics data were acquired from resected human HCC tissues and their paired non-tumor hepatic tissues (n = 46). Blood samples of the same HCC subjects (n = 23) were compared to CLD (n = 15) and healthy control (n = 15) blood samples. The participants were recruited from the National Liver Institute in Egypt. The GC-TOF MS data yielded 194 structurally annotated compounds. The most strikingly significant alteration was found for the class of sugar alcohols that were up-regulated in blood of HCC patients compared to CLD subjects (p < 2.4 × 10−12) and CLD compared to healthy controls (p = 4.1 × 10−7). In HCC tissues, sugar alcohols were the most significant (p < 1 × 10−6) class differentiating resected HCC tissues from non-malignant hepatic tissues for all HCC patients. Alteration of sugar alcohol levels in liver tissues also defined early-stage HCC from their paired non-malignant hepatic tissues (p = 2.7 × 10−6). In blood, sugar alcohols differentiated HCC from CLD subjects with an ROC-curve of 0.875 compared to 0.685 for the classic HCC biomarker alpha-fetoprotein. Blood sugar alcohol levels steadily increased from healthy controls to CLD to early stages of HCC and finally, to late-stage HCC patients. The increase in sugar alcohol levels indicates a role of aldo-keto reductases in the pathogenesis of HCC, possibly opening novel diagnostic and therapeutic options after in-depth validation.
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21
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Buechler C, Aslanidis C. Role of lipids in pathophysiology, diagnosis and therapy of hepatocellular carcinoma. Biochim Biophys Acta Mol Cell Biol Lipids 2020; 1865:158658. [PMID: 32058031 DOI: 10.1016/j.bbalip.2020.158658] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/05/2019] [Accepted: 02/06/2020] [Indexed: 12/15/2022]
Abstract
Hepatocellular carcinoma (HCC) is an aggressive and widespread cancer. Patients with liver cirrhosis of different aetiologies are at a risk to develop HCC. It is important to know that in approximately 20% of cases primary liver tumors arise in a non-cirrhotic liver. Lipid metabolism is variable in patients with chronic liver diseases, and lipid metabolites involved therein do play a role in the development of HCC. Of note, lipid composition of carcinogenic tissues differs from non-affected liver tissues. High cholesterol and low ceramide levels in the tumors protect the cells from oxidative stress and apoptosis, and do also promote cell proliferation. So far, detailed characterization of the mechanisms by which lipids enable the development of HCC has received little attention. Evaluation of the complex roles of lipids in HCC is needed to better understand the pathophysiology of HCC, the later being of paramount importance for the development of urgently needed therapeutic interventions. Disturbed hepatic lipid homeostasis has systemic consequences and lipid species may emerge as promising biomarkers for early diagnosis of HCC. The challenge is to distinguish lipids specifically related to HCC from changes simply related to the underlying liver disease. This review article discusses aberrant lipid metabolism in patients with HCC.
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Affiliation(s)
- Christa Buechler
- Department of Internal Medicine I, Regensburg University Hospital, Regensburg, Germany.
| | - Charalampos Aslanidis
- Institute for Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany
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22
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Fan Z, Zhou Y, Ressom HW. MOTA: Multi-omic integrative analysis for biomarker discovery. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:243-247. [PMID: 31945887 DOI: 10.1109/embc.2019.8857049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Recent advancement of omic technologies provides researchers with opportunities to search for disease biomarkers at the systems level. However, selection of biomarker candidates from a large number of molecules involved at various layers of the biological system is challenging. In this paper, we propose multi-omic integrative analysis (MOTA), a network-based method that uses information from multi-omic data to identify candidate disease biomarkers. We evaluated the performance of MOTA in selecting disease-associated molecules from four sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. The results demonstrate that MOTA leads to selection of more biomarker candidates that shared by two different cohorts compared to traditional statistical methods. Also, the networks constructed by MOTA allow users to investigate biological significance of the selected biomarker candidates.
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23
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Kim DJ, Cho EJ, Yu KS, Jang IJ, Yoon JH, Park T, Cho JY. Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis. Cancers (Basel) 2019; 11:E1497. [PMID: 31590436 PMCID: PMC6826937 DOI: 10.3390/cancers11101497] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/02/2019] [Accepted: 10/02/2019] [Indexed: 02/06/2023] Open
Abstract
The established biomarker for hepatocellular carcinoma (HCC), serum α-fetoprotein (AFP), has suboptimal performance in early disease stages. This study aimed to develop a metabolite panel to differentiate early-stage HCC from cirrhosis. Cross-sectional metabolomic analyses of serum samples were performed for 53 and 47 patients with early HCC and cirrhosis, respectively, and 50 matched healthy controls. Results were validated in 82 and 80 patients with early HCC and cirrhosis, respectively. To retain a broad spectrum of metabolites, technically distinct analyses (global metabolomic profiling using gas chromatography time-of-flight mass spectrometry and targeted analyses using liquid chromatography with tandem mass spectrometry) were employed. Multivariate analyses classified distinct metabolites; logistic regression was employed to construct a prediction model for HCC diagnosis. Five metabolites (methionine, proline, ornithine, pimelylcarnitine, and octanoylcarnitine) were selected in a panel. The panel distinguished HCC from cirrhosis and normal controls, with an area under the receiver operating curve (AUC) of 0.82; this was significantly better than that of AFP (AUC: 0.75). During validation, the panel demonstrated significantly better predictability (AUC: 0.94) than did AFP (AUC: 0.78). Defects in ammonia recycling, the urea cycle, and amino acid metabolism, demonstrated on enrichment pathway analysis, may reliably distinguish HCC from cirrhosis. Compared with AFP alone, the metabolite panel substantially improved early-stage HCC detection.
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Affiliation(s)
- Da Jung Kim
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Korea.
| | - Eun Ju Cho
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Korea.
| | - Kyung-Sang Yu
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Korea.
| | - In-Jin Jang
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Korea.
| | - Jung-Hwan Yoon
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Korea.
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea.
| | - Joo-Youn Cho
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Korea.
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24
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Wang W, Liu X, Wu J, Kang X, Xie Q, Sheng J, Xu W, Liu D, Zheng W. Plasma metabolite profiling reveals potential biomarkers of giant cell tumor of bone by using NMR-based metabolic profiles: A cross-sectional study. Medicine (Baltimore) 2019; 98:e17445. [PMID: 31577769 PMCID: PMC6783185 DOI: 10.1097/md.0000000000017445] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Giant cell tumor (GCT) of bone is a locally aggressive bone tumor, which accounts for 4% to 5% of all primary bone tumors. At present, the early diagnosis and postoperative recurrence monitoring are still more difficult due to the lack of effective biomarkers in GCT. As an effective tool, metabolomics has played an essential role in the biomarkers research of many tumors. However, there has been no related study of the metabolomics of GCT up to now. The purpose of this study was to identify several key metabolites as potential biomarkers for GCT by using nuclear magnetic resonance (NMR)-based metabolic profiles.Patients with GCT in our hospital were recruited in this study and their plasma was collected as the research sample, and plasma collected from healthy subjects was considered as the control. NMR was then utilized to detect all samples. Furthermore, based on correlation coefficients, variable importance for the projection values and P values of metabolites obtained from multidimensional statistical analysis, the most critical metabolites were selected as potential biomarkers of GCT. Finally, relevant metabolic pathways involved in these potential biomarkers were determined by database retrieval, based on which the metabolic pathways were plotted.Finally, 28 GCT patients and 26 healthy volunteers agreed to participate in the study. In the multidimensional statistical analysis, all results showed that there was obvious difference between the GCT group and the control group. Ultimately, 18 metabolites with significant differences met the selection condition, which were identified as potential biomarkers. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Human Metabolome Database (HMD) database searching and literature review, these metabolites were found to be mainly correlated with glucose metabolism, fat metabolism, amino acid metabolism, and intestinal microbial metabolism. These metabolic disorders might, in turn, reflect important pathological processes such as proliferation and migration of tumor cells and immune escape in GCT.Our work showed that these potential biomarkers identified appeared to have early diagnostic and relapse monitoring values for GCT, which deserve to be further investigated. In addition, it also suggested that metabolomics profiling approach is a promising screening tool for the diagnosis and relapse monitoring of GCT patients.
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Affiliation(s)
| | | | - Juan Wu
- Department of Pharmacy, General Hospital of Western Theater Command, Chengdu city, Sichuan Province, People's Republic of China
| | | | | | | | - Wei Xu
- Department of Orthopedics
| | - Da Liu
- Department of Orthopedics
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25
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Borgsmüller N, Gloaguen Y, Opialla T, Blanc E, Sicard E, Royer AL, Le Bizec B, Durand S, Migné C, Pétéra M, Pujos-Guillot E, Giacomoni F, Guitton Y, Beule D, Kirwan J. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data. Metabolites 2019; 9:metabo9090171. [PMID: 31438611 PMCID: PMC6780109 DOI: 10.3390/metabo9090171] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 08/18/2019] [Accepted: 08/20/2019] [Indexed: 02/02/2023] Open
Abstract
Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GC-MS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Medium- and low-quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset, we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results in integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.
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Affiliation(s)
- Nico Borgsmüller
- Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany
- Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
| | - Yoann Gloaguen
- Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany
- Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
| | - Tobias Opialla
- Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
- Integrative Proteomics and Metabolomics, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany
| | - Eric Blanc
- Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany
- Charité - Universitätsmedizin Berlin, 10178 Berlin, Germany
| | - Emilie Sicard
- Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont - Ferrand, France
| | - Anne-Lise Royer
- LABERCA, Oniris, INRA, Université Bretagne - Loire, 44307 Nantes, France
| | - Bruno Le Bizec
- LABERCA, Oniris, INRA, Université Bretagne - Loire, 44307 Nantes, France
| | - Stéphanie Durand
- Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont - Ferrand, France
| | - Carole Migné
- Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont - Ferrand, France
| | - Mélanie Pétéra
- Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont - Ferrand, France
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont - Ferrand, France
| | - Franck Giacomoni
- Université Clermont Auvergne, INRA, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000 Clermont - Ferrand, France
| | - Yann Guitton
- LABERCA, Oniris, INRA, Université Bretagne - Loire, 44307 Nantes, France
| | - Dieter Beule
- Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
- Charité - Universitätsmedizin Berlin, 10178 Berlin, Germany
| | - Jennifer Kirwan
- Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany.
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany.
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Jin H, Ma N, Li X, Kang M, Guo M, Song L. Application of GC/MS-Based Metabonomic Profiling in Studying the Therapeutic Effects of Aconitum carmichaeli with Ampelopsis japonica Extract on Collagen-Induced Arthritis in Rats. Molecules 2019; 24:molecules24101934. [PMID: 31137469 PMCID: PMC6571615 DOI: 10.3390/molecules24101934] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 05/11/2019] [Accepted: 05/16/2019] [Indexed: 11/16/2022] Open
Abstract
Aconitum carmichaeli with Ampelopsis japonica (AA) is a classical traditional Chinese medicine (TCM) formula. There are a lot of examples showing that AA can be used to treat rheumatoid arthritis, but its mechanism of action is still not completely clear. In this research, collagen-induced arthritis (CIA) was chosen as a rheumatoid arthritis (RA) model. Rats of treated groups were continuously administered Aconitum carmichaeli (AC), Ampelopsis japonica (AJ) and Aconitum carmichaeli + Ampelopsis japonica (AA) orally once a day from the day after the onset of arthritis (day 7) until day 42. The results showed that AA not only significantly reduced paw swelling, but also improved the levels of TNF-α and IL-6 in serum. GC-MS-based urine metabonomics was established to analysis metabolic profiles and 21 biomarkers of RA rats were identified by the Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) methods. The prediction rate of the SVM method for the 21 biomarkers was 100%. Twenty of 21 biomarkers, including D-galactose, inositol and glycerol, gradually returned to normal levels after administration of AA. Metabolomic Pathway Analysis (MetPA) generated three related metabolic pathways-galactose metabolism, glycerolipid metabolism and inositol phosphate metabolism-which explain the mechanism of AA treatment of rheumatoid arthritis. This research provides a better understanding of the therapeutic effects and possible therapeutic mechanism of action of a complex TCM (AA) on rheumatoid arthritis.
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Affiliation(s)
- Hua Jin
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Ningning Ma
- School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Xin Li
- School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Mingqin Kang
- Changchun Customs (Former Jilin Inspection and Quarantine Bureau), Changchun 130012, China.
| | - Maojuan Guo
- Department of Pathology, School of integrative Medicine, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
| | - Lili Song
- School of Traditional Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Jian Kang Chan Ye Yuan, Jinghai Dist., Tianjin 301617, China.
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Abstract
Branched chain amino acids (BCAAs) are building blocks for all life-forms. We review here the fundamentals of BCAA metabolism in mammalian physiology. Decades of studies have elicited a deep understanding of biochemical reactions involved in BCAA catabolism. In addition, BCAAs and various catabolic products act as signaling molecules, activating programs ranging from protein synthesis to insulin secretion. How these processes are integrated at an organismal level is less clear. Inborn errors of metabolism highlight the importance of organismal regulation of BCAA physiology. More recently, subtle alterations of BCAA metabolism have been suggested to contribute to numerous prevalent diseases, including diabetes, cancer, and heart failure. Understanding the mechanisms underlying altered BCAA metabolism and how they contribute to disease pathophysiology will keep researchers busy for the foreseeable future.
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Affiliation(s)
- Michael Neinast
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;
| | - Danielle Murashige
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;
| | - Zoltan Arany
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;
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28
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张 磊, 范 志, 康 华, 王 宇, 刘 树, 单 忠. [High-performance liquid chromatography-mass spectrometry-based serum metabolic profiling in patients with HBV-related hepatocellular carcinoma]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2019; 39:49-56. [PMID: 30692066 PMCID: PMC6765583 DOI: 10.12122/j.issn.1673-4254.2019.01.08] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To explore the diagnostic value of the serum metabolites identified by high-performance liquid chromatography-mass spectrometry (HPLC/MS) for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODS A total of 126 patients admitted to Tianjin Third Central Hospital were enrolled, including 27 patients with HBV-related hepatitis with negative viral DNA (DNA-N), 24 with HBV-related hepatitis with positive viral DNA, 24 with HBV-related liver cirrhosis, 27 with HBV-related HCC undergoing surgeries or radiofrequency ablation, and 24 with HBV-related HCC receiving interventional therapy, with 25 healthy volunteers as the normal control group. Serum samples were collected from all the subjects for HPLC/MS analysis, and the data were pretreated to establish an orthogonal partial least- squares discriminant analysis (OPLS-DA) model. The differential serum metabolites were preliminarily screened by comparisons between the HBV groups and the control group, and the characteristic metabolites were identified according to the results of non-parametric test. The potential clinical values of these characteristic metabolites were evaluated using receiver operator characteristic curve (ROC) analysis. RESULTS A total of 25 characteristic metabolites were identified in the HBV- infected patients, including 9 lysophosphatidylcholines, 2 fatty acids, 17α-estradiol, sphinganine, 5-methylcytidine, vitamin K2, lysophosphatidic acid, glycocholic acid and 8 metabolites with few reports. The patients with HBV- related HCC showed 22 differential serum metabolites compared with the control group, 4 differential metabolites compared with patients with HBV-related liver cirrhosis; 10 differential metabolites were identified in patients with HBV-related HCC receiving interventional therapy compared with those receiving surgical resection or radiofrequency ablation. From the normal control group to HBV-related HCC treated by interventional therapy, many metabolites underwent variations following a similar pattern. CONCLUSIONS We identified 25 characteristic metabolites in patients with HBV-related HCC, and these metabolites may have potential clinical values in the diagnosis of HBV-related HCC. The continuous change of some of these metabolites may indicate the possibility of tumorigenesis, and some may also have indications for the choice of surgical approach.
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Affiliation(s)
- 磊 张
- 天津大学化工学院,天津 300072Chemical Engineering Institute, Tianjin University, Tianjin 300072 China
- 天津市第三中心医院检验科//天津市人工细胞重点实验室//卫生部人工细胞工程技术研究中心,天津 300170Clinical Laboratory Department of Tianjin Third Central Hospital, Tianjin Key Laboratory of Artificial Cell, Artificial Cell Engineering Technology Research Center of Public Health Ministry, Tianjin 300170 China
| | - 志娟 范
- 天津市第三中心医院检验科//天津市人工细胞重点实验室//卫生部人工细胞工程技术研究中心,天津 300170Clinical Laboratory Department of Tianjin Third Central Hospital, Tianjin Key Laboratory of Artificial Cell, Artificial Cell Engineering Technology Research Center of Public Health Ministry, Tianjin 300170 China
| | - 华 康
- 天津市第三中心医院检验科//天津市人工细胞重点实验室//卫生部人工细胞工程技术研究中心,天津 300170Clinical Laboratory Department of Tianjin Third Central Hospital, Tianjin Key Laboratory of Artificial Cell, Artificial Cell Engineering Technology Research Center of Public Health Ministry, Tianjin 300170 China
| | - 宇凡 王
- 天津市第三中心医院检验科//天津市人工细胞重点实验室//卫生部人工细胞工程技术研究中心,天津 300170Clinical Laboratory Department of Tianjin Third Central Hospital, Tianjin Key Laboratory of Artificial Cell, Artificial Cell Engineering Technology Research Center of Public Health Ministry, Tianjin 300170 China
| | - 树业 刘
- 天津市第三中心医院检验科//天津市人工细胞重点实验室//卫生部人工细胞工程技术研究中心,天津 300170Clinical Laboratory Department of Tianjin Third Central Hospital, Tianjin Key Laboratory of Artificial Cell, Artificial Cell Engineering Technology Research Center of Public Health Ministry, Tianjin 300170 China
| | - 忠强 单
- 天津大学化工学院,天津 300072Chemical Engineering Institute, Tianjin University, Tianjin 300072 China
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Li Z, Zuo Y, Xu C, Varghese RS, Ressom HW. INDEED: R package for network based differential expression analysis. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2019; 2018:2709-2712. [PMID: 31179159 DOI: 10.1109/bibm.2018.8621426] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
With recent advancement of omics technologies, fueled by decreased cost and increased number of available datasets, computational methods for differential expression analysis are sought to identify disease-associated biomolecules. Conventional differential expression analysis methods (e.g. student's t-test, ANOVA) focus on assessing mean and variance of biomolecules in each biological group. On the other hand, network-based approaches take into account the interactions between biomolecules in choosing differentially expressed ones. These interactions are typically evaluated by correlation methods that tend to generate over-complicated networks due to many seemingly indirect associations. In this paper, we introduce a new R/Bioconductor package INDEED that allows users to construct a sparse network based on partial correlation, and to identify biomolecules that have significant changes both at individual expression and pairwise interaction levels. We applied INDEED for analysis of two omic datasets acquired in a cancer biomarker discovery study to help rank disease-associated biomolecules. We believe biomolecules selected by INDEED lead to improved sensitivity and specificity in detecting disease status compared to those selected by conventional statistical methods. Also, INDEED's framework is amenable to further expansion to integrate networks from multi-omic studies, thereby allowing selection of reliable disease-associated biomolecules or disease biomarkers.
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Affiliation(s)
- Zhenzhi Li
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC, USA
| | - Yiming Zuo
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC, USA
| | - Chaohui Xu
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC, USA
| | - Rency S Varghese
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC, USA
| | - Habtom W Ressom
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington DC, USA
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30
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Liu XJ, Zhang CE, Yu XH, Liu RX, Qin XM, Jia JD, Ma ZJ. Serum metabonomics characterization of liver fibrosis induced by bile duct-ligated in rats and the intervention effects of herb compound 861. J LIQ CHROMATOGR R T 2019. [DOI: 10.1080/10826076.2019.1574815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Xiao-Jie Liu
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P. R. China
| | - Cong-En Zhang
- Beijing Friendship Hospital, Capital Medical University, Beijing, P. R. China
| | - Xiao-Hong Yu
- Beijing Friendship Hospital, Capital Medical University, Beijing, P. R. China
| | - Rui-Xia Liu
- Beijing Friendship Hospital, Capital Medical University, Beijing, P. R. China
| | - Xue-Mei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, P. R. China
| | - Ji-Dong Jia
- Beijing Friendship Hospital, Capital Medical University, Beijing, P. R. China
- Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis
| | - Zhi-Jie Ma
- Beijing Friendship Hospital, Capital Medical University, Beijing, P. R. China
- Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis
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31
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Biotransformation-based metabolomics profiling method for determining and quantitating cancer-related metabolites. J Chromatogr A 2018; 1580:80-89. [DOI: 10.1016/j.chroma.2018.10.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 10/14/2018] [Accepted: 10/16/2018] [Indexed: 01/08/2023]
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32
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Guo W, Tan HY, Wang N, Wang X, Feng Y. Deciphering hepatocellular carcinoma through metabolomics: from biomarker discovery to therapy evaluation. Cancer Manag Res 2018; 10:715-734. [PMID: 29692630 PMCID: PMC5903488 DOI: 10.2147/cmar.s156837] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer, with increasing prevalence worldwide. The mortality rate of HCC is similar to its incidence rate, which reflects its poor prognosis. At present, the diagnosis of HCC is still mostly dependent on invasive biopsy, imaging methods, and serum α-fetoprotein (AFP) testing. Because of the asymptomatic nature of early HCC, biopsy and imaging methods usually detect HCC at the middle–late stages. AFP has limited sensitivity and specificity, as many other nonmalignant liver diseases can also result in a very high serum level of AFP. Therefore, better biomarkers with higher sensitivity and specificity at earlier stages are greatly needed. Since metabolic reprogramming is an essential hallmark of cancer and the liver is the metabolic hub of living systems, it is useful to investigate HCC from a metabolic perspective. As a noninvasive and nondestructive approach, metabolomics provides holistic information on dynamically metabolic responses of living systems to both endogenous and exogenous factors. Therefore, it would be conducive to apply metabolomics in investigating HCC. In this review, we summarize recent metabolomic studies on HCC cellular, animal, and clinicopathologic models with attention to metabolomics as a biomarker in cancer diagnosis. Recent applications of metabolomics with respect to therapeutic and prognostic evaluation of HCC are also covered, with emphasis on the potential of treatment by drugs from natural products. In the last section, the current challenges and trends of future development of metabolomics on HCC are discussed. Overall, metabolomics provides us with novel insight into the diagnosis, prognosis, and therapeutic evaluation of HCC.
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Affiliation(s)
- Wei Guo
- School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Hor Yue Tan
- School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Ning Wang
- School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.,Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China
| | - Xuanbin Wang
- Laboratory of Chinese Herbal Pharmacology, Oncology Center, Renmin Hospital, Hubei University of Medicine, Shiyan, China.,Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, Shiyan, China
| | - Yibin Feng
- School of Chinese Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong.,Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China.,Laboratory of Chinese Herbal Pharmacology, Oncology Center, Renmin Hospital, Hubei University of Medicine, Shiyan, China.,Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, Shiyan, China
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33
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Cala MP, Agulló‐Ortuño MT, Prieto‐García E, González‐Riano C, Parrilla‐Rubio L, Barbas C, Díaz‐García CV, García A, Pernaut C, Adeva J, Riesco MC, Rupérez FJ, Lopez‐Martin JA. Multiplatform plasma fingerprinting in cancer cachexia: a pilot observational and translational study. J Cachexia Sarcopenia Muscle 2018; 9:348-357. [PMID: 29464940 PMCID: PMC5879957 DOI: 10.1002/jcsm.12270] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 10/04/2017] [Accepted: 10/24/2017] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Cachexia is a metabolic syndrome that affects up to 50-80% of cancer patients. The pathophysiology is characterized by a variable combination of reduced food intake and abnormal metabolism, including systemic inflammation and negative protein and energy balance. Despite its high clinical significance, defined diagnostic criteria and established therapeutic strategies are lacking. The 'omics' technologies provide a global view of biological systems. We hypothesize that blood-based metabolomics might identify findings in cachectic patients that could provide clues to gain knowledge on its pathophysiology, and eventually postulate new therapeutic strategies. METHODS This is a cross-sectional observational study in two cohorts of cancer patients, with and without cachexia. Patients were consecutively recruited from routine clinical practice of a General Oncology Department at '12 de Octubre' University Hospital. Selected clinical and biochemical features were collected. Blood metabolite fingerprinting was performed using three analytical platforms, gas chromatography coupled to mass spectrometry (GC-MS), capillary electrophoresis coupled to mass spectrometry (CE-MS), and liquid chromatography coupled to mass spectrometry (LC-MS). Besides, we performed pathway-based metabolite analyses to obtain more information on biological functions. RESULTS A total of 15 subjects were included in this study, 8 cachectic and 7 non-cachectic patients. Metabolomic analyses were able to correctly classify their samples in 80% (GC-MS), 97% (CE-MS), 96% [LC-MS (positive mode)], and 89% [LC-MS (negative mode)] of the cases. The most prominent metabolic alteration in plasma of cachectic patients was the decrease of amino acids and derivatives [especially arginine, tryptophan, indolelactic acid, and threonine, with 0.4-fold change (FC) compared with non-cachectic patients], along with the reduction of glycerophospholipids [mainly lysophosphatidylcholines(O-16:0) and lysophosphatidylcholines(20:3) sn-1, FC = 0.1] and sphingolipids [SM(d30:0), FC = 0.5]. The metabolite with the highest increase was cortisol (FC = 1.6). Such alterations suggest a role of the following metabolic pathways in the pathophysiology of cancer cachexia: arginine and proline metabolism; alanine, aspartate, and glutamate metabolism; phenylalanine metabolism; lysine degradation; aminoacyl-tRNA biosynthesis; fatty acid elongation in mitochondria; tricarboxylic acids cycle; among others. CONCLUSIONS These findings suggest that plasma amino acids and lipids profiling has great potential to find the mechanisms involved in the pathogenesis of cachexia. Metabolic profiling of plasma from cancer patients show differences between cachexia and non-cachexia in amino acids and lipids that might be related to mechanisms involved in its pathophysiology. A better understanding of these mechanisms might identify novel therapeutic approaches to palliate this unmet medical condition.
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Affiliation(s)
- Mónica Patricia Cala
- Centre for Metabolomic and Bioanalysis (CEMBIO), Facultad de FarmaciaUniversidad San Pablo CEUUrbanización Montepríncipe, M‐501 km 028660Boadilla del Monte, MadridSpain
- Grupo de Investigación en Química Analítica y Bioanalítica (GABIO), Department of Chemistry, Faculty of SciencesUniversidad de los AndesCra. 1 No. 18a‐10111710BogotáColombia
| | - María Teresa Agulló‐Ortuño
- Clinical & Translational Cancer Research GroupInstituto de Investigación Sanitaria Hospital 12 de Octubre (i+12)Av Córdoba s/n28041MadridSpain
| | - Elena Prieto‐García
- Clinical & Translational Cancer Research GroupInstituto de Investigación Sanitaria Hospital 12 de Octubre (i+12)Av Córdoba s/n28041MadridSpain
| | - Carolina González‐Riano
- Centre for Metabolomic and Bioanalysis (CEMBIO), Facultad de FarmaciaUniversidad San Pablo CEUUrbanización Montepríncipe, M‐501 km 028660Boadilla del Monte, MadridSpain
| | - Lucía Parrilla‐Rubio
- Medical Oncology DepartmentHospital Universitario 12 de OctubreAv de Córdoba s/n28041MadridSpain
| | - Coral Barbas
- Centre for Metabolomic and Bioanalysis (CEMBIO), Facultad de FarmaciaUniversidad San Pablo CEUUrbanización Montepríncipe, M‐501 km 028660Boadilla del Monte, MadridSpain
| | - Carmen Vanesa Díaz‐García
- Clinical & Translational Cancer Research GroupInstituto de Investigación Sanitaria Hospital 12 de Octubre (i+12)Av Córdoba s/n28041MadridSpain
| | - Antonia García
- Centre for Metabolomic and Bioanalysis (CEMBIO), Facultad de FarmaciaUniversidad San Pablo CEUUrbanización Montepríncipe, M‐501 km 028660Boadilla del Monte, MadridSpain
| | - Cristina Pernaut
- Medical Oncology DepartmentHospital Universitario 12 de OctubreAv de Córdoba s/n28041MadridSpain
| | - Jorge Adeva
- Medical Oncology DepartmentHospital Universitario 12 de OctubreAv de Córdoba s/n28041MadridSpain
| | - María Carmen Riesco
- Medical Oncology DepartmentHospital Universitario 12 de OctubreAv de Córdoba s/n28041MadridSpain
| | - Francisco Javier Rupérez
- Centre for Metabolomic and Bioanalysis (CEMBIO), Facultad de FarmaciaUniversidad San Pablo CEUUrbanización Montepríncipe, M‐501 km 028660Boadilla del Monte, MadridSpain
| | - Jose Antonio Lopez‐Martin
- Clinical & Translational Cancer Research GroupInstituto de Investigación Sanitaria Hospital 12 de Octubre (i+12)Av Córdoba s/n28041MadridSpain
- Medical Oncology DepartmentHospital Universitario 12 de OctubreAv de Córdoba s/n28041MadridSpain
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Identification of race-associated metabolite biomarkers for hepatocellular carcinoma in patients with liver cirrhosis and hepatitis C virus infection. PLoS One 2018. [PMID: 29538406 PMCID: PMC5851549 DOI: 10.1371/journal.pone.0192748] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Disparities in hepatocellular carcinoma (HCC) incidence and survival have been observed between ethnic groups including African-Americans (AA) and European-Americans (EA). The evaluation of the changes in the levels of metabolites in samples stratified by race could provide a snapshot of ethnically diverse disease related pathways and identify reliable biomarkers. In this study, we considered AA and EA to investigate metabolites that may be associated with HCC in a race-specific manner. The levels of 46 metabolites in plasma samples, collected from patients recruited at MedStar Georgetown University Hospital, were analyzed by Agilent GC-qMS in selected ion monitoring (SIM) mode. A least absolute shrinkage and selection operator (LASSO) regression model was applied to select metabolites with significant changes in HCC vs. cirrhosis in three groups: (1) AA and EA combined; (2) AA separately; and (3) EA separately. In addition, metabolites that distinguish HCC cases from cirrhosis in these three groups were selected by excluding those without HCV infection. The performances of the metabolites selected by LASSO in each group were evaluated through a leave-one-out cross-validation. We identified race-specific metabolites that differentiated HCC cases from cirrhotic controls, yielding better area under the receiver operating characteristics (ROC) curve (AUC) compared to alpha-fetoprotein (AFP), the serological marker widely used for the diagnosis of HCC. This study sheds light on metabolites that could potentially be used as biomarkers for HCC by monitoring their levels in high-risk population of cirrhotic patients in a race-specific manner.
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35
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Liu Z, Nahon P, Li Z, Yin P, Li Y, Amathieu R, Ganne-Carrié N, Ziol M, Sellier N, Seror O, Le Moyec L, Savarin P, Xu G. Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma. Oncotarget 2018; 9:6245-6258. [PMID: 29464069 PMCID: PMC5814209 DOI: 10.18632/oncotarget.23500] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 10/05/2017] [Indexed: 02/07/2023] Open
Abstract
Hepatitis C virus (HCV) infection is associated with a high risk of developing hepatocellular carcinoma (HCC) and HCC recurrence remains the primary threat to outcomes after curative therapy. In this study, we compared recurrent and non-recurrent HCC patients treated with radiofrequency ablation (RFA) in order to identify characteristic metabolic profile variations associated with HCC recurrence. Gas chromatography-mass spectrometry (GC-MS) -based metabolomic analyses were conducted on serum samples obtained before and after RFA therapy. Significant variations were observed in metabolites in the glycerolipid, tricarboxylic acid (TCA) cycle, fatty acid, and amino acid pathways between recurrent and non-recurrent patients. Observed differences in metabolites associated with recurrence did not coincide before and after treatment except for fatty acids. Based on the comparison of serum metabolomes between recurrent and non-recurrent patients, key discriminatory metabolites were defined by a random forest (RF) test. Two combinations of these metabolites before and after RFA treatment showed outstanding performance in predicting HCV-related HCC recurrence, they were further confirmed by an external validation set. Our study showed that the determined combination of metabolites may be potential biomarkers for the prediction of HCC recurrence before and after RFA treatment.
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Affiliation(s)
- Zhicheng Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.,Université Paris 13, Sorbonne Paris Cité, Laboratoire de Chimie, Structures et Propriétés de Biomateriaux et d'Agents Therapeutiques, UMR 7244, Bobigny, France
| | - Pierre Nahon
- Hepatology Unit, Jean Verdier Teaching Hospital, AP-HP, Bondy, France.,INSERM U1162, Génomique Fonctionnelle des Tumeurs Solides, INSERM U1162, Paris, France.,University Paris 13, Bobigny, France
| | - Zaifang Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Peiyuan Yin
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Yanli Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Roland Amathieu
- Université Paris 13, Sorbonne Paris Cité, Laboratoire de Chimie, Structures et Propriétés de Biomateriaux et d'Agents Therapeutiques, UMR 7244, Bobigny, France.,Intensive Care Unit, Jean Verdier Teaching Hospital, AP-HP, Bondy, France
| | - Nathalie Ganne-Carrié
- Hepatology Unit, Jean Verdier Teaching Hospital, AP-HP, Bondy, France.,University Paris 13, Bobigny, France
| | - Marianne Ziol
- APHP, Service d'Anatomie Pathologique, Hôpital Jean Verdier, BB-0033-00027, Centre de Ressources Biologiques Maladies du foie, Groupe Hospitalier, Paris-Seine-Saint-Denis, France.,BB-0033-00027, Centre de Ressources Biologiques Maladies du Foie, Groupe Hospitalier Paris-Seine-Saint-Denis, Bondy, France
| | - Nicolas Sellier
- APHP, Service de Radiologie, Hôpital Jean Verdier, Bondy, France
| | - Olivier Seror
- INSERM U1162, Génomique Fonctionnelle des Tumeurs Solides, INSERM U1162, Paris, France.,APHP, Service de Radiologie, Hôpital Jean Verdier, Bondy, France
| | | | - Philippe Savarin
- Université Paris 13, Sorbonne Paris Cité, Laboratoire de Chimie, Structures et Propriétés de Biomateriaux et d'Agents Therapeutiques, UMR 7244, Bobigny, France
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
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Li YF, Qiu S, Gao LJ, Zhang AH. Metabolomic estimation of the diagnosis of hepatocellular carcinoma based on ultrahigh performance liquid chromatography coupled with time-of-flight mass spectrometry. RSC Adv 2018; 8:9375-9382. [PMID: 35541871 PMCID: PMC9078651 DOI: 10.1039/c7ra13616a] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 02/23/2018] [Indexed: 01/01/2023] Open
Abstract
Metabolomics has been shown to be an effective tool for biomarker screening and pathway characterization and disease diagnosis. Metabolic characteristics of hepatocellular carcinoma (HCC) may enable the discovery of novel biomarkers for its diagnosis. In this work, metabolomics was used to investigate metabolic alterations of HCC patients. Plasma samples from HCC patients and age-matched healthy controls were investigated using high resolution ultrahigh performance liquid chromatography-mass spectrometry and metabolic differences were analyzed using pattern recognition methods. 23 distinguishable metabolites were identified. The altered metabolic pathways were associated with arginine and proline metabolism, glycine, serine and threonine metabolism, steroid hormone biosynthesis, starch and sucrose metabolism, etc. To demonstrate the utility of plasma biomarkers for the diagnosis of HCC, five metabolites comprising deoxycholic acid 3-glucuronide, 6-hydroxymelatonin glucuronide, 4-methoxycinnamic acid, 11b-hydroxyprogesterone and 4-hydroxyretinoic acid were selected as candidate biomarkers. These metabolites that contributed to the combined model could significantly increase the diagnostic performance of HCC. It has proved to be a powerful tool in the discovery of new biomarkers for disease detection and suggest that panels of metabolites may be valuable to translate our findings to clinically useful diagnostic tests. Metabolomics has been shown to be an effective tool for biomarker screening and pathway characterization and disease diagnosis.![]()
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Affiliation(s)
- Yuan-Feng Li
- First Affiliated Hospital
- School of Pharmacy
- Heilongjiang University of Chinese Medicine
- Harbin 150040
- China
| | - Shi Qiu
- First Affiliated Hospital
- School of Pharmacy
- Heilongjiang University of Chinese Medicine
- Harbin 150040
- China
| | - Li-Juan Gao
- First Affiliated Hospital
- School of Pharmacy
- Heilongjiang University of Chinese Medicine
- Harbin 150040
- China
| | - Ai-Hua Zhang
- First Affiliated Hospital
- School of Pharmacy
- Heilongjiang University of Chinese Medicine
- Harbin 150040
- China
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Abstract
Introduction Cachexia is a common complication of many and varied chronic disease processes, yet it has received very little attention as an area of clinical research effort until recently. We sought to survey the contemporary literature on published research into cachexia to define where it is being published and the proportion of output classified into the main types of research output. Methods I searched the PubMed listings under the topic research term "cachexia" and related terms for articles published in the calendar years of 2015 and 2016, regardless of language. Searches were conducted and relevant papers extracted by two observers, and disagreements were resolved by consensus. Results There were 954 publications, 370 of which were review articles or commentaries, 254 clinical observations or non-randomised trials, 246 original basic science reports and only 26 were randomised controlled trials. These articles were published in 478 separate journals but with 36% of them being published in a core set of 23 journals. The H-index of these papers was 25 and there were 147 papers with 10 or more citations. Of the top 100 cited papers, 25% were published in five journals. Of the top cited papers, 48% were review articles, 18% were original basic science, and 7% were randomised clinical trials. Discussion This analysis shows a steady but modest increase in publications concerning cachexia with a strong pipeline of basic science research but still a relative lack of randomised clinical trials, with none exceeding 1000 patients. Research in cachexia is still in its infancy, but the solid basic science effort offers hope that translation into randomised controlled clinical trials may eventually lead to effective therapies for this troubling and complex clinical disease process.
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38
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Armitage EG, Ciborowski M. Applications of Metabolomics in Cancer Studies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:209-234. [PMID: 28132182 DOI: 10.1007/978-3-319-47656-8_9] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.
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Affiliation(s)
- Emily Grace Armitage
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad CEU San Pablo, Campus Monteprincipe, Madrid, Spain. .,Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, Sir Graeme Davies Building, University of Glasgow, Glasgow, UK. .,Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
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39
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Lubes G, Goodarzi M. GC-MS based metabolomics used for the identification of cancer volatile organic compounds as biomarkers. J Pharm Biomed Anal 2017; 147:313-322. [PMID: 28750734 DOI: 10.1016/j.jpba.2017.07.013] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 07/13/2017] [Accepted: 07/14/2017] [Indexed: 01/27/2023]
Abstract
A biomarker can be a metabolite, coming from a metabolic pathway or cell process, which might be employed in the diagnostic of diseases, predict patient response towards chemical therapies and/or monitor disease recurrences. Biomarkers, e.g. aldehydes or hydrocarbons, are often identified from different body fluids such as blood, urine, serum, saliva or from various tissues samples, and their concentration can vary from one sample to the other. However, the detection and the action of these biomarkers for diseases is a complicated process. Cancer is one of the main cause of death worldwide. The main characteristic of cancerous tumor is the uncontrolled growing of cells inside the organism. Likely, these uncontrolled growths are as consequence changes in the metabolism that could be analytically monitored. Depending on where the cancer cells are located, they provide different characteristics profiles. These profiles as fingerprints are used for differentiation in a comparison to normal cells. This critical study aimed at highlighting the latest progress in this area, especially in the employment of gas chromatography for the monitoring of volatile organic compounds (VOCs) and the identification of possible molecules used as biomarkers for cancer therapy.
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Affiliation(s)
- Giuseppe Lubes
- Laboratorio de Equilibrios en Solución, Universidad Simón Bolívar, Venezuela
| | - Mohammad Goodarzi
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.
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40
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Xue P, Zeng F, Duan Q, Xiao J, Liu L, Yuan P, Fan L, Sun H, Malyarenko OS, Lu H, Xiu R, Liu S, Shao C, Zhang J, Yan W, Wang Z, Zheng J, Zhu F. BCKDK of BCAA Catabolism Cross-talking With the MAPK Pathway Promotes Tumorigenesis of Colorectal Cancer. EBioMedicine 2017; 20:50-60. [PMID: 28501528 PMCID: PMC5478211 DOI: 10.1016/j.ebiom.2017.05.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2017] [Revised: 05/01/2017] [Accepted: 05/01/2017] [Indexed: 01/13/2023] Open
Abstract
Branched-chain amino acids catabolism plays an important role in human cancers. Colorectal cancer is the third most commonly diagnosed cancer in males and the second in females, and the new global incidence is over 1.2 million cases. The branched-chain α-keto acid dehydrogenase kinase (BCKDK) is a rate-limiting enzyme in branched-chain amino acids catabolism, which plays an important role in many serious human diseases. Here we investigated that abnormal branched-chain amino acids catabolism in colorectal cancer is a result of the disease process, with no role in disease initiation; BCKDK is widely expressed in colorectal cancer patients, and those patients that express higher levels of BCKDK have shorter survival times than those with lower levels; BCKDK promotes cell transformation or colorectal cancer ex vivo or in vivo. Mechanistically, BCKDK promotes colorectal cancer by enhancing the MAPK signaling pathway through direct MEK phosphorylation, rather than by branched-chain amino acids catabolism. And the process above could be inhibited by a BCKDK inhibitor, phenyl butyrate.
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Affiliation(s)
- Peipei Xue
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Fanfan Zeng
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Qiuhong Duan
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Juanjuan Xiao
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Lin Liu
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Ping Yuan
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Linni Fan
- Department of Pathology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, PR China
| | - Huimin Sun
- Department of Urology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, PR China
| | - Olesya S Malyarenko
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China; G.B. Elyakov Pacific Institute of Bioorganic Chemistry, Far Eastern Branch of Russian Academy of Sciences, Laboratory of Enzyme Chemistry, Vladivostok, Russia
| | - Hui Lu
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Ruijuan Xiu
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Shaoqing Liu
- Department of State Key Laboratory of Cancer Biology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, PR China
| | - Chen Shao
- Department of Urology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, PR China
| | - Jianmin Zhang
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China
| | - Wei Yan
- Department of Pathology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, PR China.
| | - Zhe Wang
- Department of Pathology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, PR China.
| | - Jianyong Zheng
- Department of State Key Laboratory of Cancer Biology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, PR China.
| | - Feng Zhu
- Department of Biochemistry and Molecular Biology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China.
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41
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Xie J, Zhang A, Wang X. Metabolomic applications in hepatocellular carcinoma: toward the exploration of therapeutics and diagnosis through small molecules. RSC Adv 2017. [DOI: 10.1039/c7ra00698e] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Hepatocellular carcinoma (HCC), a complex public health issue that is the most common primary hepatic malignancy, remains the highest incidence in developing countries and is showing sustained growth across the developed world.
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Affiliation(s)
- Jing Xie
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Chinmedomics Research Center of State Administration of TCM
- Metabolomics Laboratory
- Department of Pharmaceutical Analysis
| | - Aihua Zhang
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Chinmedomics Research Center of State Administration of TCM
- Metabolomics Laboratory
- Department of Pharmaceutical Analysis
| | - Xijun Wang
- Sino-America Chinmedomics Technology Collaboration Center
- National TCM Key Laboratory of Serum Pharmacochemistry
- Chinmedomics Research Center of State Administration of TCM
- Metabolomics Laboratory
- Department of Pharmaceutical Analysis
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42
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Metabolomic Strategies Involving Mass Spectrometry Combined with Liquid and Gas Chromatography. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:77-98. [DOI: 10.1007/978-3-319-47656-8_4] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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43
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Kang H, Li X, Zhou Q, Quan C, Xue F, Zheng J, Yu Y. Exploration of candidate biomarkers for human psoriasis based on gas chromatography-mass spectrometry serum metabolomics. Br J Dermatol 2016; 176:713-722. [PMID: 27564527 DOI: 10.1111/bjd.15008] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/20/2016] [Indexed: 01/03/2023]
Affiliation(s)
- H. Kang
- School of Pharmacy; Fudan University; Shanghai 201203 China
| | - X. Li
- Department of Dermatology; Ruijin Hospital; School of Medicine; Shanghai Jiaotong University; Shanghai 200025 China
| | - Q. Zhou
- School of Pharmacy; Fudan University; Shanghai 201203 China
| | - C. Quan
- Department of Dermatology; Ruijin Hospital; School of Medicine; Shanghai Jiaotong University; Shanghai 200025 China
| | - F. Xue
- Department of Dermatology; Ruijin Hospital; School of Medicine; Shanghai Jiaotong University; Shanghai 200025 China
| | - J. Zheng
- Department of Dermatology; Ruijin Hospital; School of Medicine; Shanghai Jiaotong University; Shanghai 200025 China
| | - Y. Yu
- School of Pharmacy; Fudan University; Shanghai 201203 China
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Di Poto C, Ferrarini A, Zhao Y, Varghese RS, Tu C, Zuo Y, Wang M, Nezami Ranjbar MR, Luo Y, Zhang C, Desai CS, Shetty K, Tadesse MG, Ressom HW. Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery. Cancer Epidemiol Biomarkers Prev 2016; 26:675-683. [PMID: 27913395 DOI: 10.1158/1055-9965.epi-16-0366] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 11/01/2016] [Accepted: 11/14/2016] [Indexed: 12/18/2022] Open
Abstract
Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675-83. ©2016 AACR.
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Affiliation(s)
- Cristina Di Poto
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Alessia Ferrarini
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Yi Zhao
- Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island
| | - Rency S Varghese
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Chao Tu
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Yiming Zuo
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Minkun Wang
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Mohammad R Nezami Ranjbar
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Yue Luo
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Chi Zhang
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC
| | - Chirag S Desai
- MedStar Georgetown University Hospital and Georgetown University Medical Center, Washington, DC
| | - Kirti Shetty
- Division of Gastroenterology & Hepatology, Johns Hopkins University, Sibley Memorial Hospital, Washington, DC
| | - Mahlet G Tadesse
- Department of Mathematics and Statistics, Georgetown University, Washington DC
| | - Habtom W Ressom
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington DC.
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Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma. Sci Rep 2016; 6:37275. [PMID: 27857216 PMCID: PMC5114559 DOI: 10.1038/srep37275] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 10/27/2016] [Indexed: 12/21/2022] Open
Abstract
RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.
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Krautbauer S, Meier EM, Rein-Fischboeck L, Pohl R, Weiss TS, Sigruener A, Aslanidis C, Liebisch G, Buechler C. Ceramide and polyunsaturated phospholipids are strongly reduced in human hepatocellular carcinoma. Biochim Biophys Acta Mol Cell Biol Lipids 2016; 1861:1767-1774. [PMID: 27570113 DOI: 10.1016/j.bbalip.2016.08.014] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 08/16/2016] [Accepted: 08/22/2016] [Indexed: 02/08/2023]
Abstract
Lipid composition affects membrane function, cell proliferation and cell death and is changed in cancer tissues. Hepatocellular carcinoma (HCC) is an aggressive cancer and this study aimed at a comprehensive characterization of hepatic and serum lipids in human HCC. Cholesteryl ester were higher in tumorous tissues (TT) compared to adjacent non-tumorous tissues (NT). Free cholesterol exerting cytotoxic effects was not changed. Phosphatidylethanolamine, -serine (PS) and -inositol but not phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) were reduced in HCC tissues. Saturated species mostly increased and polyunsaturated species were diminished in all of these phospholipids. Ceramide (Cer) was markedly reduced in HCC tissues and higher levels of sphingomyelin suggest impaired sphingomyelinase activity as one of the underlying mechanisms. Importantly, ceramide in NT increased in HCC stage T3. Ceramide released from hepatocytes attracts immune cells and a positive association of the macrophage specific receptor CD163 with NT ceramide was identified. HCC associated lipid changes did not differ in patients suffering from type 2 diabetes. Protein levels of p53 were induced in TT and negatively correlated with Cer d18:1/16:0 and PS 36:1. Of the lipid species changed in HCC tissues only TT Cer d18:1/16:0, Cer d18:1/24:1, PC 38:6 and LPC 22:6 correlated with the respective serum levels. Our study demonstrates a considerably altered hepatic lipidome in HCC tissues. Ceramide was markedly reduced in HCC tissues, and therefore, raising ceramide levels specifically in the tumor represents a reasonable therapeutic approach for the treatment of this malignancy.
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Affiliation(s)
- Sabrina Krautbauer
- Department of Internal Medicine I, Regensburg University Hospital, Regensburg, Germany; Institute of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany
| | - Elisabeth M Meier
- Department of Internal Medicine I, Regensburg University Hospital, Regensburg, Germany
| | - Lisa Rein-Fischboeck
- Department of Internal Medicine I, Regensburg University Hospital, Regensburg, Germany
| | - Rebekka Pohl
- Department of Internal Medicine I, Regensburg University Hospital, Regensburg, Germany
| | - Thomas S Weiss
- University Children Hospital (KUNO), Regensburg University Hospital, Regensburg, Germany
| | - Alexander Sigruener
- Institute of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany
| | - Charalampos Aslanidis
- Institute of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany
| | - Gerhard Liebisch
- Institute of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, Regensburg, Germany
| | - Christa Buechler
- Department of Internal Medicine I, Regensburg University Hospital, Regensburg, Germany.
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47
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Wang M, Tsai TH, Di Poto C, Ferrarini A, Yu G, Ressom HW. Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies. BMC Genomics 2016; 17 Suppl 4:545. [PMID: 27535232 PMCID: PMC5001243 DOI: 10.1186/s12864-016-2796-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. Methods We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. Results The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (<7 %) between estimation and ground truth. By applying the topic model-based purification to mass spectrometric data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. Conclusions We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed that incorporation of scan-level features have the potential to lead to more accurate purification results by alleviating the loss in information as a result of integrating peaks. We believe cancer biomarker discovery studies that use mass spectrometric analysis of human biospecimens can greatly benefit from topic model-based purification of the data prior to statistical and pathway analyses.
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Affiliation(s)
- Minkun Wang
- Department of Oncology, Georgetown University, 4000 Reservoir Rd NW, Washington D.C., USA.,Department of Electrical and Computer Engineering, Virginia Tech, 900 N Glebe Rd, Arlington, VA, USA
| | - Tsung-Heng Tsai
- Department of Oncology, Georgetown University, 4000 Reservoir Rd NW, Washington D.C., USA
| | - Cristina Di Poto
- Department of Oncology, Georgetown University, 4000 Reservoir Rd NW, Washington D.C., USA
| | - Alessia Ferrarini
- Department of Oncology, Georgetown University, 4000 Reservoir Rd NW, Washington D.C., USA
| | - Guoqiang Yu
- Department of Electrical and Computer Engineering, Virginia Tech, 900 N Glebe Rd, Arlington, VA, USA
| | - Habtom W Ressom
- Department of Oncology, Georgetown University, 4000 Reservoir Rd NW, Washington D.C., USA.
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Wang M, Yu G, Ressom HW. Integrative Analysis of Proteomic, Glycomic, and Metabolomic Data for Biomarker Discovery. IEEE J Biomed Health Inform 2016; 20:1225-1231. [PMID: 27249841 DOI: 10.1109/jbhi.2016.2574201] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Studies associating changes in the levels of multiple biomolecules including proteins, glycans, glycoproteins, and metabolites with the onset of cancer have been widely investigated to identify clinically relevant diagnostic biomarkers. Advances in liquid or gas chromatography mass spectrometry (LC-MS, GC-MS) have enabled high-throughput qualitative and quantitative analysis of these biomolecules. While results from separate analyses of different biomolecules have been reported widely, the mutual information obtained by partly or fully combining them has been relatively unexplored. In this study, we investigate integrative analysis of proteins, N-glycans, and metabolites to take advantage of complementary information to improve the ability to distinguish cancer cases from controls. Specifically, support vector machine-recursive feature elimination algorithm is utilized to select a panel of proteins, N-glycans, and metabolites based on LC-MS and GC-MS data previously acquired by the analysis of blood samples from two cohorts in a liver cancer study. Improved performances are observed by integrative analysis compared to separate proteomic, glycomic, and metabolomic studies in distinguishing liver cancer cases from patients with liver cirrhosis.
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Wu Q, Zou M, Yang M, Zhou S, Yan X, Sun B, Wang Y, Chang S, Tang Y, Liang F, Yu S. Revealing Potential Biomarkers of Functional Dyspepsia by Combining 1H NMR Metabonomics Techniques and an Integrative Multi-objective Optimization Method. Sci Rep 2016; 6:18852. [PMID: 26743458 PMCID: PMC4705523 DOI: 10.1038/srep18852] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 11/27/2015] [Indexed: 12/31/2022] Open
Abstract
Metabonomics methods have gradually become important auxiliary tools for screening disease biomarkers. However, recognition of metabolites or potential biomarkers closely related to either particular clinical symptoms or prognosis has been difficult. The current study aims to identify potential biomarkers of functional dyspepsia (FD) by a new strategy that combined hydrogen nuclear magnetic resonance (1H NMR)-based metabonomics techniques and an integrative multi-objective optimization (LPIMO) method. First, clinical symptoms of FD were evaluated using the Nepean Dyspepsia Index (NDI), and plasma metabolic profiles were measured by 1H NMR. Correlations between the key metabolites and the NDI scores were calculated. Then, LPIMO was developed to identify a multi-biomarker panel by maximizing diagnostic ability and correlation with the NDI score. Finally, a KEGG database search elicited the metabolic pathways in which the potential biomarkers are involved. The results showed that glutamine, alanine, proline, HDL, β-glucose, α-glucose and LDL/VLDL levels were significantly altered in FD patients. Among them, phosphatidycholine (PtdCho) and leucine/isoleucine (Leu/Ile) were positively and negatively correlated with the NDI Symptom Index (NDSI) respectively. Our procedure not only significantly improved the credibility of the biomarkers, but also demonstrated the potential of further explorations and applications to diagnosis and treatment of complex disease.
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Affiliation(s)
- Qiaofeng Wu
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
| | - Meng Zou
- National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100080, China
| | - Mingxiao Yang
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
| | - Siyuan Zhou
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
| | - Xianzhong Yan
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Bo Sun
- National Center of Biomedical Analysis, Beijing, 100850, China
| | - Yong Wang
- National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100080, China
| | - Shyang Chang
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 300, Taiwan
| | - Yong Tang
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
| | - Fanrong Liang
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
| | - Shuguang Yu
- Acupuncture and Tuina College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
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Metabolomic analysis of human cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease and non-alcoholic steatohepatitis diseases. GASTROENTEROLOGY AND HEPATOLOGY FROM BED TO BENCH 2016; 9:158-73. [PMID: 27458508 PMCID: PMC4947130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Metabolome analysis is used to evaluate the characteristics and interactions of low molecular weight metabolites under a specific set of conditions. In cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatotic hepatitis (NASH) the liver does not function thoroughly due to long-term damage. Unfortunately the early detection of cirrhosis, HCC, NAFLD and NASH is a clinical problem and determining a sensitive, specific and predictive novel method based on biomarker discovery is an important task. On the other hand, metabolomics has been reported as a new and powerful technology in biomarker discovery and dynamic field that cause global comprehension of system biology. In this review, it has been collected a heterogeneous set of metabolomics published studies to discovery of biomarkers in researches to introduce diagnostic biomarkers for early detection and the choice of patient-specific therapies.
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