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Bathe OF. Tumor metabolism as a factor affecting diversity in cancer cachexia. Am J Physiol Cell Physiol 2025; 328:C908-C920. [PMID: 39870605 DOI: 10.1152/ajpcell.00677.2024] [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: 09/16/2024] [Revised: 09/21/2024] [Accepted: 01/20/2025] [Indexed: 01/29/2025]
Abstract
Cancer cachexia is a multifaceted metabolic syndrome characterized by muscle wasting, fat redistribution, and metabolic dysregulation, commonly associated with advanced cancer but sometimes also evident in early-stage disease. More subtle body composition changes have also been reported in association with cancer, including sarcopenia, myosteatosis, and increased fat radiodensity. Emerging evidence reveals that body composition changes including sarcopenia, myosteatosis, and increased fat radiodensity, arise from distinct biological mechanisms and significantly impact survival outcomes. Importantly, these features often occur independently, with their combined presence exacerbating poor prognoses. Tumor plays a pivotal role in driving these host changes, either by acting as a metabolic parasite or by releasing mediators that disrupt normal tissue function. This review explores the diversity of tumor metabolism. It highlights the potential for tumor-specific metabolic phenotypes to influence systemic effects, including fat redistribution and sarcopenia. Addressing this tumor-host metabolic interplay requires personalized approaches that disrupt tumor metabolism while preserving host health. Promising strategies include targeted pharmacological interventions and anticachexia agents like growth differentiation factor 15 (GDF-15) inhibitors. Nutritional modifications such as ketogenic diets and omega-3 fatty acid supplementation also merit further investigation. In addition to preserving muscle, these therapies will need to be evaluated for their capability to improve survival and quality of life. This review underscores the need for further research into tumor-driven metabolic effects on the host and the development of integrative treatment strategies to address the interconnected challenges of cancer progression and cachexia.
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Affiliation(s)
- Oliver F Bathe
- Department of Surgery and Oncology, University of Calgary, Calgary, Alberta, Canada
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, Alberta, Canada
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Michálková L, Horník Š, Sýkora J, Setnička V, Bunganič B. Prediction of Pathologic Change Development in the Pancreas Associated with Diabetes Mellitus Assessed by NMR Metabolomics. J Proteome Res 2023. [PMID: 37018516 DOI: 10.1021/acs.jproteome.3c00047] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Nuclear magnetic resonance (NMR) metabolomics was used for identification of metabolic changes in pancreatic cancer (PC) blood plasma samples when compared to healthy controls or diabetes mellitus patients. An increased number of PC samples enabled a subdivision of the group according to individual PC stages and the construction of predictive models for finer classification of at-risk individuals recruited from patients with recently diagnosed diabetes mellitus. High-performance values of orthogonal partial least squares (OPLS) discriminant analysis were found for discrimination between individual PC stages and both control groups. The discrimination between early and metastatic stages was achieved with only 71.5% accuracy. A predictive model based on discriminant analyses between individual PC stages and the diabetes mellitus group identified 12 individuals out of 59 as at-risk of development of pathological changes in the pancreas, and four of them were classified as at moderate risk.
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Affiliation(s)
- Lenka Michálková
- Institute of Chemical Process Fundamentals of the CAS, 165 00 Prague 6, Czech Republic
- Department of Analytical Chemistry, University of Chemistry and Technology, Prague, 166 28 Prague 6, Czech Republic
| | - Štěpán Horník
- Institute of Chemical Process Fundamentals of the CAS, 165 00 Prague 6, Czech Republic
| | - Jan Sýkora
- Laboratory of NMR Spectroscopy, University of Chemistry and Technology, Prague, 166 28 Prague 6, Czech Republic
| | - Vladimír Setnička
- Department of Analytical Chemistry, University of Chemistry and Technology, Prague, 166 28 Prague 6, Czech Republic
| | - Bohuš Bunganič
- Department of Internal Medicine, 1st Faculty of Medicine of Charles University and Military University Hospital, 169 02 Prague 6, Czech Republic
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Roth HE, Powers R. Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics. Cancers (Basel) 2022; 14:3992. [PMID: 36010984 PMCID: PMC9406125 DOI: 10.3390/cancers14163992] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 08/09/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022] Open
Abstract
Clinical metabolomics is a rapidly expanding field focused on identifying molecular biomarkers to aid in the efficient diagnosis and treatment of human diseases. Variations in study design, metabolomics methodologies, and investigator protocols raise serious concerns about the accuracy and reproducibility of these potential biomarkers. The explosive growth of the field has led to the recent availability of numerous replicate clinical studies, which permits an evaluation of the consistency of biomarkers identified across multiple metabolomics projects. Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related death and has the lowest five-year survival rate primarily due to the lack of an early diagnosis and the limited treatment options. Accordingly, PDAC has been a popular target of clinical metabolomics studies. We compiled 24 PDAC metabolomics studies from the scientific literature for a detailed meta-analysis. A consistent identification across these multiple studies allowed for the validation of potential clinical biomarkers of PDAC while also highlighting variations in study protocols that may explain poor reproducibility. Our meta-analysis identified 10 metabolites that may serve as PDAC biomarkers and warrant further investigation. However, 87% of the 655 metabolites identified as potential biomarkers were identified in single studies. Differences in cohort size and demographics, p-value choice, fold-change significance, sample type, handling and storage, data collection, and analysis were all factors that likely contributed to this apparently large false positive rate. Our meta-analysis demonstrated the need for consistent experimental design and normalized practices to accurately leverage clinical metabolomics data for reliable and reproducible biomarker discovery.
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Affiliation(s)
- Heidi E. Roth
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, USA
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Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer. Metabolites 2022; 12:metabo12050409. [PMID: 35629913 PMCID: PMC9145477 DOI: 10.3390/metabo12050409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/16/2022] Open
Abstract
Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5−3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.
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Tu W, Feng Y, Lai Q, Wang J, Yuan W, Yang J, Jiang S, Wu A, Cheng S, Shao J, Li J, Jiang Z, Tang H, Shi Y, Zhang S. Metabolic Profiling Implicates a Critical Role of Cyclooxygenase-2-Mediated Arachidonic Acid Metabolism in Radiation-Induced Esophageal Injury in Rats. Radiat Res 2022; 197:480-490. [PMID: 35172004 DOI: 10.1667/rade-20-00240.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/05/2022] [Indexed: 11/03/2022]
Abstract
Radiation-induced esophageal injury (RIEL) is a major dose-limiting complication of radiotherapy, especially for esophageal and thoracic cancers. RIEL is a multi-factorial and multi-step process, which is regulated by a complex network of DNA, RNA, protein and metabolite. However, it is unclear which esophageal metabolites are altered by ionizing radiation and how these changes affect RIEL progression. In this work, we established a rat model of RIEL with 0-40 Gy X-ray irradiation. Esophageal irradiation using ≥25 Gy induced significant changes to rats, such as body weight, food intake, water intake and esophageal structure. The metabolic changes and related pathways of rat esophageal metabolites were investigated by liquid chromatography-mass spectrometry (LC-MS). One hundred eighty metabolites showed an up-regulation in a dose-dependent manner (35 Gy ≥ 25 Gy > controls), and 199 metabolites were downregulated with increasing radiation dose (35 Gy ≤ 25 Gy < controls). The KEGG analysis showed that ionizing radiation seriously disrupted multiple metabolic pathways, and arachidonic acid metabolism was the most significantly enriched pathway. 20 metabolites were dysregulated in arachidonic acid metabolism, including up-regulation of five prostaglandins (PGA2, PGJ2, PGD2, PGH2, and PGI2) in 25 or 35 Gy groups. Cyclooxygenase-2 (COX-2), the key enzyme in catalyzing the biosynthesis of prostaglandins from arachidonic acid, was highly expressed in the esophagus of irradiated rats. Additionally, receiver operating characteristic (ROC) curve analysis revealed that PGJ2 may serve as a promising tissue biomarker for RIEL diagnosis. Taken together, these findings indicate that ionizing radiation induces esophageal metabolic alterations, which advance our understanding of the pathophysiology of RIEL from the perspective of metabolism.
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Affiliation(s)
- Wenling Tu
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China.,School of Bioscience and Technology, Chengdu Medical College, Chengdu, 610500, China
| | - Yahui Feng
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China
| | - Qian Lai
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, 610500, China
| | - Jinlong Wang
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, 610500, China
| | - Weijun Yuan
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, 610500, China
| | - Jingxuan Yang
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, 610500, China
| | - Sheng Jiang
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China
| | - Ailing Wu
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China
| | - Shuanghua Cheng
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China
| | - Jichun Shao
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China
| | - Jingyi Li
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China.,School of Bioscience and Technology, Chengdu Medical College, Chengdu, 610500, China
| | - Zhiqiang Jiang
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China
| | - Hui Tang
- West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Yuhong Shi
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China
| | - Shuyu Zhang
- The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, China.,West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
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6
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Stolzenberg-Solomon RZ, Derkach A, Moore S, Weinstein S, Albanes D, Sampson J. Associations between metabolites and pancreatic cancer risk in a large prospective epidemiological study. Gut 2020; 69:2008-2015. [PMID: 32060129 PMCID: PMC7980697 DOI: 10.1136/gutjnl-2019-319811] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/17/2020] [Accepted: 01/20/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To assess whether prediagnostic metabolites were associated with incident pancreatic ductal adenocarcinoma (PDAC) in a prospective cohort study. DESIGN We conducted an untargeted analysis of 554 known metabolites measured in prediagnostic serum (up to 24 years) to determine their association with incident PDAC in a nested case-control study of male smokers (372 matched case-control sets) and an independent nested case-control study that included women and non-smokers (107 matched sets). Metabolites were measured using Orbitrap Elite or Q-Exactive high-resolution/accurate mass spectrometers. Controls were matched to cases by age, sex, race, date of blood draw, and follow-up time. We used conditional logistic regression adjusted for age to calculate ORs and 95% CIs for a 1 SD increase in log-metabolite level separately in each cohort and combined the two ORs using a fixed-effects meta-analysis. RESULTS Thirty-one metabolites were significantly associated with PDAC at a false discovery rate <0.05 with 12 metabolites below the Bonferroni-corrected threshold (p<9.04×10-5). Similar associations were observed in both cohorts. The dipeptides glycylvaline, aspartylphenylalanine, pyroglutamylglycine, phenylalanylphenylalanine, phenylalanylleucine and tryptophylglutamate and amino acids aspartate and glutamate were positively while the dipeptides tyrosylglutamine and α-glutamyltyrosine, fibrinogen cleavage peptide DSGEGDFXAEGGGVR and glutathione-related amino acid cysteine-glutathione disulfide were inversely associated with PDAC after Bonferroni correction. Five top metabolites demonstrated significant time-varying associations (p<0.023) with the strongest associations observed 10-15 years after participants' blood collection and attenuated thereafter. CONCLUSION Our results suggest that prediagnostic metabolites related to subclinical disease, γ-glutamyl cycle metabolism and adiposity/insulin resistance are associated with PDAC.
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Affiliation(s)
- Rachael Z. Stolzenberg-Solomon
- Metabolic Epidemiology Branch, Division Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States
| | - Andriy Derkach
- Biostatistics Branch, Division Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States
| | - Steven Moore
- Metabolic Epidemiology Branch, Division Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States
| | - Stephanie Weinstein
- Metabolic Epidemiology Branch, Division Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States
| | - Joshua Sampson
- Biostatistics Branch, Division Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States
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7
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Nannini G, Meoni G, Amedei A, Tenori L. Metabolomics profile in gastrointestinal cancers: Update and future perspectives. World J Gastroenterol 2020; 26:2514-2532. [PMID: 32523308 PMCID: PMC7265149 DOI: 10.3748/wjg.v26.i20.2514] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/11/2020] [Accepted: 05/15/2020] [Indexed: 02/06/2023] Open
Abstract
Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.
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Affiliation(s)
- Giulia Nannini
- Department of Experimental and Clinical Medicine, University of Florence, Florence 50134, Italy
| | - Gaia Meoni
- Giotto Biotech Srl, and CERM (University of Florence), Florence 50019, Italy
| | - Amedeo Amedei
- Department of Experimental and Clinical Medicine, University of Florence, Florence 50134, Italy
- SOD of Interdisciplinary Internal Medicine, Azienda Ospedaliera Universitaria Careggi, Florence 50134, Italy
| | - Leonardo Tenori
- Consorzio Interuniversitario Risonanze Magnetiche di Metalloproteine, Florence 50019, Italy
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Kowalczyk T, Ciborowski M, Kisluk J, Kretowski A, Barbas C. Mass spectrometry based proteomics and metabolomics in personalized oncology. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165690. [PMID: 31962175 DOI: 10.1016/j.bbadis.2020.165690] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/18/2019] [Accepted: 01/15/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.
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Affiliation(s)
- Tomasz Kowalczyk
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Michal Ciborowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Joanna Kisluk
- Department of Clinical Molecular Biology, Medical University of Bialystok, Bialystok, Poland
| | - Adam Kretowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland; Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Madrid, Spain.
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Song Z, Wang H, Yin X, Deng P, Jiang W. Application of NMR metabolomics to search for human disease biomarkers in blood. Clin Chem Lab Med 2019; 57:417-441. [PMID: 30169327 DOI: 10.1515/cclm-2018-0380] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Accepted: 07/16/2018] [Indexed: 02/05/2023]
Abstract
Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.
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Affiliation(s)
- Zikuan Song
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Haoyu Wang
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Xiaotong Yin
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Pengchi Deng
- Analytical and Testing Center, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wei Jiang
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China
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Abstract
Metabolomics is a comprehensive characterization of the small polar molecules (metabolites) in different biological systems. One of the analytical platforms commonly used to study metabolic alterations in biofluid samples is proton nuclear magnetic resonance (1H NMR) spectroscopy. NMR spectroscopy is very specific, quantitative, and highly reproducible. Moreover, sample preparation for NMR experiments is very simple and straightforward, and this gives NMR spectroscopy a distinct advantage over other metabolic profiling methods. It has already been shown that 1H NMR-based profiling of biological fluids can be effective in differentiating benign from malignant lesions and in investigating the efficacy of specific cancer treatments. Therefore, 1H NMR spectroscopy may become a promising tool for early noninvasive diagnosis and rapid assessment of treatment effects in cancer patients. Here, we describe a detailed protocol for 1H NMR metabolite profiling in serum, plasma, and urine samples, including sample collection procedures, sample preparation for 1H NMR experiments, spectral acquisition and processing, and quantitative profiling of 1H NMR spectra. We also discuss several aspects of appropriate study design and some multivariate statistical methods that are commonly used to analyze metabolomics datasets.
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11
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Stretch C, Aubin JM, Mickiewicz B, Leugner D, Al-manasra T, Tobola E, Salazar S, Sutherland FR, Ball CG, Dixon E, Vogel HJ, Damaraju S, Baracos VE, Bathe OF. Sarcopenia and myosteatosis are accompanied by distinct biological profiles in patients with pancreatic and periampullary adenocarcinomas. PLoS One 2018; 13:e0196235. [PMID: 29723245 PMCID: PMC5933771 DOI: 10.1371/journal.pone.0196235] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 04/09/2018] [Indexed: 01/06/2023] Open
Abstract
Introduction Pancreatic and periampullary adenocarcinomas are associated with abnormal body composition visible on CT scans, including low muscle mass (sarcopenia) and low muscle radiodensity due to fat infiltration in muscle (myosteatosis). The biological and clinical correlates to these features are poorly understood. Methods Clinical characteristics and outcomes were studied in 123 patients who underwent pancreaticoduodenectomy for pancreatic or non-pancreatic periampullary adenocarcinoma and who had available preoperative CT scans. In a subgroup of patients with pancreatic cancer (n = 29), rectus abdominus muscle mRNA expression was determined by cDNA microarray and in another subgroup (n = 29) 1H-NMR spectroscopy and gas chromatography-mass spectrometry were used to characterize the serum metabolome. Results Muscle mass and radiodensity were not significantly correlated. Distinct groups were identified: sarcopenia (40.7%), myosteatosis (25.2%), both (11.4%). Fat distribution differed in these groups; sarcopenia associated with lower subcutaneous adipose tissue (P<0.0001) and myosteatosis associated with greater visceral adipose tissue (P<0.0001). Sarcopenia, myosteatosis and their combined presence associated with shorter survival, Log Rank P = 0.005, P = 0.06, and P = 0.002, respectively. In muscle, transcriptomic analysis suggested increased inflammation and decreased growth in sarcopenia and disrupted oxidative phosphorylation and lipid accumulation in myosteatosis. In the circulating metabolome, metabolites consistent with muscle catabolism associated with sarcopenia. Metabolites consistent with disordered carbohydrate metabolism were identified in both sarcopenia and myosteatosis. Discussion Muscle phenotypes differ clinically and biologically. Because these muscle phenotypes are linked to poor survival, it will be imperative to delineate their pathophysiologic mechanisms, including whether they are driven by variable tumor biology or host response.
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Affiliation(s)
- Cynthia Stretch
- Department of Oncology, University of Calgary, Calgary, Canada
| | | | - Beata Mickiewicz
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Derek Leugner
- Department of Surgery, University of Calgary, Calgary, Canada
| | - Tariq Al-manasra
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | | | | | | | - Chad G. Ball
- Department of Surgery, University of Calgary, Calgary, Canada
| | - Elijah Dixon
- Department of Surgery, University of Calgary, Calgary, Canada
| | - Hans J. Vogel
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Sambasivario Damaraju
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
- Department of Oncology, University of Alberta, Edmonton, Canada
| | | | - Oliver F. Bathe
- Department of Oncology, University of Calgary, Calgary, Canada
- Department of Surgery, University of Calgary, Calgary, Canada
- * E-mail:
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12
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Pinu FR, Granucci N, Daniell J, Han TL, Carneiro S, Rocha I, Nielsen J, Villas-Boas SG. Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test. Metabolomics 2018; 14:43. [PMID: 30830324 DOI: 10.1007/s11306-018-1339-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 02/07/2018] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies. MATERIALS AND METHODS The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow. CONCLUSIONS Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.
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Affiliation(s)
- Farhana R Pinu
- The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland, 1142, New Zealand.
| | - Ninna Granucci
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - James Daniell
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
- LanzaTech, Skokie, IL, 60077, USA
| | - Ting-Li Han
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Sonia Carneiro
- Center of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
| | - Isabel Rocha
- Center of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivagen 10, 412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2970, Hørsholm, Denmark
| | - Silas G Villas-Boas
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
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Farshidfar F, Kopciuk KA, Hilsden R, McGregor SE, Mazurak VC, Buie WD, MacLean A, Vogel HJ, Bathe OF. A quantitative multimodal metabolomic assay for colorectal cancer. BMC Cancer 2018; 18:26. [PMID: 29301511 PMCID: PMC5755335 DOI: 10.1186/s12885-017-3923-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 12/14/2017] [Indexed: 02/08/2023] Open
Abstract
Background Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation. Methods The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed. Results The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (R2Y = 0.83, Q2Y = 0.75, CV-ANOVA p-value < 0.00001). In this quantitative assay, the coefficient of variance for each metabolite was <10%, and this dramatically enhanced the separation of these groups. Independent validation resulted in AUROC of 0.98 (95% CI, 0.93–1.00) and sensitivity and specificity of 93% and 95%. Similarly, we were able to distinguish adenoma from controls (R2Y = 0.30, Q2Y = 0.20, CV-ANOVA p-value = 0.01; internal AUROC = 0.82 (95% CI, 0.72–0.93)). When combined with the previously generated GC-MS signatures for CRC and adenoma, the candidate biomarker performance improved slightly. Conclusion The diagnostic power for metabolomic tests for colorectal neoplasia can be improved by utilizing a multimodal approach and combining metabolites from diverse chemical classes. In addition, quantification of metabolites enhances separation of disease-specific metabolomic profiles. Our future efforts will be focused on developing a quantitative assay for the metabolites comprising the optimal diagnostic biomarker. Electronic supplementary material The online version of this article (10.1186/s12885-017-3923-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Farshad Farshidfar
- Department of Surgery, University of Calgary, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Karen A Kopciuk
- Department Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.,Population Health Research, Alberta Health Services, Calgary, AB, Canada
| | - Robert Hilsden
- Department of Medicine, University of Calgary, Calgary, AB, Canada.,Forzani & MacPhail Colon Cancer Screening Centre, Calgary, AB, Canada
| | - S Elizabeth McGregor
- Department of Oncology, University of Calgary, Calgary, AB, Canada.,Population Health Research, Alberta Health Services, Calgary, AB, Canada
| | - Vera C Mazurak
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada
| | - W Donald Buie
- Department of Surgery, University of Calgary, Calgary, AB, Canada
| | - Anthony MacLean
- Department of Surgery, University of Calgary, Calgary, AB, Canada
| | - Hans J Vogel
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Oliver F Bathe
- Department of Surgery, University of Calgary, Calgary, AB, Canada. .,Department of Oncology, University of Calgary, Calgary, AB, Canada. .,Division of Surgical Oncology, Tom Baker Cancer Centre, 1331 - 29th St NW, Calgary, AB, T2N 4N2, Canada.
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Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination. Int J Mol Sci 2017; 18:ijms18040767. [PMID: 28375170 PMCID: PMC5412351 DOI: 10.3390/ijms18040767] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Revised: 03/22/2017] [Accepted: 03/27/2017] [Indexed: 12/15/2022] Open
Abstract
This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis−mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.
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