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Stålberg SM, Silwal-Pandit L, Bastani NE, Nebdal DJH, Lingjærde OC, Skålhegg BS, Kure EH. Preoperative profiles of plasma amino acids and derivatives distinguish periampullary cancer and benign disease. BMC Cancer 2024; 24:555. [PMID: 38702616 PMCID: PMC11067218 DOI: 10.1186/s12885-024-12320-8] [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: 12/04/2023] [Accepted: 04/29/2024] [Indexed: 05/06/2024] Open
Abstract
Periampullary cancers, including pancreatic ductal adenocarcinoma, ampullary-, cholangio-, and duodenal carcinoma, are frequently diagnosed in an advanced stage and are associated with poor overall survival. They are difficult to differentiate from each other and challenging to distinguish from benign periampullary disease preoperatively. To improve the preoperative diagnostics of periampullary neoplasms, clinical or biological markers are warranted.In this study, 28 blood plasma amino acids and derivatives from preoperative patients with benign (N = 45) and malignant (N = 72) periampullary disease were analyzed by LC-MS/MS.Principal component analysis and consensus clustering both separated the patients with cancer and the patients with benign disease. Glutamic acid had significantly higher plasma expression and 15 other metabolites significantly lower plasma expression in patients with malignant disease compared with patients having benign disease. Phenylalanine was the only metabolite associated with improved overall survival (HR = 0.50, CI 0.30-0.83, P < 0.01).Taken together, plasma metabolite profiles from patients with malignant and benign periampullary disease were significantly different and have the potential to distinguish malignant from benign disease preoperatively.
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Affiliation(s)
- Stina Margrethe Stålberg
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Department of Natural Sciences and Environmental Health, University of South-Eastern Norway, Bø i Telemark, Norway
- Department of Pathology, Skien Hospital, Vestfold og Telemark, Norway
| | - Laxmi Silwal-Pandit
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Nasser Ezzatkhah Bastani
- Division for Molecular Nutrition, Institute for Basic Medical Sciences, University of Oslo, Oslo, Norway
| | | | - Ole Christian Lingjærde
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Department of Computer Science, University of Oslo, Oslo, Norway
| | - Bjørn Steen Skålhegg
- Division for Molecular Nutrition, Institute for Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Elin Hegland Kure
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.
- Department of Natural Sciences and Environmental Health, University of South-Eastern Norway, Bø i Telemark, Norway.
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2
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Chen J, Lu H, Cao D, Sun J, Qi F, Liu X, Liu J, Yang J, Yu M, Zhou H, Cheng N, Wang J, Zhang Y, Peng P, Wang T, Shen K, Sun W. Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry. Metabolomics 2024; 20:18. [PMID: 38281200 DOI: 10.1007/s11306-023-02085-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/19/2023] [Indexed: 01/30/2024]
Abstract
OBJECTIVE This study aimed to reveal the urinary and serum metabolic pattern of endometrial cancer (EC) and establish diagnostic models to identify EC from controls, high-risk from low-risk EC, and type II from type I EC. METHOD This study included 146 EC patients (comprising 79 low-risk and 67 high-risk patients, including 124 type I and 22 type II) and 59 controls. The serum and urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry. Analysis was used to elucidate the distinct metabolites and altered metabolic pathways. Receiver operating characteristic (ROC) analyses were employed to discover and validate the potential biomarker models. RESULTS Serum and urine metabolomes displayed significant differences between EC and controls, with metabolites related to amino acid and nicotinamide metabolisms. The serum and urine panels distinguished these two groups with Area Under the Curve (AUC) of 0.821 and 0.902, respectively. The panel consisting of serum and urine metabolites demonstrated the best predictive ability (AUC = 0.953 and 0.976 in discovering and validation group). In comparing high-risk and low risk EC, differential metabolites were enriched in purine and glutamine metabolism. The AUC values for serum and urine panels were 0.818, and 0.843, respectively. The combined panel exhibited better predictive accuracy (0.881 in discovering group and 0.936 in external validation). In the comparison between type I and type II group, altered folic acid metabolism was identified. The serum, urine and combined panels discriminated these two groups with the AUC of 0.829, 0.913 and 0.922, respectively. CONCLUSION The combined urine and serum metabolome effectively revealed the metabolic patterns in EC patients, offering valuable diagnostic models for EC diagnosis and classification.
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Affiliation(s)
- Junyu Chen
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Obstetrics and Gynecology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Hezhen Lu
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Dongyan Cao
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
| | - Jiameng Sun
- Core Facility of Instrument, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Feng Qi
- Core Facility of Instrument, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoyan Liu
- Core Facility of Instrument, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jiaqi Liu
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jiaxin Yang
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Mei Yu
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Huimei Zhou
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ninghai Cheng
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jinhui Wang
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ying Zhang
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Peng Peng
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Tao Wang
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Keng Shen
- Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wei Sun
- China-Japan Union Hospital of Jilin University, Changchun, China.
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Chen J, Liu J, Cao D. Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry. BMC Womens Health 2023; 23:583. [PMID: 37940929 PMCID: PMC10634093 DOI: 10.1186/s12905-023-02730-4] [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: 05/16/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVE This study aimed to reveal the urine metabolic change of endometrial cancer (EC) patients during fertility-sparing treatment and establish non-invasive predictive models to identify patients with complete remission (CR). METHOD This study enrolled 20 EC patients prior to treatment (PT) and 22 patients with CR, aged 25-40 years. Eligibility criteria consisted of stage IA high-grade EC, lesions confined to endometrium, normal hepatic and renal function, normal urine test, no contraindication for fertility-sparing treatment and no prior therapy. Urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), a technique chosen for its high sensitivity and resolution, allows for rapid, accurate identification and quantification of metabolites, providing a comprehensive metabolic profile and facilitating the discovery of potential biomarkers. Analytical techniques were employed to determine distinct metabolites and altered metabolic pathways. The statistical analyses were performed using univariate and multivariate analyses, logistic regression and receiver operating characteristic (ROC) curves to discover and validate the potential biomarker models. RESULTS A total of 108 different urine metabolomes were identified between CR and PT groups. These metabolites were enriched in ascorbate and aldarate metabolism, one carbon pool by folate, and some amino acid metabolisms pathways. A panel consisting of Baicalin, 5beta-1,3,7 (11)-Eudesmatrien-8-one, Indolylacryloylglycine, Edulitine, and Physapubenolide were selected as biomarkers, which demonstrated the best predictive ability with the AUC values of 0.982/0.851 in training/10-fold-cross-validation group, achieving a sensitivity of 0.975 and specificity of 0.967, respectively. CONCLUSION The urine metabolic analysis revealed the metabolic changes in EC patients during the fertility-sparing treatment. The predictive biomarkers present great potential diagnostic value in fertility-sparing treatments for EC patients, offering a less invasive means of monitoring treatment efficacy. Further research should explore the mechanistic underpinnings of these metabolic changes and validate the biomarker panel in larger, diverse populations due to the small sample size and single-institution nature of our study.
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Affiliation(s)
- Junyu Chen
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, China
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, National Clinical Research Center for Obstetric & Gynecologic Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Jiale Liu
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Dongyan Cao
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, National Clinical Research Center for Obstetric & Gynecologic Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
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Choi M, Park M, Lee SH, Lee MJ, Paik Y, Jang SI, Lee DK, Lee S, Kang CM. Development of a metabolite calculator for diagnosis of pancreatic cancer. Cancer Med 2023; 12:15933-15944. [PMID: 37350558 PMCID: PMC10469663 DOI: 10.1002/cam4.6233] [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: 10/23/2022] [Revised: 04/22/2023] [Accepted: 06/01/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND Carbohydrate antigen (CA) 19-9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites-based diagnostic calculator for detecting PC with high accuracy. METHODS A targeted quantitative approach of direct flow injection-tandem mass spectrometry combined with liquid chromatography-tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQ™ p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS-DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2-by-2 contingency table of predicted probabilities obtained from the models and actual diagnoses. RESULTS Predictive values obtained through OPLS-DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS-DA. The SPLS-DA-obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively. CONCLUSIONS We successfully developed a 76 metabolome-based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases.
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Affiliation(s)
- Munseok Choi
- Department of Surgery, Yongin Severance HospitalYonsei University College of MedicineYongin‐siSouth Korea
| | - Minsu Park
- Department of Information and StatisticsChungnam National UniversityDaejeonSouth Korea
| | - Sung Hwan Lee
- Department of Surgery, CHA Bundang Medical CenterCHA UniversitySouth Korea
| | - Min Jung Lee
- Yonsei Proteome Research Center and Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, College of Life Science and BiotechnologyYonsei UniversitySeoulSouth Korea
| | - Young‐Ki Paik
- Yonsei Proteome Research Center and Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, College of Life Science and BiotechnologyYonsei UniversitySeoulSouth Korea
| | - Sung Il Jang
- Department of Internal Medicine, Gangnam Severance HospitalYonsei University College of MedicineSeoulSouth Korea
| | - Dong Ki Lee
- Department of Internal Medicine, Gangnam Severance HospitalYonsei University College of MedicineSeoulSouth Korea
| | - Sang‐Guk Lee
- Department of Laboratory Medicine, Severance HospitalYonsei University College of MedicineSeoulSouth Korea
| | - Chang Moo Kang
- Department of Surgery, Severance HospitalYonsei University College of MedicineSeoulSouth Korea
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Peller MT, Das KK. Blood-Based Biomarkers in the Diagnosis and Risk Stratification of Pancreatic Cysts. Gastrointest Endosc Clin N Am 2023; 33:559-581. [PMID: 37245936 DOI: 10.1016/j.giec.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The use of blood-based biomarkers for the assessment of pancreatic cystic lesions is a rapidly growing field with incredible potential. CA 19-9 remains the only blood-based marker in common use, while many novel biomarkers are in early stages of development and validation. We highlight current work in the fields of proteomics, metabolomics, cell-free DNA/circulating tumor DNA, extracellular vesicles, and microRNA among others, as well as barriers to development and future directions in the work of blood-based biomarkers for pancreatic cystic lesions.
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Affiliation(s)
- Matthew T Peller
- Division of Gastroenterology, Washington University School of Medicine, 660 South Euclid Avenue Campus Box 8124, Saint Louis, MO 63110, USA
| | - Koushik K Das
- Division of Gastroenterology, Washington University School of Medicine, 660 South Euclid Avenue Campus Box 8124, Saint Louis, MO 63110, USA.
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Anwardeen NR, Diboun I, Mokrab Y, Althani AA, Elrayess MA. Statistical methods and resources for biomarker discovery using metabolomics. BMC Bioinformatics 2023; 24:250. [PMID: 37322419 DOI: 10.1186/s12859-023-05383-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 06/09/2023] [Indexed: 06/17/2023] Open
Abstract
Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.
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Affiliation(s)
- Najeha R Anwardeen
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Ilhame Diboun
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Younes Mokrab
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
| | - Asma A Althani
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar
- QU Health, Qatar University, Doha, Qatar
| | - Mohamed A Elrayess
- Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.
- QU Health, Qatar University, Doha, Qatar.
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Muranaka H, Hendifar A, Osipov A, Moshayedi N, Placencio-Hickok V, Tatonetti N, Stotland A, Parker S, Van Eyk J, Pandol SJ, Bhowmick NA, Gong J. Plasma Metabolomics Predicts Chemotherapy Response in Advanced Pancreatic Cancer. Cancers (Basel) 2023; 15:3020. [PMID: 37296982 PMCID: PMC10252041 DOI: 10.3390/cancers15113020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 05/26/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Pancreatic cancer (PC) is one of the deadliest cancers. Developing biomarkers for chemotherapeutic response prediction is crucial for improving the dismal prognosis of advanced-PC patients (pts). To evaluate the potential of plasma metabolites as predictors of the response to chemotherapy for PC patients, we analyzed plasma metabolites using high-performance liquid chromatography-mass spectrometry from 31 cachectic, advanced-PC subjects enrolled into the PANCAX-1 (NCT02400398) prospective trial to receive a jejunal tube peptide-based diet for 12 weeks and who were planned for palliative chemotherapy. Overall, there were statistically significant differences in the levels of intermediates of multiple metabolic pathways in pts with a partial response (PR)/stable disease (SD) vs. progressive disease (PD) to chemotherapy. When stratified by the chemotherapy regimen, PD after 5-fluorouracil-based chemotherapy (e.g., FOLFIRINOX) was associated with decreased levels of amino acids (AAs). For gemcitabine-based chemotherapy (e.g., gemcitabine/nab-paclitaxel), PD was associated with increased levels of intermediates of glycolysis, the TCA cycle, nucleoside synthesis, and bile acid metabolism. These results demonstrate the feasibility of plasma metabolomics in a prospective cohort of advanced-PC patients for assessing the effect of enteral feeding as their primary source of nutrition. Metabolic signatures unique to FOLFIRINOX or gemcitabine/nab-paclitaxel may be predictive of a patient's response and warrant further study.
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Affiliation(s)
- Hayato Muranaka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Andrew Hendifar
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Arsen Osipov
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Natalie Moshayedi
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Veronica Placencio-Hickok
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Nicholas Tatonetti
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA;
| | - Aleksandr Stotland
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (A.S.); (S.P.); (J.V.E.)
| | - Sarah Parker
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (A.S.); (S.P.); (J.V.E.)
| | - Jennifer Van Eyk
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (A.S.); (S.P.); (J.V.E.)
| | - Stephen J. Pandol
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Neil A. Bhowmick
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Research, VA Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
| | - Jun Gong
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; (H.M.); (A.H.); (A.O.); (N.M.); (V.P.-H.); (S.J.P.); (N.A.B.)
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
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8
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Pelling M, Chandrapalan S, West E, Arasaradnam RP. A Systematic Review and Meta-Analysis: Volatile Organic Compound Analysis in the Detection of Hepatobiliary and Pancreatic Cancers. Cancers (Basel) 2023; 15:cancers15082308. [PMID: 37190235 DOI: 10.3390/cancers15082308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Hepatobiliary cancers are notoriously difficult to detect, frequently leading to diagnosis in later stages of disease when curative treatment is not an option. The currently used biomarkers such as AFP (alpha-fetoprotein) and CA19.9 lack sensitivity and specificity. Hence, there is an unmet need for an alternative biomarker. AIM To evaluate the diagnostic accuracy of volatile organic compounds (VOCs) for the detection of hepatobiliary and pancreatic cancers. METHODS A systematic review of VOCs' use in the detection of hepatobiliary and pancreatic cancers was performed. A meta-analysis was performed using the software R. Heterogeneity was explored through meta-regression analysis. RESULTS A total of 18 studies looking at 2296 patients were evaluated. Pooled sensitivity and specificity of VOCs for the detection of hepatobiliary and pancreatic cancer were 0.79 (95% CI, 0.72-0.85) and 0.81 (97.5% CI, 0.76-0.85), respectively. The area under the curve was 0.86. Meta-regression analysis showed that the sample media used contributed to heterogeneity. Bile-based VOCs showed the highest precision values, although urine and breath are preferred for their feasibility. CONCLUSIONS Volatile organic compounds have the potential to be used as an adjunct tool to aid in the early diagnosis of hepatobiliary cancers.
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Affiliation(s)
- Melina Pelling
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | | | - Emily West
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Ramesh P Arasaradnam
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
- Department of Gastroenterology, University Hospital of Coventry and Warwickshire, Coventry CV2 2DX, UK
- Health, Biological & Experimental Sciences, University of Coventry, Coventry CV1 5FB, UK
- School of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
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9
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Zhao R, Ren S, Li C, Guo K, Lu Z, Tian L, He J, Zhang K, Cao Y, Liu S, Li D, Wang Z. Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study. Cancer Med 2023; 12:5158-5171. [PMID: 36161527 PMCID: PMC9972159 DOI: 10.1002/cam4.5296] [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: 05/25/2022] [Revised: 08/10/2022] [Accepted: 09/15/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis. METHODS We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis. RESULTS Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34-3.53]). The three markers showed area under the receiver-operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19-9 (CA19-9) was added to the model. CONCLUSION The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19-9.
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Affiliation(s)
- Rui Zhao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Changyin Li
- Department of Clinical Pharmacology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Kai Guo
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zipeng Lu
- Pancreas Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Lei Tian
- Pancreas Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Jian He
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Kai Zhang
- Pancreas Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Yingying Cao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shijia Liu
- Department of Pharmacy, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhongqiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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10
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Anesti O, Papaioannou N, Gabriel C, Karakoltzidis A, Dzhedzheia V, Petridis I, Stratidakis A, Dickinson M, Horvat M, Snoj Tratnik J, Tsatsakis A, Karakitsios S, Sarigiannis DA. An exposome connectivity paradigm for the mechanistic assessment of the effects of prenatal and early life exposure to metals on neurodevelopment. Front Public Health 2023; 10:871218. [PMID: 36699871 PMCID: PMC9869756 DOI: 10.3389/fpubh.2022.871218] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 09/28/2022] [Indexed: 01/12/2023] Open
Abstract
The exposome paradigm through an integrated approach to investigating the impact of perinatal exposure to metals on child neurodevelopment in two cohorts carried out in Slovenia (PHIME cohort) and Greece (HERACLES cohort) respectively, is presented herein. Heavy metals are well-known neurotoxicants with well-established links to impaired neurodevelopment. The links between in utero and early-life exposure to metals, metabolic pathway dysregulation, and neurodevelopmental disorders were drawn through urinary and plasma untargeted metabolomics analysis, followed by the combined application of in silico and biostatistical methods. Heavy metal prenatal and postnatal exposure was evaluated, including parameters indirectly related to exposure and health adversities, such as sociodemographic and anthropometric parameters and dietary factors. The primary outcome of the study was that the identified perturbations related to the TCA cycle are mainly associated with impaired mitochondrial respiration, which is detrimental to cellular homeostasis and functionality; this is further potentiated by the capacity of heavy metals to induce oxidative stress. Insufficient production of energy from the mitochondria during the perinatal period is associated with developmental disorders in children. The HERACLES cohort included more detailed data regarding diet and sociodemographic status of the studied population, allowing the identification of a broader spectrum of effect modifiers, such as the beneficial role of a diet rich in antioxidants such as lycopene and ω-3 fatty acids, the negative effect the consumption of food items such as pork and chicken meat has or the multiple impacts of fish consumption. Beyond diet, several other factors have been proven influential for child neurodevelopment, such as the proximity to pollution sources (e.g., waste treatment site) and the broader living environment, including socioeconomic and demographic characteristics. Overall, our results demonstrate the utility of exposome-wide association studies (EWAS) toward understanding the relationships among the multiple factors that determine human exposure and the underlying biology, reflected as omics markers of effect on neurodevelopment during childhood.
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Affiliation(s)
- Ourania Anesti
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Centre of Toxicology Science and Research, School of Medicine, University of Crete, Heraklion, Greece
| | - Nafsika Papaioannou
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Catherine Gabriel
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Achilleas Karakoltzidis
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vazha Dzhedzheia
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioannis Petridis
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Antonios Stratidakis
- Science, Technology, and Society Department, Istituto Universitario di Studi Superiori (IUSS), University School for Advanced Study, Pavia, Italy
| | | | - Milena Horvat
- Department of Environmental Sciences, Josef Stefan Institute, Ljubljana, Slovenia
| | - Janja Snoj Tratnik
- Department of Environmental Sciences, Josef Stefan Institute, Ljubljana, Slovenia
| | - Aristidis Tsatsakis
- Centre of Toxicology Science and Research, School of Medicine, University of Crete, Heraklion, Greece
| | - Spyros Karakitsios
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimosthenis A. Sarigiannis
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, Thessaloniki, Greece,Centre of Toxicology Science and Research, School of Medicine, University of Crete, Heraklion, Greece,Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece,*Correspondence: Dimosthenis A. Sarigiannis
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11
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Cao YY, Guo K, Zhao R, Li Y, Lv XJ, Lu ZP, Tian L, Ren S, Wang ZQ. Untargeted metabolomics characterization of the resectable pancreatic ductal adenocarcinoma. Digit Health 2023; 9:20552076231179007. [PMID: 37312938 PMCID: PMC10259126 DOI: 10.1177/20552076231179007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/12/2023] [Indexed: 06/15/2023] Open
Abstract
Background Diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to the lack of specific symptoms and screening methods. Only less than 10% of PDAC patients are candidates for surgery at the time of diagnosis. Thus, there is a great global unmet need for valuable biomarkers that could improve the opportunity to detect PDAC at the resectable stage. This study aimed to develop a potential biomarker model for the detection of resectable PDAC by tissue and serum metabolomics. Methods Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was performed for metabolome quantification in 98 serum samples (49 PDAC patients and 49 healthy controls (HCs)) and 20 pairs of matched pancreatic cancer tissues (PCTs) and adjacent noncancerous tissues (ANTs) from PDAC patients. Univariate and multivariate analyses were used to profile the differential metabolites between PDAC and HC. Results A total of 12 differential metabolites were present in both serum and tissue samples of PDAC. Among them, a total of eight differential metabolites showed the same expressional levels, including four upregulated and four downregulated metabolites. Finally, a panel of three metabolites including 16-hydroxypalmitic acid, phenylalanine, and norleucine was constructed by logistic regression analysis. Notably, the panel was capable of distinguishing resectable PDAC from HC with an AUC value of 0.942. Additionally, a multimarker model based on the 3-metabolites-based panel and CA19-9 showed a better performance than the metabolites panel or CA19-9 alone (AUC: 0.968 vs. 0.942, 0.850). Conclusions Taken together, the resectable early-stage PDAC has unique metabolic features in serum and tissue samples. The defined panel of three metabolites has the potential value for early screening of PDAC at the resectable stage.
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Affiliation(s)
- Ying-Ying Cao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Kai Guo
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Rui Zhao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yuan Li
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiao-Jing Lv
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zi-Peng Lu
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Tian
- Pancreas Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhong-Qiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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12
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Yu Z, Matsukawa N, Saigusa D, Motoike IN, Ono C, Okamura Y, Onuma T, Takahashi Y, Sakai M, Kudo H, Obara T, Murakami K, Shirota M, Kikuchi S, Kobayashi N, Kikuchi Y, Sugawara J, Minegishi N, Ogishima S, Kinoshita K, Yamamoto M, Yaegashi N, Kuriyama S, Koshiba S, Tomita H. Plasma metabolic disturbances during pregnancy and postpartum in women with depression. iScience 2022; 25:105666. [PMID: 36505921 PMCID: PMC9732390 DOI: 10.1016/j.isci.2022.105666] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/17/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Examining plasma metabolic profiling during pregnancy and postpartum could help clinicians understand the risk factors for postpartum depression (PPD) development. This analysis targeted paired plasma metabolites in mid-late gestational and 1 month postpartum periods in women with (n = 209) or without (n = 222) PPD. Gas chromatogram-mass spectrometry was used to analyze plasma metabolites at these two time points. Among the 170 objected plasma metabolites, principal component analysis distinguished pregnancy and postpartum metabolites but failed to discriminate women with and without PPD. Compared to women without PPD, those with PPD exhibited 37 metabolites with disparate changes during pregnancy and the 1-month postpartum period and an enriched citrate cycle. Machine learning and multivariate statistical analysis identified two or three compounds that could be potential biomarkers for PPD prediction during pregnancy. Our findings suggest metabolic disturbances in women with depression and may help to elucidate metabolic processes associated with PPD development.
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Affiliation(s)
- Zhiqian Yu
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan,Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Corresponding author
| | - Naomi Matsukawa
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Daisuke Saigusa
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Laboratory of Biomedical and Analytical Sciences, Faculty of Pharma-Science, Teikyo University
| | - Ikuko N. Motoike
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Department of System Bioinformatics, Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Chiaki Ono
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yasunobu Okamura
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Innovations in Next-Generation Medicine, Advanced Research Center, Tohoku University, Sendai, Japan
| | - Tomomi Onuma
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Yuta Takahashi
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Mai Sakai
- Department of Disaster Psychiatry, International Research Institute for Disaster Science, Tohoku University, Sendai, Japan
| | - Hisaaki Kudo
- Department of Biobank Life Science, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Taku Obara
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Keiko Murakami
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Matusyuki Shirota
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Saya Kikuchi
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Natsuko Kobayashi
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yoshie Kikuchi
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Junichi Sugawara
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Naoko Minegishi
- Department of Biobank Life Science, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Soichi Ogishima
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kengo Kinoshita
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Department of System Bioinformatics, Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Nobuo Yaegashi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Department of Gynecology and Obstetrics, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Division of Disaster Public Health, International Research Institute for Disaster Science, Tohoku University, Sendai, Japan
| | - Seizo Koshiba
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan,Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan,Department of Disaster Psychiatry, International Research Institute for Disaster Science, Tohoku University, Sendai, Japan
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13
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Guo P, Teng T, Liu W, Fang Y, Wei B, Feng J, Huang H. Metabolomic analyses redefine the biological classification of pancreatic cancer and correlate with clinical outcomes. Int J Cancer 2022; 151:1835-1846. [PMID: 35830200 DOI: 10.1002/ijc.34208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 11/10/2022]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is characterized by high heterogeneity, and the postoperative prognosis of different patients often varies greatly. Therefore, the classification of pancreatic cancer patients and precise treatment becomes particularly important. In our study, 1 H NMR spectroscopy was used to analyze the 76 PDAC serum samples and identify the potential metabolic subtypes. The metabolic characteristics of each metabolic subtype were screened out and the relationship between metabolic subtype and the long-term prognosis was further identified. The clinical stages of PDAC did not show the metabolic differences at the serum metabolomic level. And three metabolic subtypes, basic, choline-like and amino acid-enriched types, were defined by the hierarchical cluster analysis of the serum metabolites and the disturbed metabolic pathways. The characteristic metabolites of each PDAC subtype were identified, and the metabolite model was established to distinguish the PDAC patients in the different subtypes. Among the three metabolic subtypes, choline-like type displayed better long-term prognosis compared to the other two types of patients. Metabolic subtypes are of clinical importance and are closer to expressing the heterogeneity in the actual life activities of pancreatic cancer than molecular typing. The excavation of metabolic subtypes based on this will be more in line with clinical reality and more promising to guide clinical precision individualization treatment.
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Affiliation(s)
- Pengfei Guo
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Tianhong Teng
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wuping Liu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Yanying Fang
- Fuzhou Children Hospital of Fujian Province, Fuzhou, China
| | - Binbin Wei
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Jianghua Feng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Heguang Huang
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
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14
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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: 9] [Impact Index Per Article: 4.5] [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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15
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Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid. Metabolites 2022; 12:metabo12040277. [PMID: 35448464 PMCID: PMC9029812 DOI: 10.3390/metabo12040277] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/08/2022] [Accepted: 03/17/2022] [Indexed: 02/04/2023] Open
Abstract
In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.
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16
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1H-NMR-based metabolomics of skin squamous cell carcinoma and peri-tumoral region tissues. J Pharm Biomed Anal 2022; 212:114643. [DOI: 10.1016/j.jpba.2022.114643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/29/2022] [Accepted: 02/01/2022] [Indexed: 11/21/2022]
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17
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Yan TB, Huang JQ, Huang SY, Ahir BK, Li LM, Mo ZN, Zhong JH. Advances in the Detection of Pancreatic Cancer Through Liquid Biopsy. Front Oncol 2021; 11:801173. [PMID: 34993149 PMCID: PMC8726483 DOI: 10.3389/fonc.2021.801173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/06/2021] [Indexed: 01/27/2023] Open
Abstract
Pancreatic cancer refers to the development of malignant tumors in the pancreas: it is associated with high mortality rates and mostly goes undetected in its early stages for lack of symptoms. Currently, surgical treatment is the only effective way to improve the survival of pancreatic cancer patients. Therefore, it is crucial to diagnose the disease as early as possible in order to improve the survival rate of patients with pancreatic cancer. Liquid biopsy is a unique in vitro diagnostic technique offering the advantage of earlier detection of tumors. Although liquid biopsies have shown promise for screening for certain cancers, whether they are effective for early diagnosis of pancreatic cancer is unclear. Therefore, we reviewed relevant literature indexed in PubMed and collated updates and information on advances in the field of liquid biopsy with respect to the early diagnosis of pancreatic cancer.
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Affiliation(s)
- Tian-Bao Yan
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Jia-Qi Huang
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Shi-Yun Huang
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Bhavesh K. Ahir
- Section of Hematology and Oncology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Long-Man Li
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Zeng-Nan Mo
- Center for Genomics and Personalized Medicine, Guangxi Key Laboratory for Genomics and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomics and Personalized Medicine, Guangxi Medical University, Nanning, China
| | - Jian-Hong Zhong
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- *Correspondence: Jian-Hong Zhong,
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18
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Lin JY, Juo BR, Yeh YH, Fu SH, Chen YT, Chen CL, Wu KP. Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics. BMC Bioinformatics 2021; 22:305. [PMID: 34090341 PMCID: PMC8180080 DOI: 10.1186/s12859-021-04235-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/04/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks. RESULTS We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism. CONCLUSIONS In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.
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Affiliation(s)
- Jia-You Lin
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Bao-Rong Juo
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, 33302, Taiwan
| | - Yu-Hsuan Yeh
- Department of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Shu-Hsuan Fu
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, 33302, Taiwan
| | - Yi-Ting Chen
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, 33302, Taiwan
- Department of Biomedical Sciences, Chang Gung University, Taoyuan, 33302, Taiwan
| | - Chien-Lun Chen
- Department of Urology, Chang Gung Memorial Hospital, Taoyuan, 33305, Taiwan.
- College of Medicine, Chang Gung University, Taoyuan, 33302, Taiwan.
| | - Kun-Pin Wu
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan.
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19
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Sarigiannis DA, Papaioannou N, Handakas E, Anesti O, Polanska K, Hanke W, Salifoglou A, Gabriel C, Karakitsios S. Neurodevelopmental exposome: The effect of in utero co-exposure to heavy metals and phthalates on child neurodevelopment. ENVIRONMENTAL RESEARCH 2021; 197:110949. [PMID: 33716031 DOI: 10.1016/j.envres.2021.110949] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 12/27/2020] [Accepted: 02/25/2021] [Indexed: 05/22/2023]
Abstract
In this study, the exposome paradigm has been applied on a mother-child cohort adopting an optimised untargeted metabolomics approach for human urine followed by advanced bioinformatics analysis. Exposome-wide association algorithms were used to draw links between in utero co-exposure to metals and phthalates, metabolic pathways deregulation, and clinically observed phenotypes of neurodevelopmental disorders such as problems in linguistic, motor development and cognitive capacity. Children (n = 148) were tested at the first and second year of their life using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Their mothers had been exposed to metals and phthalates during the pregnancy, according to human biomonitoring results from previously performed studies. Untargeted metabolomics analysis of biobanked urine samples from the mothers was performed using a combination of the high throughput analytical methods liquid chromatography-high resolution mass spectrometry (LC-HRMS) and nuclear magnetic resonance (NMR). Most perturbed metabolic pathways from co-exposure heavy metals and phthalates were pathways related to the tricarboxylic acid cycle (TCA cycle) and oxidative phosphorylation, indicating the possibility of disruption of mitochondrial respiration. Overproduction of reactive oxygen species (ROS); the presence of glutathione peroxidase 3 (GPx3) during pregnancy and presence of glutathione peroxidase 1 (GPx1) in the umbilical cord were linked to verbal development problems. Another finding of the study is that in real life, adverse outcomes occur as a combination of environmental and social factors, all of them acting synergistically towards the deployment of an observed phenotype. Finally, the two-steps association process (exposure to pathways and pathways to adverse outcomes) was able to (a) provide associations that are not evident by directly associating exposure to outcomes and (b) provides additional insides on the mechanisms of environmental disease.
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Affiliation(s)
- Denis A Sarigiannis
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10thkm Thessaloniki-Thermi Road, 57001, Greece; School for Advanced Study (IUSS), Science, Technology and Society Department, Environmental Health Engineering, Piazza Della Vittoria 15, Pavia, 27100, Italy.
| | - Nafsika Papaioannou
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10thkm Thessaloniki-Thermi Road, 57001, Greece
| | - Evangelos Handakas
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece
| | - Ourania Anesti
- HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10thkm Thessaloniki-Thermi Road, 57001, Greece; School of Medicine, University of Crete, Voutes, Heraklion, 71003, Greece
| | - Kinga Polanska
- Nofer Institute of Occupational Medicine, 91348, Lodz, Poland
| | - Woijcek Hanke
- Nofer Institute of Occupational Medicine, 91348, Lodz, Poland
| | - Athanasios Salifoglou
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Inorganic Chemistry Laboratory, University Campus, Thessaloniki, 54124, Greece
| | - Catherine Gabriel
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10thkm Thessaloniki-Thermi Road, 57001, Greece
| | - Spyros Karakitsios
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki, 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10thkm Thessaloniki-Thermi Road, 57001, Greece
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20
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Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review. Cancers (Basel) 2021; 13:cancers13112722. [PMID: 34072842 PMCID: PMC8198035 DOI: 10.3390/cancers13112722] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/20/2021] [Accepted: 05/27/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC), which represents approximately 90% of all pancreatic cancers, is an extremely aggressive and lethal disease. It is considered a silent killer due to a largely asymptomatic course and late clinical presentation. Earlier detection of the disease would likely have a great impact on changing the currently poor survival figures for this malignancy. In this comprehensive review, we assessed over 4000 reports on non-invasive PDAC biomarkers in the last decade. Applying the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, we selected and reviewed in more detail 49 relevant studies reporting on the most promising candidate biomarkers. In addition, we also highlight the present challenges and complexities of translating novel biomarkers into clinical use. Abstract Pancreatic ductal adenocarcinoma (PDAC) carries a deadly diagnosis, due in large part to delayed presentation when the disease is already at an advanced stage. CA19-9 is currently the most commonly utilized biomarker for PDAC; however, it lacks the necessary accuracy to detect precursor lesions or stage I PDAC. Novel biomarkers that could detect this malignancy with improved sensitivity (SN) and specificity (SP) would likely result in more curative resections and more effective therapeutic interventions, changing thus the present dismal survival figures. The aim of this study was to systematically and comprehensively review the scientific literature on non-invasive biomarkers in biofluids such as blood, urine and saliva that were attempting earlier PDAC detection. The search performed covered a period of 10 years (January 2010—August 2020). Data were extracted using keywords search in the three databases: MEDLINE, Web of Science and Embase. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied for study selection based on establishing the risk of bias and applicability concerns in Patient Selection, Index test (biomarker assay) and Reference Standard (standard-of-care diagnostic test). Out of initially over 4000 published reports, 49 relevant studies were selected and reviewed in more detail. In addition, we discuss the present challenges and complexities in the path of translating the discovered biomarkers into the clinical setting. Our systematic review highlighted several promising biomarkers that could, either alone or in combination with CA19-9, potentially improve earlier detection of PDAC. Overall, reviewed biomarker studies should aim to improve methodological and reporting quality, and novel candidate biomarkers should be investigated further in order to demonstrate their clinical usefulness. However, challenges and complexities in the path of translating the discovered biomarkers from the research laboratory to the clinical setting remain and would have to be addressed before a more realistic breakthrough in earlier detection of PDAC is achieved.
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21
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Al-Shaheri FN, Alhamdani MSS, Bauer AS, Giese N, Büchler MW, Hackert T, Hoheisel JD. Blood biomarkers for differential diagnosis and early detection of pancreatic cancer. Cancer Treat Rev 2021; 96:102193. [PMID: 33865174 DOI: 10.1016/j.ctrv.2021.102193] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/17/2021] [Accepted: 03/19/2021] [Indexed: 12/12/2022]
Abstract
Pancreatic cancer is currently the most lethal tumor entity and case numbers are rising. It will soon be the second most frequent cause of cancer-related death in the Western world. Mortality is close to incidence and patient survival after diagnosis stands at about five months. Blood-based diagnostics could be one crucial factor for improving this dismal situation and is at a stage that could make this possible. Here, we are reviewing the current state of affairs with its problems and promises, looking at various molecule types. Reported results are evaluated in the overall context. Also, we are proposing steps toward clinical utility that should advance the development toward clinical application by improving biomarker quality but also by defining distinct clinical objectives and the respective diagnostic accuracies required to achieve them. Many of the discussed points and conclusions are highly relevant to other solid tumors, too.
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Affiliation(s)
- Fawaz N Al-Shaheri
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany.
| | - Mohamed S S Alhamdani
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
| | - Andrea S Bauer
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
| | - Nathalia Giese
- Department of General Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
| | - Markus W Büchler
- Department of General Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
| | - Thilo Hackert
- Department of General Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
| | - Jörg D Hoheisel
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
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22
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Michálková L, Horník Š, Sýkora J, Habartová L, Setnička V, Bunganič B. Early Detection of Pancreatic Cancer in Type 2 Diabetes Mellitus Patients Based on 1H NMR Metabolomics. J Proteome Res 2021; 20:1744-1753. [PMID: 33617266 DOI: 10.1021/acs.jproteome.0c00990] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The association of pancreatic cancer with type 2 diabetes mellitus was investigated by 1H NMR metabolomic analysis of blood plasma. Concentration data of 58 metabolites enabled discrimination of pancreatic cancer (PC) patients from healthy controls (HC) and long-term type 2 diabetes mellitus (T2DM) patients. A panel of eight metabolites was proposed and successfully tested for group discrimination. Furthermore, a prediction model for the identification of at-risk individuals for future development of pancreatic cancer was built and tested on recent-onset diabetes mellitus (RODM) patients. Six of 59 RODM samples were assessed as PC with an accuracy of more than 80%. The health condition of these individuals was re-examined, and in four cases, a correlation to the prediction was found. The current health condition can be retrospectively attributed to misdiagnosed pancreatogenic diabetes or to early-stage pancreatic cancer.
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Affiliation(s)
- Lenka Michálková
- Department of Analytical Chemistry, Institute of Chemical Process Fundamentals of the CAS, Prague 6 16502, Czech Republic.,Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Štěpán Horník
- Department of Analytical Chemistry, Institute of Chemical Process Fundamentals of the CAS, Prague 6 16502, Czech Republic.,Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Jan Sýkora
- Department of Analytical Chemistry, Institute of Chemical Process Fundamentals of the CAS, Prague 6 16502, Czech Republic
| | - Lucie Habartová
- Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Vladimír Setnička
- Department of Analytical Chemistry, University of Chemistry and Technology Prague, Prague 6 16628, Czech Republic
| | - Bohuš Bunganič
- Department of Internal Medicine, 1st Faculty of Medicine of Charles University and Military University Hospital, Prague 6 16902, Czech Republic
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23
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Salvatore M, Beesley LJ, Fritsche LG, Hanauer D, Shi X, Mondul AM, Pearce CL, Mukherjee B. Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks. J Biomed Inform 2021; 113:103652. [PMID: 33279681 PMCID: PMC7855433 DOI: 10.1016/j.jbi.2020.103652] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/27/2020] [Accepted: 11/30/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND Traditional methods for disease risk prediction and assessment, such as diagnostic tests using serum, urine, blood, saliva or imaging biomarkers, have been important for identifying high-risk individuals for many diseases, leading to early detection and improved survival. For pancreatic cancer, traditional methods for screening have been largely unsuccessful in identifying high-risk individuals in advance of disease progression leading to high mortality and poor survival. Electronic health records (EHR) linked to genetic profiles provide an opportunity to integrate multiple sources of patient information for risk prediction and stratification. We leverage a constellation of temporally associated diagnoses available in the EHR to construct a summary risk score, called a phenotype risk score (PheRS), for identifying individuals at high-risk for having pancreatic cancer. The proposed PheRS approach incorporates the time with respect to disease onset into the prediction framework. We combine and contrast the PheRS with more well-known measures of inherited susceptibility, namely, the polygenic risk scores (PRS) for prediction of pancreatic cancer. METHODOLOGY We first calculated pairwise, unadjusted associations between pancreatic cancer diagnosis and all possible other diagnoses across the medical phenome. We call these pairwise associations co-occurrences. After accounting for cross-phenotype correlations, the multivariable association estimates from a subset of relatively independent diagnoses were used to create a weighted sum PheRS. We constructed time-restricted risk scores using data from 38,359 participants in the Michigan Genomics Initiative (MGI) based on the diagnoses contained in the EHR at 0, 1, 2, and 5 years prior to the target pancreatic cancer diagnosis. The PheRS was assessed for predictability in the UK Biobank (UKB). We tested the relative contribution of PheRS when added to a model containing a summary measure of inherited genetic susceptibility (PRS) plus other covariates like age, sex, smoking status, drinking status, and body mass index (BMI). RESULTS Our exploration of co-occurrence patterns identified expected associations while also revealing unexpected relationships that may warrant closer attention. Solely using the pancreatic cancer PheRS at 5 years before the target diagnoses yielded an AUC of 0.60 (95% CI = [0.58, 0.62]) in UKB. A larger predictive model including PheRS, PRS, and the covariates at the 5-year threshold achieved an AUC of 0.74 (95% CI = [0.72, 0.76]) in UKB. We note that PheRS does contribute independently in the joint model. Finally, scores at the top percentiles of the PheRS distribution demonstrated promise in terms of risk stratification. Scores in the top 2% were 10.20 (95% CI = [9.34, 12.99]) times more likely to identify cases than those in the bottom 98% in UKB at the 5-year threshold prior to pancreatic cancer diagnosis. CONCLUSIONS We developed a framework for creating a time-restricted PheRS from EHR data for pancreatic cancer using the rich information content of a medical phenome. In addition to identifying hypothesis-generating associations for future research, this PheRS demonstrates a potentially important contribution in identifying high-risk individuals, even after adjusting for PRS for pancreatic cancer and other traditional epidemiologic covariates. The methods are generalizable to other phenotypic traits.
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Affiliation(s)
- Maxwell Salvatore
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Lauren J Beesley
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Lars G Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States; Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, MI 48109, United States; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - David Hanauer
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, United States
| | - Xu Shi
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, United States.
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Xu H, Zhang L, Kang H, Liu J, Zhang J, Zhao J, Liu S. Metabolomics Identifies Biomarker Signatures to Differentiate Pancreatic Cancer from Type 2 Diabetes Mellitus in Early Diagnosis. Int J Endocrinol 2021; 2021:9990768. [PMID: 34868309 PMCID: PMC8639267 DOI: 10.1155/2021/9990768] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 10/07/2021] [Accepted: 11/02/2021] [Indexed: 12/26/2022] Open
Abstract
METHODS Plasma metabolic profiles in 26 PC patients, 27 DM patients, and 23 healthy volunteers were examined using an ultraperformance liquid chromatography coupled with tandem mass spectrometry platform. Differential metabolite ions were then identified using the principal component analysis (PCA) model and the orthogonal partial least-squares discrimination analysis (OPLS-DA) model. The diagnosis performance of metabolite biomarkers was validated by logistic regression models. RESULTS We established a PCA model (R2X = 23.5%, Q2 = 8.21%) and an OPLS-DA model (R2X = 70.0%, R2Y = 84.9%, Q2 = 69.7%). LysoPC (16 : 0), catelaidic acid, cerebronic acid, nonadecanetriol, and asparaginyl-histidine were found to identify PC, with a sensitivity of 89% and a specificity of 91%. Besides, lysoPC (16 : 0), lysoPC (16 : 1), lysoPC (22 : 6), and lysoPC (20 : 3) were found to differentiate PC from DM, with higher accuracy (68% versus 55%) and higher AUC values (72% versus 63%) than those of CA19-9. The diagnostic performance of metabolite biomarkers was finally validated by logistic regression models. CONCLUSION We succeeded in screening differential metabolite ions among PC and DM patients and healthy individuals, thus providing a preliminary basis for screening the biomarkers for the early diagnosis of PC.
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Affiliation(s)
- Hongmin Xu
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, No. 83, Jintang Road, Hedong District, Tianjin 300170, China
| | - Lei Zhang
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, No. 83, Jintang Road, Hedong District, Tianjin 300170, China
| | - Hua Kang
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, No. 83, Jintang Road, Hedong District, Tianjin 300170, China
| | - Jie Liu
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, No. 83, Jintang Road, Hedong District, Tianjin 300170, China
| | - Jiandong Zhang
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, No. 83, Jintang Road, Hedong District, Tianjin 300170, China
| | - Jie Zhao
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, No. 83, Jintang Road, Hedong District, Tianjin 300170, China
| | - Shuye Liu
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, No. 83, Jintang Road, Hedong District, Tianjin 300170, China
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März J, Kurlbaum M, Roche-Lancaster O, Deutschbein T, Peitzsch M, Prehn C, Weismann D, Robledo M, Adamski J, Fassnacht M, Kunz M, Kroiss M. Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors. Front Endocrinol (Lausanne) 2021; 12:722656. [PMID: 34557163 PMCID: PMC8453166 DOI: 10.3389/fendo.2021.722656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/09/2021] [Indexed: 12/11/2022] Open
Abstract
CONTEXT Pheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet. OBJECTIVE Evaluation of quantitative metabolomics as a diagnostic tool for PPGL. DESIGN Targeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study. PATIENTS Prospectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded. RESULTS Among 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling. CONCLUSIONS The diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.
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Affiliation(s)
- Juliane März
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
| | - Max Kurlbaum
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
- Core Unit Clinical Mass Spectrometry, University Hospital, Würzburg, Germany
- *Correspondence: Matthias Kroiss, ; Max Kurlbaum,
| | - Oisin Roche-Lancaster
- Chair of Medical Informatics, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany
- Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-Europäische Metropolregion Nürnberg (CCC ER-EMN), Erlangen, Germany
| | - Timo Deutschbein
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
- Medicover Oldenburg Medizinisches Versorgungszentrum (MVZ), Oldenburg, Germany
| | - Mirko Peitzsch
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Carl Gustav Carus at Technische Universität (TU) Dresden, Dresden, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Dirk Weismann
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
| | - Mercedes Robledo
- Hereditary Endocrine Cancer Group, Spanish National Cancer Research Center, Madrid, Spain
- Hereditary Endocrine Cancer Group, Spanish National Cancer Research Center and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Martin Fassnacht
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
- Core Unit Clinical Mass Spectrometry, University Hospital, Würzburg, Germany
- Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany
| | - Meik Kunz
- Chair of Medical Informatics, Friedrich-Alexander University (FAU) of Erlangen-Nürnberg, Erlangen, Germany
- Fraunhofer Institute of Toxicology and Experimental Medicine, Hannover, Germany
| | - Matthias Kroiss
- Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Germany
- Core Unit Clinical Mass Spectrometry, University Hospital, Würzburg, Germany
- Department of Internal Medicine IV, University Hospital Munich, Ludwig-Maximilians-Universität München, Munich, Germany
- *Correspondence: Matthias Kroiss, ; Max Kurlbaum,
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Saigusa D, Matsukawa N, Hishinuma E, Koshiba S. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metab Pharmacokinet 2020; 37:100373. [PMID: 33631535 DOI: 10.1016/j.dmpk.2020.11.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 12/12/2022]
Abstract
Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.
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Affiliation(s)
- Daisuke Saigusa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Naomi Matsukawa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Eiji Hishinuma
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
| | - Seizo Koshiba
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
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Wei B, Wang C, Teng T, Guo P, Chen M, Xia F, Liu H, Xie J, Feng J, Huang H. Chemotherapeutic efficacy of cucurmosin for pancreatic cancer as an alternative of gemcitabine: a comparative metabolomic study. Gland Surg 2020; 9:1428-1442. [PMID: 33224818 DOI: 10.21037/gs-20-202] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Background As the preferred drug for single chemotherapeutic application in pancreatic cancer, gemcitabine often demonstrated low sensitivity and strong chemotherapy resistance in patients. Therefore, the search for other drugs with high efficiency and low side effects has become of high importance. The aim of this study was to assess the therapeutic effects of cucurmosin on pancreatic cancer as an alternative of gemcitabine and explore its underlying biochemical mechanism. Methods The subcutaneous xenograft mice with pancreatic cancer were treated by high- and low-dose cucurmosin and gemcitabine, respectively. A comparative metabolomic analysis was performed on the serum samples from the different groups by 1H nuclear magnetic resonance (NMR) techniques and then subjected to univariate and multivariate statistical analysis. Results Cucurmosin demonstrated a dose-dependent inhibition to the pancreatic tumors. High-dose cucurmosin provided similar chemotherapeutic efficacy with gemcitabine by positively regulating pyruvate metabolism, glycolysis or gluconeogenesis, and cysteine and methionine metabolism. Inactivating GFR signaling pathway and further inducing apoptosis of tumor cells are the important mechanism of anti-tumor function of cucurmosin. Conclusions Cucurmosin is a promising chemotherapeutic drug for pancreatic cancer. However, the dose selection and surface modification should be optimized according to the stage of pancreatic cancer, and an expanded study in both laboratory and clinical regimes needs to be performed.
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Affiliation(s)
- Binbin Wei
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Congfei Wang
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Tianhong Teng
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Pengfei Guo
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Minghuang Chen
- State Structural Chemistry Key Laboratory of Fujian Institute of Research on Structure of Matter, Chinese Academy of Sciences, Fuzhou, China
| | - Feng Xia
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Huili Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Center for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China
| | - Jieming Xie
- Department of Pharmacology, Fujian Medical University, Fuzhou, China
| | - Jianghua Feng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Heguang Huang
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
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28
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Zhang WH, Wang WQ, Han X, Gao HL, Li TJ, Xu SS, Li S, Xu HX, Li H, Ye LY, Lin X, Wu CT, Long J, Yu XJ, Liu L. Advances on diagnostic biomarkers of pancreatic ductal adenocarcinoma: A systems biology perspective. Comput Struct Biotechnol J 2020; 18:3606-3614. [PMID: 33304458 PMCID: PMC7710502 DOI: 10.1016/j.csbj.2020.11.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/08/2020] [Accepted: 11/10/2020] [Indexed: 12/26/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is usually diagnosed at an advanced stage when curative surgery is no longer an option. Robust diagnostic biomarkers with high sensitivity and specificity for early detection are urgently needed. Systems biology provides a powerful tool for understanding diseases and solving challenging biological problems, allowing biomarkers to be identified and quantified with increasing accuracy, sensitivity, and comprehensiveness. Here, we present a comprehensive overview of efforts to identify biomarkers of PDAC using genomics, transcriptomics, proteomics, metabonomics, and bioinformatics. Systems biology perspective provides a crucial “network” to integrate multi-omics approaches to biomarker identification, shedding additional light on early PDAC detection.
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Affiliation(s)
- Wu-Hu Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Wen-Quan Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xuan Han
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - He-Li Gao
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Tian-Jiao Li
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Shuai-Shuai Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Shuo Li
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Hua-Xiang Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Hao Li
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Long-Yun Ye
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xuan Lin
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Chun-Tao Wu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Jiang Long
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Xian-Jun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Liang Liu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Pancreatic Cancer Institute, Shanghai, China.,Pancreatic Cancer Institute, Fudan University, Shanghai, China
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29
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Plasma free amino acid profiling as metabolomic diagnostic and prognostic biomarker in paediatric cancer patients: a follow-up study. Amino Acids 2020; 53:133-138. [PMID: 33179163 PMCID: PMC7822799 DOI: 10.1007/s00726-020-02910-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 11/01/2020] [Indexed: 01/09/2023]
Abstract
Amino acids (AAs) play a crucial role in cancer cell metabolism. Levels of 22 plasma AAs at the time of diagnosis and after treatment were established among 39 pediatric cancer patients and 33 healthy children. Glutamic acid levels decreased and tryptophan levels increased during treatment. Cancer patients presented significantly lower levels of glutamine and leucine post-treatment while levels of 12 other AAs were higher comparing to controls. Results suggest that plasma free AA profile may serve as a prognostic biomarker.
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30
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Taylor NJ, Gaynanova I, Eschrich SA, Welsh EA, Garrett TJ, Beecher C, Sharma R, Koomen JM, Smalley KSM, Messina JL, Kanetsky PA. Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment. PLoS One 2020; 15:e0240849. [PMID: 33108391 PMCID: PMC7591037 DOI: 10.1371/journal.pone.0240849] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 10/04/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Melanoma causes the vast majority of deaths attributable to skin cancer, largely due to its propensity for metastasis. To date, few studies have examined molecular changes between primary cutaneous melanoma and adjacent putatively normal skin. To broaden temporal inferences related to initiation of disease, we performed a metabolomics investigation of primary melanoma and matched extratumoral microenvironment (EM) tissues; and, to make inferences about progressive disease, we also compared unmatched metastatic melanoma tissues to EM tissues. METHODS Ultra-high performance liquid chromatography-mass spectrometry-based metabolic profiling was performed on frozen human tissues. RESULTS We observed 824 metabolites as differentially abundant among 33 matched tissue samples, and 1,118 metabolites as differentially abundant between metastatic melanoma (n = 46) and EM (n = 34) after false discovery rate (FDR) adjustment (p<0.01). No significant differences in metabolite abundances were noted comparing primary and metastatic melanoma tissues. CONCLUSIONS Overall, pathway-based results significantly distinguished melanoma tissues from EM in the metabolism of: ascorbate and aldarate, propanoate, tryptophan, histidine, and pyrimidine. Within pathways, the majority of individual metabolite abundances observed in comparisons of primary melanoma vs. EM and metastatic melanoma vs. EM were directionally consistent. This observed concordance suggests most identified compounds are implicated in the initiation or maintenance of melanoma.
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Affiliation(s)
- Nicholas J. Taylor
- Department of Epidemiology and Biostatistics, Texas A&M University, College Station, Texas, United States of America
| | - Irina Gaynanova
- Department of Statistics, Texas A&M University, College Station, Texas, United States of America
| | - Steven A. Eschrich
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Eric A. Welsh
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Timothy J. Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Chris Beecher
- IROA Technologies, Chapel Hill, North Carolina, United States of America
| | - Ritin Sharma
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - John M. Koomen
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Keiran S. M. Smalley
- Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
- Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Jane L. Messina
- Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
| | - Peter A. Kanetsky
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, United States of America
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31
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Ahmed-Salim Y, Galazis N, Bracewell-Milnes T, Phelps DL, Jones BP, Chan M, Munoz-Gonzales MD, Matsuzono T, Smith JR, Yazbek J, Krell J, Ghaem-Maghami S, Saso S. The application of metabolomics in ovarian cancer management: a systematic review. Int J Gynecol Cancer 2020; 31:754-774. [PMID: 33106272 DOI: 10.1136/ijgc-2020-001862] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/24/2020] [Accepted: 09/28/2020] [Indexed: 12/15/2022] Open
Abstract
Metabolomics, the global analysis of metabolites in a biological specimen, could potentially provide a fast method of biomarker identification for ovarian cancer. This systematic review aims to examine findings from studies that apply metabolomics to the diagnosis, prognosis, treatment, and recurrence of ovarian cancer. A systematic search of English language publications was conducted on PubMed, Science Direct, and SciFinder. It was augmented by a snowball strategy, whereby further relevant studies are identified from reference lists of included studies. Studies in humans with ovarian cancer which focus on metabolomics of biofluids and tumor tissue were included. No restriction was placed on the time of publication. A separate review of targeted metabolomic studies was conducted for completion. Qualitative data were summarized in a comprehensive table. The studies were assessed for quality and risk of bias using the ROBINS-I tool. 32 global studies were included in the main systematic review. Most studies applied metabolomics to diagnosing ovarian cancer, within which the most frequently reported metabolite changes were a down-regulation of phospholipids and amino acids: histidine, citrulline, alanine, and methionine. Dysregulated phospholipid metabolism was also reported in the separately reviewed 18 targeted studies. Generally, combinations of more than one significant metabolite as a panel, in different studies, achieved a higher sensitivity and specificity for diagnosis than a single metabolite; for example, combinations of different phospholipids. Widespread metabolite differences were observed in studies examining prognosis, treatment, and recurrence, and limited conclusions could be drawn. Cellular processes of proliferation and invasion may be reflected in metabolic changes present in poor prognosis and recurrence. For example, lower levels of lysine, with increased cell invasion as an underlying mechanism, or glutamine dependency of rapidly proliferating cancer cells. In conclusion, this review highlights potential metabolites and biochemical pathways which may aid the clinical care of ovarian cancer if further validated.
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Affiliation(s)
| | - Nicolas Galazis
- Department of Obstetrics and Gynaecology, Northwick Park Hospital, Harrow, UK
| | | | - David L Phelps
- Department of Gynaecological Oncology, Hammersmith Hospital Campus, Du Cane Road, Imperial College Healthcare NHS Trust, London, UK
| | - Benjamin P Jones
- Division of Surgery and Cancer, Institute of Reproductive and Developmental Biology, Hammersmith Hospital Campus, Du Cane Road, Imperial College London, London, UK
| | - Maxine Chan
- South Kensington Campus, Imperial College London Department of Materials, London, UK
| | | | - Tomoko Matsuzono
- Queen Elizabeth Hospital, Department of Obstetrics and Gynaecology, Hong Kong, Hong Kong
| | - James Richard Smith
- West London Gynaecological Cancer Centre, Queen Charlotte's Hospital, Hammersmith Hospital Campus, Du Cane Road, Imperial College Healthcare NHS Trust, London, UK
| | - Joseph Yazbek
- West London Gynaecological Cancer Centre, Queen Charlotte's Hospital, Hammersmith Hospital Campus, Du Cane Road, Imperial College Healthcare NHS Trust, London, UK
| | - Jonathan Krell
- West London Gynaecological Cancer Centre, Queen Charlotte's Hospital, Hammersmith Hospital Campus, Du Cane Road, Imperial College Healthcare NHS Trust, London, UK
| | - Sadaf Ghaem-Maghami
- Department of Gynaecological Oncology, West London Gynaecological Cancer Centre, Queen Charlotte's Hospital, Hammersmith Hospital Campus, Imperial College London and NHS Trust, Du Cane Road, Imperial College London, London, UK
| | - Srdjan Saso
- Division of Surgery and Cancer, Institute of Reproductive and Developmental Biology, Hammersmith Hospital Campus, Du Cane Road, Imperial College London, London, UK
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32
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Nakano R, Nishiumi S, Kobayashi T, Ikegawa T, Kodama Y, Yoshida M. Possibility of detecting intraductal papillary mucinous neoplasms using metabolite biomarkers for pancreatic cancer. Biomark Med 2020; 14:1009-1020. [PMID: 32940075 DOI: 10.2217/bmm-2019-0587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Aim: The aim of this study was to identify whether metabolite biomarker candidates for pancreatic cancer (PC) could aid detection of intraductal papillary mucinous neoplasms (IPMN), recognized as high-risk factors for PC. Materials & methods: The 12 metabolite biomarker candidates, which were found to be useful to detect PC in our previous study, were evaluated for plasma samples from patients with PC (n = 44) or IPMN (n = 24) or healthy volunteers (n = 46). Results: Regarding the performance of individual biomarkers of PC and PC high-risk IPMN, lysine exhibited the best performance (sensitivity: 67.8%; specificity: 86.9%). The multiple logistic regression analysis-based detection model displayed high sensitivity and specificity values of 92.5 and 90.6%, respectively. Conclusion: Metabolite biomarker candidates for PC are useful for detecting high-risk IPMN, which can progress to PC.
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Affiliation(s)
- Ryota Nakano
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Shin Nishiumi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan.,Department of Omics Medicine, Hyogo College of Medicine, Nishinomiya, Japan
| | - Takashi Kobayashi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Takuya Ikegawa
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Yuzo Kodama
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan
| | - Masaru Yoshida
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Hyogo, Japan.,Division of Metabolomics Research, Department of Internal Related, Kobe University Graduate School of Medicine, Hyogo, Japan.,AMED-CREST, AMED, Hyogo, Japan
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33
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Zhou D, Mu D, Cheng M, Dou Y, Zhang X, Feng Z, Qiu G, Yu H, Chen Y, Xu H, Sun J, Zhou L. Differences in lipidomics may be potential biomarkers for early diagnosis of pancreatic cancer. Acta Cir Bras 2020; 35:e202000508. [PMID: 32638847 PMCID: PMC7341992 DOI: 10.1590/s0102-865020200050000008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 04/22/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose To analyze the plasma lipid spectrum between healthy control and patients with pancreatic cancer and to select differentially expressed tumor markers for early diagnosis. Methods In total, 20 patents were divided into case group and healthy control group according to surgical pathology. Of almost 1206 plasma lipid molecules harvested from 20 patients were measured by HILIC using the normal phase LC/MS. Heat map presented the relative levels of metabolites and lipids in the healthy control group and patients with pancreatic cancer. The PCA model was constructed to find out the difference in lipid metabolites. The principal components were drawn in a score plot and any clustering tendency could be observed. PLS-DA were performed to distinguish the healthy control group and pancreatic cancer according to the identified lipid profiling datasets. The volcano plot was used to visualize all variables with VIP>1 and presented the important variables with P<0.01 and |FC|>2. Results The upregulated lipid metabolites in patients with pancreatic cancer contained 9 lipids; however, the downregulated lipid metabolites contained 79 lipids. Conclusion There were lipid metabolomic differences in patients with pancreatic cancer, which could serve as potential tumor markers for pancreatic cancer.
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Affiliation(s)
| | | | | | - Yuting Dou
- Shanghai Changning Maternity and Infant Health Hospital, China
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34
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Zhang X, Shi X, Lu X, Li Y, Zhan C, Akhtar ML, Yang L, Bai Y, Zhao J, Wang Y, Yao Y, Li Y, Nie H. Novel Metabolomics Serum Biomarkers for Pancreatic Ductal Adenocarcinoma by the Comparison of Pre-, Postoperative and Normal Samples. J Cancer 2020; 11:4641-4651. [PMID: 32626510 PMCID: PMC7330680 DOI: 10.7150/jca.41250] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 04/14/2020] [Indexed: 12/11/2022] Open
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive human malignancies. The metabolomic approaches are developed to discover the novel biomarkers of PDAC. Methods: 550 preoperative, postoperative PDAC and normal controls (NCs) serums were employed to characterize metabolic alterations in training and validation sets by LC-MS. Results: The results of PLS-DA analysis indicated that three groups could be distinguished clearly and the post-PDAC group is adjacent to a normal group as compared with pre-PDAC group. Further results showed that histidinyl-lysine significantly increased whereas docosahexaenoic acid and LysoPC (14:0) decreased in pre-PDAC patients as compared with NCs. And these three markers had a significant tendency to recover after tumor resection. The validation set results revealed that for CA19-9 negative patients, 92.3% (12/13) of them can be screened using these three metabolites. The combination of these markers could significantly improve the diagnostic performance for PDAC, with higher sensitivity (0.93), specificity (0.92) and AUC (0.97). Moreover, network and pathways analyses explored the latent relationship among differential metabolites. The glycerolipid metabolism and primary bile acid synthesis showed variation in network and pathway analysis. Conclusions: These three markers combined with CA199 displayed high sensitivity and specificity for detecting PDAC patients from NCs. The results indicated that these three metabolites could be regarded as potential biomarkers to distinguish PDAC from NCs.
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Affiliation(s)
- Xiaohan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiuyun Shi
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xin Lu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yiqun Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Chao Zhan
- The Affiliated Tumor Hospital, Harbin Medical University, Harbin, China
| | | | - Lijun Yang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yunfan Bai
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jianxiang Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yu Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yuanfei Yao
- The Affiliated Tumor Hospital, Harbin Medical University, Harbin, China
| | - Yu Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Huan Nie
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
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35
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Gu W, Tong Z. Clinical Application of Metabolomics in Pancreatic Diseases: A Mini-Review. Lab Med 2020; 51:116-121. [PMID: 31340007 DOI: 10.1093/labmed/lmz046] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Metabolomics is a powerful new analytical method to describe the set of metabolites within cellular tissue and bodily fluids. Metabolomics can uncover detailed information about metabolic changes in organisms. The morphology of these metabolites represents the metabolic processes that occur in cells, such as anabolism, catabolism, inhomogeneous natural absorption and metabolism, detoxification, and metabolism of biomass energy. Because the metabolites of different diseases are different, the specificity of the changes can be found by metabolomics testing, which provides a new source of biomarkers for the early identification of diseases and the difference between benign and malignant states. Metabolomics has a wide application potential in pancreatic diseases, including early detection, diagnosis, and identification of pancreatic diseases. However, there are few studies on metabolomics in pancreatic diseases in the literature. This article reviews the application of metabolomics in the diagnosis, prognosis, treatment, and evaluation of pancreatic diseases.
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Affiliation(s)
- Wang Gu
- Anhui Medical University, Hefei City, China
| | - Zhong Tong
- Hefei First People's Hospital, Hefei City, China
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36
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Martín-Blázquez A, Jiménez-Luna C, Díaz C, Martínez-Galán J, Prados J, Vicente F, Melguizo C, Genilloud O, Pérez del Palacio J, Caba O. Discovery of Pancreatic Adenocarcinoma Biomarkers by Untargeted Metabolomics. Cancers (Basel) 2020; 12:E1002. [PMID: 32325731 PMCID: PMC7225994 DOI: 10.3390/cancers12041002] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/08/2020] [Accepted: 04/13/2020] [Indexed: 12/12/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal cancers, with a 5-year survival rate of less than 5%. In fact, complete surgical resection remains the only curative treatment. However, fewer than 20% of patients are candidates for surgery at the time of presentation. Hence, there is a critical need to identify diagnostic biomarkers with potential clinical utility in this pathology. In this context, metabolomics could be a powerful tool to search for new robust biomarkers. Comparative metabolomic profiling was performed in serum samples from 59 unresectable PDAC patients and 60 healthy controls. Samples were analyzed by using an untargeted metabolomics workflow based on liquid chromatography, coupled to high-resolution mass spectrometry in positive and negative electrospray ionization modes. Univariate and multivariate analysis allowed the identification of potential candidates that were significantly altered in PDAC patients. A panel of nine candidates yielded excellent diagnostic capacities. Pathway analysis revealed four altered pathways in our patients. This study shows the potential of liquid chromatography coupled to high-resolution mass spectrometry as a diagnostic tool for PDAC. Furthermore, it identified novel robust biomarkers with excellent diagnostic capacities.
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Affiliation(s)
- Ariadna Martín-Blázquez
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, 18016 Granada, Spain; (A.M.-B.); (C.D.); (F.V.); (O.G.); (J.P.d.P.)
| | - Cristina Jiménez-Luna
- Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, 1066 Epalinges, Switzerland;
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18016 Granada, Spain; (C.M.); (O.C.)
| | - Caridad Díaz
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, 18016 Granada, Spain; (A.M.-B.); (C.D.); (F.V.); (O.G.); (J.P.d.P.)
| | - Joaquina Martínez-Galán
- Service of Medical Oncology, Hospital Universitario Virgen de las Nieves, 18014 Granada, Spain;
| | - Jose Prados
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18016 Granada, Spain; (C.M.); (O.C.)
- Instituto Biosanitario de Granada (ibs. GRANADA), 18016 Granada, Spain
| | - Francisca Vicente
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, 18016 Granada, Spain; (A.M.-B.); (C.D.); (F.V.); (O.G.); (J.P.d.P.)
| | - Consolación Melguizo
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18016 Granada, Spain; (C.M.); (O.C.)
- Instituto Biosanitario de Granada (ibs. GRANADA), 18016 Granada, Spain
| | - Olga Genilloud
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, 18016 Granada, Spain; (A.M.-B.); (C.D.); (F.V.); (O.G.); (J.P.d.P.)
| | - José Pérez del Palacio
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, 18016 Granada, Spain; (A.M.-B.); (C.D.); (F.V.); (O.G.); (J.P.d.P.)
| | - Octavio Caba
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18016 Granada, Spain; (C.M.); (O.C.)
- Instituto Biosanitario de Granada (ibs. GRANADA), 18016 Granada, Spain
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37
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Wu J, Wu M, Wu Q. Identification of potential metabolite markers for colon cancer and rectal cancer using serum metabolomics. J Clin Lab Anal 2020; 34:e23333. [PMID: 32281150 PMCID: PMC7439421 DOI: 10.1002/jcla.23333] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 01/04/2023] Open
Abstract
Background To determine the metabolic characteristics of patients with colon cancer (CC) and rectal cancer (RC) using gas chromatography‐mass spectrometry (GC‐MS)‐based metabolomics. Methods In this study, serum samples were collected from 22 CC patients and 23 RC patients preoperatively and postoperatively and 45 healthy volunteers (HVs), and subjected to metabolomics analysis by GC‐MS. Differential metabolites in the preoperative RC and CC samples and HVs were identified as potential biomarkers and evaluated for their utilities by receiver operating characteristic analyses. Results The different metabolic markers between CC and RC patients were identified, which may assist in distinguishing the two types of cancers. The area under the curve (AUC) was 0.805 for combination of d‐glucose and d‐mannose for CC diagnosis, and 0.889 for combination of 2‐aminobutanoic acid, 3‐hydroxypyridine, d‐glucose, d‐mannose, isoleucine, l‐tryptophan, urea, and uric acid for RC diagnosis. The combinations of metabolite markers showed a better predictability than CEA and CA199 two commonly used protein markers for CRC diagnosis in clinical practice. Combining the metabolite markers with these two protein markers effectively improved the diagnostic accuracy with the AUC reaching 0.936 and 0.937 for CC and RC diagnosis, respectively. Conclusions Metabolic profiles are different in the blood samples between CC and RC patients. The study has established a panel of metabolic markers as a predictive and multiplexing signature for CC and RC diagnosis.
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Affiliation(s)
- Jianping Wu
- Department of Clinical Laboratory, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Minyi Wu
- Department of Clinical Laboratory, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Qianxia Wu
- Department of Clinical Laboratory, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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38
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Sun Y, Li S, Li J, Xiao X, Hua Z, Wang X, Yan S. A clinical metabolomics-based biomarker signature as an approach for early diagnosis of gastric cardia adenocarcinoma. Oncol Lett 2020; 19:681-690. [PMID: 31897184 PMCID: PMC6924188 DOI: 10.3892/ol.2019.11173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 10/10/2019] [Indexed: 12/13/2022] Open
Abstract
Gastric cardia adenocarcinoma (GCA) has a high mortality rate worldwide; however, current early diagnostic methods lack efficacy. Therefore, the aim of the present study was to identify potential biomarkers for the early diagnosis of GCA. Global metabolic profiles were obtained from plasma samples collected from 21 patients with GCA and 48 healthy controls using ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry. The orthogonal partial least squares discrimination analysis model was applied to distinguish patients with GCA from healthy controls and to identify potential biomarkers. Metabolic pathway analysis was performed using MetaboAnalyst (version 4.0) and revealed that ‘glycerophospholipid metabolism’, ‘linoleic acid metabolism’, ‘fatty acid biosynthesis’ and ‘primary bile acid biosynthesis’ were significantly associated with GCA. In addition, an early diagnostic model for GCA was established based on the relative levels of four key biomarkers, including phosphorylcholine, glycocholic acid, L-acetylcarnitine and arachidonic acid. The area under the receiver operating characteristic curve revealed that the diagnostic model had a sensitivity and specificity of 0.977 and 0.952, respectively. The present study demonstrated that metabolomics may aid the identification of the mechanisms underlying the pathogenesis of GCA. In addition, the proposed diagnostic method may serve as a promising approach for the early diagnosis of GCA.
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Affiliation(s)
- Yuanfang Sun
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
| | - Shasha Li
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, P.R. China
| | - Jin Li
- Department of Oncology, The 903rd Hospital of PLA, Hangzhou, Zhejiang 310013, P.R. China
| | - Xue Xiao
- Institute of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510006, P.R. China
| | - Zhaolai Hua
- People's Hospital of Yangzhong, Yangzhong, Jiangsu 212200, P.R. China
| | - Xi Wang
- Department of Oncology, The 903rd Hospital of PLA, Hangzhou, Zhejiang 310013, P.R. China
| | - Shikai Yan
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, P.R. China
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39
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Rho SY, Lee SG, Park M, Lee J, Lee SH, Hwang HK, Lee MJ, Paik YK, Lee WJ, Kang CM. Developing a preoperative serum metabolome-based recurrence-predicting nomogram for patients with resected pancreatic ductal adenocarcinoma. Sci Rep 2019; 9:18634. [PMID: 31819109 PMCID: PMC6901525 DOI: 10.1038/s41598-019-55016-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 11/14/2019] [Indexed: 12/12/2022] Open
Abstract
We investigated the potential application of preoperative serum metabolomes in predicting recurrence in patients with resected pancreatic cancer. From November 2012 to June 2014, patients who underwent potentially curative pancreatectomy for pancreatic ductal adenocarcinoma were examined. Among 57 patients, 32 were men; 42 had pancreatic head cancers. The 57 patients could be clearly categorized into two main clusters using 178 preoperative serum metabolomes. Patients within cluster 2 showed earlier tumor recurrence, compared with those within cluster 1 (p = 0.034). A nomogram was developed for predicting the probability of early disease-free survival in patients with resected pancreatic cancer. Preoperative cancer antigen (CA) 19–9 levels and serum metabolomes PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful preoperative clinical variables with which to predict 6-month and 1-year cancer recurrence-free survival after radical pancreatectomy, with a Harrell’s concordance index of 0.823 (95% CI: 0.750–0.891) and integrated area under the curve of 0.816 (95% CI: 0.736–0.893). Patients with resected pancreatic cancer could be categorized according to their different metabolomes to predict early cancer recurrence. Preoperative detectable parameters, serum CA 19–9, PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful predictors of early recurrence of pancreatic cancer.
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Affiliation(s)
- Seoung Yoon Rho
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, Korea.,Yonsei Pancreatobiliary Cancer Center, Severance Hospital, Seoul, Korea
| | - Sang-Guk Lee
- Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Minsu Park
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinae Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung Hwan Lee
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, Korea.,Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ho Kyoung Hwang
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, Korea.,Yonsei Pancreatobiliary Cancer Center, Severance Hospital, Seoul, Korea
| | - Min Jung Lee
- Yonsei Proteome Research Center and ‡Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, Yonsei University College of Life Science and Biotechnology, Seoul, Korea
| | - Young-Ki Paik
- Yonsei Proteome Research Center and ‡Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, Yonsei University College of Life Science and Biotechnology, Seoul, Korea
| | - Woo Jung Lee
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, Korea.,Yonsei Pancreatobiliary Cancer Center, Severance Hospital, Seoul, Korea
| | - Chang Moo Kang
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, Korea. .,Yonsei Pancreatobiliary Cancer Center, Severance Hospital, Seoul, Korea.
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40
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Germeys C, Vandoorne T, Bercier V, Van Den Bosch L. Existing and Emerging Metabolomic Tools for ALS Research. Genes (Basel) 2019; 10:genes10121011. [PMID: 31817338 PMCID: PMC6947647 DOI: 10.3390/genes10121011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 11/23/2019] [Accepted: 12/03/2019] [Indexed: 12/12/2022] Open
Abstract
Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies.
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Affiliation(s)
- Christine Germeys
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
| | - Tijs Vandoorne
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
| | - Valérie Bercier
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
| | - Ludo Van Den Bosch
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
- Correspondence: ; Tel.: +32-16-33-06-81
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41
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Kang CM, Yun B, Kim M, Song M, Kim YH, Lee SH, Lee H, Lee SM, Lee SM. Postoperative serum metabolites of patients on a low carbohydrate ketogenic diet after pancreatectomy for pancreatobiliary cancer: a nontargeted metabolomics pilot study. Sci Rep 2019; 9:16820. [PMID: 31727967 PMCID: PMC6856065 DOI: 10.1038/s41598-019-53287-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/29/2019] [Indexed: 02/07/2023] Open
Abstract
A ketogenic diet is a potential adjuvant cancer therapy that limits glucose availability to tumours while fuelling normal tissues with ketone bodies. We examined the effect of a low carbohydrate ketogenic diet (LCKD) (80% kcal from fat, ketogenic ratio 1.75:1, w/w) compared to a general hospital diet (GD) on serum metabolic profiles in patients (n = 18, ≥ 19 years old) who underwent pancreatectomy for pancreatobiliary cancer. Serum samples collected preoperatively (week 0) and after the dietary intervention (week 2) were analysed with a nontargeted metabolomics approach using liquid chromatography-tandem mass spectrometry. Serum β-hydroxybutyrate and total ketone levels significantly increased after 2 weeks of LCKD compared to GD (p < 0.05). Principal component analysis score plots and orthogonal partial least squares discriminant analysis also showed significant differences between groups at week 2, with strong validation. In all, 240 metabolites differed between LCKD and GD. Pathways including glycerophospholipid and sphingolipid metabolisms were significantly enriched in the LCKD samples. LCKD decreased C22:1-ceramide levels, which are reported to be high in pancreatic cancer, while increasing lysophosphatidylcholine (18:2), uric acid, citrulline, and inosine levels, which are generally low in pancreatic cancer. Postoperative LCKD might beneficially modulate pancreatic cancer-related metabolites in patients with pancreatobiliary cancer.
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Affiliation(s)
- Chang Moo Kang
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Yonsei University College of Medicine, Yonsei Pancreatobiliary Cancer Center, Severance Hospital, Seoul, 03722, Korea
| | - BoKyeong Yun
- Department of Food and Nutrition, BK21 PLUS Project, College of Human Ecology, Yonsei University, Seoul, 03722, Korea
| | - Minju Kim
- Department of Food and Nutrition, BK21 PLUS Project, College of Human Ecology, Yonsei University, Seoul, 03722, Korea
| | - Mina Song
- Department of Food and Nutrition, BK21 PLUS Project, College of Human Ecology, Yonsei University, Seoul, 03722, Korea
| | - Yeon-Hee Kim
- Department of Food and Nutrition, BK21 PLUS Project, College of Human Ecology, Yonsei University, Seoul, 03722, Korea
| | - Sung Hwan Lee
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Texas, 77030, United States
| | - Hosun Lee
- Department of Nutrition Care, Severance Hospital, Yonsei University Health System, Seoul, 03722, Korea
| | - Song Mi Lee
- Department of Nutrition Care, Severance Hospital, Yonsei University Health System, Seoul, 03722, Korea
| | - Seung-Min Lee
- Department of Food and Nutrition, BK21 PLUS Project, College of Human Ecology, Yonsei University, Seoul, 03722, Korea.
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Carmicheal J, Patel A, Dalal V, Atri P, Dhaliwal AS, Wittel UA, Malafa MP, Talmon G, Swanson BJ, Singh S, Jain M, Kaur S, Batra SK. Elevating pancreatic cystic lesion stratification: Current and future pancreatic cancer biomarker(s). Biochim Biophys Acta Rev Cancer 2019; 1873:188318. [PMID: 31676330 DOI: 10.1016/j.bbcan.2019.188318] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 10/25/2019] [Accepted: 10/25/2019] [Indexed: 02/06/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an incredibly deadly disease with a 5-year survival rate of 9%. The presence of pancreatic cystic lesions (PCLs) confers an increased likelihood of future pancreatic cancer in patients placing them in a high-risk category. Discerning concurrent malignancy and risk of future PCL progression to cancer must be carefully and accurately determined to improve survival outcomes and avoid unnecessary morbidity of pancreatic resection. Unfortunately, current image-based guidelines are inadequate to distinguish benign from malignant lesions. There continues to be a need for accurate molecular and imaging biomarker(s) capable of identifying malignant PCLs and predicting the malignant potential of PCLs to enable risk stratification and effective intervention management. This review provides an update on the current status of biomarkers from pancreatic cystic fluid, pancreatic juice, and seromic molecular analyses and discusses the potential of radiomics for differentiating PCLs harboring cancer from those that do not.
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Affiliation(s)
- Joseph Carmicheal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Asish Patel
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA; Department of Surgery, University of Nebraska Medical Center, Omaha, NE, USA
| | - Vipin Dalal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Pranita Atri
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Amaninder S Dhaliwal
- Department of Internal Medicine, Division of Gastroenterology-Hepatology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Uwe A Wittel
- Department of General- and Visceral Surgery, University of Freiburg Medical Center, Faculty of Medicine, Freiburg, Germany
| | - Mokenge P Malafa
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Geoffrey Talmon
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Benjamin J Swanson
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Shailender Singh
- Department of Internal Medicine, Division of Gastroenterology-Hepatology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Maneesh Jain
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA; Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sukhwinder Kaur
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Surinder K Batra
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA; Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA; Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, USA; Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA.
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43
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Furukawa H, Oka S, Shimada K, Hashimoto A, Komiya A, Matsui T, Fukui N, Tohma S. Serum Metabolomic Profiles of Rheumatoid Arthritis Patients With Acute-Onset Diffuse Interstitial Lung Disease. Biomark Insights 2019; 14:1177271919870472. [PMID: 31488947 PMCID: PMC6709435 DOI: 10.1177/1177271919870472] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 07/26/2019] [Indexed: 12/02/2022] Open
Abstract
Objective: Acute-onset diffuse interstitial lung disease (AoDILD) includes acute
exacerbation of interstitial lung disease (ILD), drug-induced ILD, and
Pneumocystis pneumonia, and frequently occurs in
patients with rheumatoid arthritis (RA). Since AoDILD causes a poor
prognosis in RA, biomarkers for AoDILD were eagerly desired. Metabolomic
analyses were extensively performed in cancer patients and successfully
generated better diagnostic biomarkers. In the present study, serum
metabolomic profiles of AoDILD in RA were investigated to generate better
potential metabolomic biomarkers. Methods: Serum samples of 10 RA patients with AoDILD were collected on admission and
in a stable state, more than 3 months before the admission. Serum
metabolomic analyses were conducted on the samples from these RA patients
with AoDILD. Results: Apparently distinct serum metabolomic profiles in AoDILD were not observed in
univariate or hierarchical cluster analyses. Partial least
squares-discriminant analysis (PLS-DA) was performed to select candidate
metabolites based on variable importance in projection (VIP) scores. The
PLS-DA model generated from the four metabolites with VIP scores more than
2.25 (mannosamine, alliin, kynurenine, and 2-hydroxybutyric acid) could
successfully discriminate AoDILD from the stable condition (area under the
curve: 0.962, 95% confidence interval: 0.778–1.000). Conclusion: It was demonstrated that metabolomic profiling was useful to generate better
biomarkers in AoDILD.
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Affiliation(s)
- Hiroshi Furukawa
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan.,Molecular and Genetic Epidemiology Laboratory, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Department of Rheumatology, National Hospital Organization Tokyo National Hospital, Kiyose, Japan
| | - Shomi Oka
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan.,Molecular and Genetic Epidemiology Laboratory, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Department of Rheumatology, National Hospital Organization Tokyo National Hospital, Kiyose, Japan
| | - Kota Shimada
- Department of Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan.,Department of Rheumatic Diseases, Tokyo Metropolitan Tama Medical Center, Fuchu, Japan
| | - Atsushi Hashimoto
- Department of Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan
| | - Akiko Komiya
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan.,Department of Clinical Laboratory, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan
| | - Toshihiro Matsui
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan.,Department of Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan
| | - Naoshi Fukui
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan
| | - Shigeto Tohma
- Clinical Research Center for Allergy and Rheumatology, National Hospital Organization Sagamihara National Hospital, Sagamihara, Japan.,Department of Rheumatology, National Hospital Organization Tokyo National Hospital, Kiyose, Japan
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Kobayashi T, Honda K. Trends in biomarker discoveries for the early detection and risk stratification of pancreatic cancer using omics studies. Expert Rev Mol Diagn 2019; 19:651-654. [PMID: 31298060 DOI: 10.1080/14737159.2019.1643718] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Takashi Kobayashi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine , Kobe , Hyogo , Japan
| | - Kazufumi Honda
- Department of Biomarkers for Early Detection of Cancer, National Cancer Center Research Institute , Tokyo , Japan
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Fest J, Vijfhuizen LS, Goeman JJ, Veth O, Joensuu A, Perola M, Männistö S, Ness-Jensen E, Hveem K, Haller T, Tonisson N, Mikkel K, Metspalu A, van Duijn CM, Ikram A, Stricker BH, Ruiter R, van Eijck CHJ, van Ommen GJB, ʼt Hoen PAC. Search for Early Pancreatic Cancer Blood Biomarkers in Five European Prospective Population Biobanks Using Metabolomics. Endocrinology 2019; 160:1731-1742. [PMID: 31125048 PMCID: PMC6594461 DOI: 10.1210/en.2019-00165] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 05/17/2019] [Indexed: 02/06/2023]
Abstract
Most patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer. Prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between 1 month and 17 years after sampling (N = 356) and age- and sex-matched controls (N = 887) were collected from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian Biobank, Rotterdam Study). We applied proton nuclear magnetic resonance-based metabolomics on the Nightingale platform. Logistic regression identified two interesting hits: glutamine (P = 0.011) and histidine (P = 0.012), with Westfall-Young family-wise error rate adjusted P values of 0.43 for both. Stratification in quintiles showed a 1.5-fold elevated risk for the lowest 20% of glutamine and a 2.2-fold increased risk for the lowest 20% of histidine. Stratification by time to diagnosis suggested glutamine to be involved in an earlier process (2 to 5 years before diagnosis), and histidine in a process closer to the actual onset (<2 years). Our data did not support the branched-chain amino acids identified earlier in several US cohorts as potential biomarkers for pancreatic cancer. Thus, although we identified glutamine and histidine as potential biomarkers of biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically useful per se as prognostic biomarkers.
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Affiliation(s)
- Jesse Fest
- Department of Surgery, Erasmus Medical Center, Rotterdam, Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Lisanne S Vijfhuizen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Jelle J Goeman
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Olga Veth
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Anni Joensuu
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Markus Perola
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Satu Männistö
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - Eivind Ness-Jensen
- HUNT Research Center, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Kristian Hveem
- HUNT Research Center, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Neeme Tonisson
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Clinical Genetics, Tartu University Hospital, Tartu, Estonia
| | - Kairit Mikkel
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | | | - Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Rikje Ruiter
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Gert-Jan B van Ommen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Peter A C ʼt Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, Netherlands
- Correspondence: Peter A. C. ’t Hoen, PhD, Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Route 260, P.O. Box 9101, 6500 HB Nijmegen, Netherlands. E-mail:
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NMR-Based Metabolomics in Metal-Based Drug Research. Molecules 2019; 24:molecules24122240. [PMID: 31208065 PMCID: PMC6630333 DOI: 10.3390/molecules24122240] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 06/12/2019] [Accepted: 06/13/2019] [Indexed: 12/24/2022] Open
Abstract
Thanks to recent advances in analytical technologies and statistical capabilities, the application field of metabolomics has increased significantly. Currently, this approach is used to investigate biological substrates looking for metabolic profile alterations, diseases markers, and drug effects. In particular, NMR spectroscopy has shown great potential as a detection technique, mainly for the ability to detect multiple (10s to 100s) metabolites at once without separation. Only in recent years has the NMR-based metabolomic approach been extended to investigate the cell metabolic alterations induced by metal-based antitumor drug administration. As expected, these studies are mainly focused on platinum complexes, but some palladium and ruthenium compounds are also under investigation. The use of a metabolomics approach was very effective in assessing tumor response to drugs and providing insights into the mechanism of action and resistance. Therefore, metabolomics may open new perspectives into the development of metal-based drugs. In particular, it has been shown that NMR-based, in vitro metabolomics is a powerful tool for detecting variations of the cell metabolites induced by the metal drug exposure, thus offering also the possibility of identifying specific markers for in vivo monitoring of tumor responsiveness to anticancer treatments.
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Lagies S, Schlimpert M, Braun LM, Kather M, Plagge J, Erbes T, Wittel UA, Kammerer B. Unraveling altered RNA metabolism in pancreatic cancer cells by liquid-chromatography coupling to ion mobility mass spectrometry. Anal Bioanal Chem 2019; 411:6319-6328. [PMID: 31037374 DOI: 10.1007/s00216-019-01814-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/27/2019] [Accepted: 03/27/2019] [Indexed: 12/12/2022]
Abstract
Ion mobility coupling to mass spectrometry facilitates enhanced identification certitude. Further coupling to liquid chromatography results in multi-dimensional analytical methods, especially suitable for complex matrices with structurally similar compounds. Modified nucleosides represent a large group of very similar members linked to aberrant proliferation. Besides basal production under physiological conditions, they are increasingly excreted by transformed cells and subsequently discussed as putative biomarkers for various cancer types. Here, we report a method for modified nucleosides covering 37 species. We determined collisional cross-sections with high reproducibility from pure analytical standards. For sample purification, we applied an optimized phenylboronic acid solid-phase extraction on media obtained from four different pancreatic cancer cell lines. Our analysis could discriminate different subtypes of pancreatic cancer cell lines. Importantly, they could clearly be separated from a pancreatic control cell line as well as blank medium. m1A, m27G, and Asm were the most important features discriminating cancer cell lines derived from well-differentiated and poorly differentiated cancers. Eventually, we suggest the analytical method reported here for future tumor-marker identification studies. Graphical abstract.
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Affiliation(s)
- Simon Lagies
- Center for Biological Systems Analysis ZBSA, Albert-Ludwigs-University Freiburg, Habsburgerstr. 49, 79104, Freiburg, Germany.,Institute of Biology II, Albert-Ludwigs-University Freiburg, Schänzlestr. 1, 79104, Freiburg, Germany.,Spemann Graduate School of Biology and Medicine, Albert-Ludwigs-University Freiburg, Albertstr. 19A, 79104, Freiburg, Germany
| | - Manuel Schlimpert
- Center for Biological Systems Analysis ZBSA, Albert-Ludwigs-University Freiburg, Habsburgerstr. 49, 79104, Freiburg, Germany.,Institute of Biology II, Albert-Ludwigs-University Freiburg, Schänzlestr. 1, 79104, Freiburg, Germany.,Spemann Graduate School of Biology and Medicine, Albert-Ludwigs-University Freiburg, Albertstr. 19A, 79104, Freiburg, Germany
| | - Lukas M Braun
- Center for Biological Systems Analysis ZBSA, Albert-Ludwigs-University Freiburg, Habsburgerstr. 49, 79104, Freiburg, Germany.,Department of General- and Visceral Surgery, University of Freiburg Medical Center, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Michel Kather
- Center for Biological Systems Analysis ZBSA, Albert-Ludwigs-University Freiburg, Habsburgerstr. 49, 79104, Freiburg, Germany.,Faculty of Chemistry and Pharmacy, Albert-Ludwigs-University Freiburg, Hebelstr. 27, 79104, Freiburg, Germany.,Hermann Staudinger Graduate School, University of Freiburg, Hebelstr. 27, 79104, Freiburg, Germany
| | - Johannes Plagge
- Center for Biological Systems Analysis ZBSA, Albert-Ludwigs-University Freiburg, Habsburgerstr. 49, 79104, Freiburg, Germany
| | - Thalia Erbes
- Department of Gynecology and Obstetrics, Faculty of Medicine and Medical Center, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Uwe A Wittel
- Department of General- and Visceral Surgery, University of Freiburg Medical Center, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Bernd Kammerer
- Center for Biological Systems Analysis ZBSA, Albert-Ludwigs-University Freiburg, Habsburgerstr. 49, 79104, Freiburg, Germany. .,BIOSS Centre for Biological Signalling Studies, University of Freiburg, Schänzlestr. 16, 79104, Freiburg, Germany.
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Yu M, Xiang T, Wu X, Zhang S, Yang W, Zhang Y, Chen Q, Sun S, Xie B. Diagnosis of acute pediatric appendicitis from children with inflammatory diseases by combination of metabolic markers and inflammatory response variables. Clin Chem Lab Med 2019; 56:1001-1010. [PMID: 29306913 DOI: 10.1515/cclm-2017-0858] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2017] [Accepted: 12/04/2017] [Indexed: 11/15/2022]
Abstract
BACKGROUND The discovery of new metabolic markers may be helpful for early diagnosis of acute pediatric appendicitis (APA). However, no studies have been reported regarding identification of potential metabolic markers for the APA diagnosis by metabonomics. METHODS Serum samples of APA (n=32), non-appendicitis inflammation (NAI, n=32) and healthy children (HS, n=65) were analyzed by the 1H NMR-based metabonomics. A logistic regression model was established to screen the most efficient markers combinations for classification. Forty double-blind samples were further validated the model. RESULTS Nine blood metabolites that were different in the APA group from other groups were identified. To differentiate APA from HS, single variable of acetate, formate, white blood cell (WBC) and C-reactive protein (CRP) showed a high diagnostic value (area under the receiver operating characteristic [AUROC]<0.92), while they had a weak diagnostic value (AUROC<0.77) for identifying the APA and NAI. By contrast, the AUROC values of leucine (0.799) were higher than that of WBC and CRP. A combination of five variables, i.e. leucine, lactate, betaine, WBC and CRP, showed a high diagnostic value (AUROC=0.973) for the APA discriminating from the NAI, and the sensitivity and specificity were 93.8% and 93.7%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 85% for 40 unknown samples. CONCLUSIONS The current study provides useful information in our understanding of the metabolic alterations associated with APA and indicates that measurement of these metabolites in serum effectively aids in the clinical identification of APA.
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Affiliation(s)
- Mengjie Yu
- Department of Infectious Diseases, The Second Affiliated Hospital of Nanchang University, School of Pharmaceutical Science, Nanchang University, Nanchang, P.R. China
| | - Tianxin Xiang
- Department of Infectious Disease, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Xiaoping Wu
- Department of Infectious Disease, The First Affiliated Hospital of Nanchang University, Nanchang, P.R. China
| | - Shouhua Zhang
- Department of General Surgery, Jiangxi Children's Hospital, Nanchang, P.R. China
| | - Wenlong Yang
- Department of Infectious Diseases, The Second Affiliated Hospital of Nanchang University, School of Pharmaceutical Science, Nanchang University, Nanchang, P.R. China
| | - Yu Zhang
- Department of Infectious Diseases, The Second Affiliated Hospital of Nanchang University, School of Pharmaceutical Science, Nanchang University, Nanchang, P.R. China
| | - Qiang Chen
- Department of General Surgery, Jiangxi Children's Hospital, Nanchang, P.R. China
| | - Shuilin Sun
- Department of Infectious Diseases, The Second Affiliated Hospital of Nanchang University, School of Pharmaceutical Science, Nanchang University, Nanchang, P.R. China
| | - Baogang Xie
- Department of Infectious Diseases, The Second Affiliated Hospital of Nanchang University, School of Pharmaceutical Science, Nanchang University, Nanchang 330006, P.R. China, Phone: +86 791 86361839, Fax: +86 791 86361839
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Abstract
Pancreatic cancer remains the most fatal human tumor type. The aggressive tumor biology coupled with the lack of early detection strategies and effective treatment are major reasons for the poor survival rate. Collaborative research efforts have been devoted to understand pancreatic cancer at the molecular level. Large-scale genomic studies have generated important insights into the genetic drivers of pancreatic cancer. In the post-genomic era, protein sequencing of tumor tissue, cell lines, pancreatic juice, and blood from patients with pancreatic cancer has provided a fundament for the development of new diagnostic and prognostic biomarkers. The integration of mass spectrometry and genomic sequencing strategies may help characterize protein identities and post-translational modifications that relate to a specific mutation. Consequently, proteomic and genomic techniques have become a compulsory requirement in modern medicine and health care. These types of proteogenomic studies may usher in a new era of precision diagnostics and treatment in patients with pancreatic cancer.
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Jiao L, Maity S, Coarfa C, Rajapakshe K, Chen L, Jin F, Putluri V, Tinker LF, Mo Q, Chen F, Sen S, Sangi-Hyghpeykar H, El-Serag HB, Putluri N. A Prospective Targeted Serum Metabolomics Study of Pancreatic Cancer in Postmenopausal Women. Cancer Prev Res (Phila) 2019; 12:237-246. [PMID: 30723176 DOI: 10.1158/1940-6207.capr-18-0201] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 11/12/2018] [Accepted: 01/29/2019] [Indexed: 12/11/2022]
Abstract
To examine the association between metabolic deregulation and pancreatic cancer, we conducted a two-stage case-control targeted metabolomics study using prediagnostic sera collected one year before diagnosis in the Women's Health Initiative study. We used the LC/MS to quantitate 470 metabolites in 30 matched case/control pairs. From 180 detectable metabolites, we selected 14 metabolites to be validated in additional 18 matched case/control pairs. We used the paired t test to compare the concentrations of each metabolite between cases and controls and used the log fold change (FC) to indicate the magnitude of difference. FDR adjusted q-value < 0.25 was indicated statistically significant. Logistic regression model and ROC curve analysis were used to evaluate the clinical utility of the metabolites. Among 30 case/control pairs, 1-methyl-l-tryptophan (L-1MT) was significantly lower in the cases than in the controls (log2 FC = -0.35; q-value = 0.03). The area under the ROC curve was 0.83 in the discrimination analysis based on the levels of L-1MT, acadesine, and aspartic acid. None of the metabolites was validated in additional independent 18 case/control pairs. No significant association was found between the examined metabolites and undiagnosed pancreatic cancer.
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Affiliation(s)
- Li Jiao
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas. .,Center for Innovations in Quality, Effectiveness and Safety (IQuESt), Michael E. DeBakey VA Medical Center, Houston, Texas.,Advanced Technology Core, Baylor College of Medicine, Houston, Texas.,Department of Molecular & Cell Biology, Baylor College of Medicine, Houston, Texas.,Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Suman Maity
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Cristian Coarfa
- Advanced Technology Core, Baylor College of Medicine, Houston, Texas.,Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | | | - Liang Chen
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas.,Center for Innovations in Quality, Effectiveness and Safety (IQuESt), Michael E. DeBakey VA Medical Center, Houston, Texas
| | - Feng Jin
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Vasanta Putluri
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Lesley F Tinker
- Center for Translational Research on Inflammatory Diseases (CTRID), Michael E. DeBakey VA Medical Center, Houston, Texas
| | - Qianxing Mo
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Fengju Chen
- Advanced Technology Core, Baylor College of Medicine, Houston, Texas
| | - Subrata Sen
- Department of Translational Molecular Pathology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | | | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, Texas.,Center for Innovations in Quality, Effectiveness and Safety (IQuESt), Michael E. DeBakey VA Medical Center, Houston, Texas.,Advanced Technology Core, Baylor College of Medicine, Houston, Texas.,Department of Molecular & Cell Biology, Baylor College of Medicine, Houston, Texas
| | - Nagireddy Putluri
- Advanced Technology Core, Baylor College of Medicine, Houston, Texas.,Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas.,Texas Medical Center Digestive Disease Center, Houston, Texas
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