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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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2
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Michálková L, Horník Š, Sýkora J, Setnička V, Bunganič B. Prediction of Pathologic Change Development in the Pancreas Associated with Diabetes Mellitus Assessed by NMR Metabolomics. J Proteome Res 2023. [PMID: 37018516 DOI: 10.1021/acs.jproteome.3c00047] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Nuclear magnetic resonance (NMR) metabolomics was used for identification of metabolic changes in pancreatic cancer (PC) blood plasma samples when compared to healthy controls or diabetes mellitus patients. An increased number of PC samples enabled a subdivision of the group according to individual PC stages and the construction of predictive models for finer classification of at-risk individuals recruited from patients with recently diagnosed diabetes mellitus. High-performance values of orthogonal partial least squares (OPLS) discriminant analysis were found for discrimination between individual PC stages and both control groups. The discrimination between early and metastatic stages was achieved with only 71.5% accuracy. A predictive model based on discriminant analyses between individual PC stages and the diabetes mellitus group identified 12 individuals out of 59 as at-risk of development of pathological changes in the pancreas, and four of them were classified as at moderate risk.
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Affiliation(s)
- Lenka Michálková
- Institute of Chemical Process Fundamentals of the CAS, 165 00 Prague 6, Czech Republic
- Department of Analytical Chemistry, University of Chemistry and Technology, Prague, 166 28 Prague 6, Czech Republic
| | - Štěpán Horník
- Institute of Chemical Process Fundamentals of the CAS, 165 00 Prague 6, Czech Republic
| | - Jan Sýkora
- Laboratory of NMR Spectroscopy, University of Chemistry and Technology, Prague, 166 28 Prague 6, Czech Republic
| | - Vladimír Setnička
- Department of Analytical Chemistry, University of Chemistry and Technology, Prague, 166 28 Prague 6, Czech Republic
| | - Bohuš Bunganič
- Department of Internal Medicine, 1st Faculty of Medicine of Charles University and Military University Hospital, 169 02 Prague 6, Czech Republic
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3
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Ketavarapu V, Ravikanth V, Sasikala M, Rao GV, Devi CV, Sripadi P, Bethu MS, Amanchy R, Murthy HVV, Pandol SJ, Reddy DN. Integration of metabolites from meta-analysis with transcriptome reveals enhanced SPHK1 in PDAC with a background of pancreatitis. BMC Cancer 2022; 22:792. [PMID: 35854233 PMCID: PMC9295503 DOI: 10.1186/s12885-022-09816-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/22/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Pathophysiology of transformation of inflammatory lesions in chronic pancreatitis (CP) to pancreatic ductal adenocarcinoma (PDAC) is not clear. METHODS We conducted a systematic review, meta-analysis of circulating metabolites, integrated this data with transcriptome analysis of human pancreatic tissues and validated using immunohistochemistry. Our aim was to establish biomarker signatures for early malignant transformation in patients with underlying CP and identify therapeutic targets. RESULTS Analysis of 19 studies revealed AUC of 0.86 (95% CI 0.81-0.91, P < 0.0001) for all the altered metabolites (n = 88). Among them, lipids showed higher differentiating efficacy between PDAC and CP; P-value (< 0.0001). Pathway enrichment analysis identified sphingomyelin metabolism (impact value-0.29, FDR of 0.45) and TCA cycle (impact value-0.18, FDR of 0.06) to be prominent pathways in differentiating PDAC from CP. Mapping circulating metabolites to corresponding genes revealed 517 altered genes. Integration of these genes with transcriptome data of CP and PDAC with a background of CP (PDAC-CP) identified three upregulated genes; PIGC, PPIB, PKM and three downregulated genes; AZGP1, EGLN1, GNMT. Comparison of CP to PDAC-CP and PDAC-CP to PDAC identified upregulation of SPHK1, a known oncogene. CONCLUSIONS Our analysis suggests plausible role for SPHK1 in development of pancreatic adenocarcinoma in long standing CP patients. SPHK1 could be further explored as diagnostic and potential therapeutic target.
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Affiliation(s)
- Vijayasarathy Ketavarapu
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Vishnubhotla Ravikanth
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Mitnala Sasikala
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - G. V. Rao
- grid.410866.d0000 0004 1803 177XAIG Hospitals, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Ch. Venkataramana Devi
- grid.412419.b0000 0001 1456 3750Department of Biochemistry, University College of Science, Osmania University, Hyderabad, 500 007 India
| | - Prabhakar Sripadi
- grid.417636.10000 0004 0636 1405Centre for Mass Spectrometry, Analytical & Structural Chemistry Department, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, 500 007 India
| | - Murali Satyanarayana Bethu
- grid.410865.eDivision of Applied Biology, CSIR-IICT (Indian Institute of Chemical Technology), Ministry of Science and Technology (GOI), Hyderabad, Telangana 500007 India ,grid.240614.50000 0001 2181 8635Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Elm &Carlton Streets, Buffalo, New York, 14221 USA
| | - Ramars Amanchy
- grid.410865.eDivision of Applied Biology, CSIR-IICT (Indian Institute of Chemical Technology), Ministry of Science and Technology (GOI), Hyderabad, Telangana 500007 India
| | - H. V. V. Murthy
- grid.410866.d0000 0004 1803 177XAsian Healthcare Foundation, Asian Institute of Gastroenterology, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
| | - Stephen J. Pandol
- grid.50956.3f0000 0001 2152 9905Department of Medicine, Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - D. Nageshwar Reddy
- grid.410866.d0000 0004 1803 177XAIG Hospitals, Mindspace Rd, Gachibowli, Hyderabad, Telangana 500032 India
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4
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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: 3] [Impact Index Per Article: 1.0] [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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5
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Feng Y, Tian L. Flexible diagnostic measures and new cut-point selection methods under multiple ordered classes. Pharm Stat 2021; 21:220-240. [PMID: 34449107 DOI: 10.1002/pst.2166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 07/21/2021] [Accepted: 08/01/2021] [Indexed: 11/08/2022]
Abstract
Medical diagnosis is essentially a classification problem and usually it is done with multiple ordered classes. For example, cancer diagnosis might be "non-malignant," "early stage," or "late stage." Therefore, appropriate measures are needed to assess the accuracy of diagnostic markers under multiple ordered classes. However, all existing measures fail to differentiate among some distinctly different biomarkers. This paper presents a multi-step procedure for evaluating biomarker accuracy under multiple ordered classes. This procedure leads to two new flexible overall measures as well as three new cut-point selection methods with great computational ease. The performance of proposed measures and cut-point selection methods are numerically explored via a simulation study. In the end, an ovarian cancer dataset from the Prostate, Lung, Colorectal, and Ovarian cancer study is analyzed. The proposed accuracy measures were estimated for markers CA125 and HE4, and cut-points were estimated for the risk of ovarian malignancy algorithm score.
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Affiliation(s)
- Yingdong Feng
- Department of Biostatistics, University at Buffalo, Buffalo, New York, USA
| | - Lili Tian
- Department of Biostatistics, University at Buffalo, Buffalo, New York, USA
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6
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Bantis LE, Nakas CT, Reiser B. Statistical inference for the difference between two maximized Youden indices obtained from correlated biomarkers. Biom J 2021; 63:1241-1253. [PMID: 33852754 DOI: 10.1002/bimj.202000128] [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: 05/04/2020] [Revised: 12/09/2020] [Accepted: 12/09/2020] [Indexed: 11/07/2022]
Abstract
Currently, there is global interest in deriving new promising cancer biomarkers that could complement or substitute the conventional ones. Clinical decisions can often be based on the cutoff that corresponds to the maximized Youden index when maximum accuracy drives decisions. When more than one classification criteria are measured within the same individuals, correlated measurements arise. In this work, we propose hypothesis tests and confidence intervals for the comparison of two correlated receiver operating characteristic (ROC) curves in terms of their corresponding maximized Youden indices. We explore delta-based techniques under parametric assumptions, or power transformations. Nonparametric kernel-based methods are also examined. We evaluate our approaches through simulations and illustrate them using data from a metabolomic study referring to the detection of pancreatic cancer.
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Affiliation(s)
- Leonidas E Bantis
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Christos T Nakas
- Laboratory of Biometry, School of Agriculture, University of Thessaly, Nea Ionia/Volos, Magnesia, Greece
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Benjamin Reiser
- Department of Statistics, University of Haifa, Haifa, Israel
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7
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Biomarkers in Pancreatic Cancer as Analytic Targets for Nanomediated Imaging and Therapy. MATERIALS 2021; 14:ma14113083. [PMID: 34199998 PMCID: PMC8200189 DOI: 10.3390/ma14113083] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 05/31/2021] [Accepted: 06/02/2021] [Indexed: 12/12/2022]
Abstract
As the increase in therapeutic and imaging technologies is swiftly improving survival chances for cancer patients, pancreatic cancer (PC) still has a grim prognosis and a rising incidence. Practically everything distinguishing for this type of malignancy makes it challenging to treat: no approved method for early detection, extended asymptomatic state, limited treatment options, poor chemotherapy response and dense tumor stroma that impedes drug delivery. We provide a narrative review of our main findings in the field of nanoparticle directed treatment for PC, with a focus on biomarker targeted delivery. By reducing drug toxicity, increasing their tumor accumulation, ability to modulate tumor microenvironment and even improve imaging contrast, it seems that nanotechnology may one day give hope for better outcome in pancreatic cancer. Further conjugating nanoparticles with biomarkers that are overexpressed amplifies the benefits mentioned, with potential increase in survival and treatment response.
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8
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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: 30] [Impact Index Per Article: 10.0] [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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9
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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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10
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Noll S, Furrer R, Reiser B, Nakas CT. Inference in receiver operating characteristic surface analysis via a trinormal model‐based testing approach. Stat (Int Stat Inst) 2020. [DOI: 10.1002/sta4.249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Samuel Noll
- Department of MathematicsUniversity of Zurich Zurich Switzerland
| | - Reinhard Furrer
- Department of MathematicsUniversity of Zurich Zurich Switzerland
- Department of Computational ScienceUniversity of Zurich Zurich Switzerland
| | | | - Christos T. Nakas
- Department of Agriculture, Crop Production and Rural EnvironmentUniversity of Thessaly Volos 38446 Greece
- Department of Clinical ChemistryInselspital, Bern University Hospital, University of Bern Bern Switzerland
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11
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Hua J, Tian L. A comprehensive and comparative review of optimal cut-points selection methods for diseases with multiple ordinal stages. J Biopharm Stat 2019; 30:46-68. [PMID: 31250693 DOI: 10.1080/10543406.2019.1632876] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Cut-points selection is a key topic in the field of diagnostic studies. For binary classification, there exist several well-developed methods, some of which have been extended to three-class settings and beyond. This paper focuses on optimal cut-points selection methods for diseases with multiple ordinal stages. The purpose of this paper is two-fold: 1) to propose three new cut-points selection methods; and 2) to present a comprehensive simulation study to assess and compare the performance of all the available methods. Two real data sets, one from ovarian cancer and the other from pancreatic cancer, are analyzed.
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Affiliation(s)
- Jia Hua
- Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA
| | - Lili Tian
- Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA
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12
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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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13
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Beachler T, Gracz H, Long NM, Borst L, Morgan D, Nebel A, Andrews N, Koipillai J, Frable S, Bembenek Bailey S, Ellis K, Von Dollen K, Lyle S, Gadsby J, Bailey CS. Allantoic Metabolites, Progesterone, and Estradiol-17β Remain Unchanged After Infection in an Experimental Model of Equine Ascending Placentitis. J Equine Vet Sci 2019. [DOI: 10.1016/j.jevs.2018.11.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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14
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Long NP, Yoon SJ, Anh NH, Nghi TD, Lim DK, Hong YJ, Hong SS, Kwon SW. A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. Metabolomics 2018; 14:109. [PMID: 30830397 DOI: 10.1007/s11306-018-1404-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 07/31/2018] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients. OBJECTIVES In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. METHODS PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.
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Affiliation(s)
- Nguyen Phuoc Long
- Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Sang Jun Yoon
- Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Nguyen Hoang Anh
- Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Tran Diem Nghi
- School of Medicine, Vietnam National University, Ho Chi Minh City, 700000, Vietnam
| | - Dong Kyu Lim
- Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Yu Jin Hong
- Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Soon-Sun Hong
- Department of Drug Development, College of Medicine, Inha University, Incheon, 22212, South Korea
| | - Sung Won Kwon
- Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul, 08826, South Korea.
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15
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Zhang X, Shi S, Zhang B, Ni Q, Yu X, Xu J. Circulating biomarkers for early diagnosis of pancreatic cancer: facts and hopes. Am J Cancer Res 2018; 8:332-353. [PMID: 29636993 PMCID: PMC5883088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 02/25/2018] [Indexed: 06/08/2023] Open
Abstract
Pancreatic cancer (PC) is characterized by extremely high mortality and poor prognosis, which are largely ascribed to difficulties in early diagnosis and limited therapeutics. Although there is a sufficient window for intervention before preneoplastic lesions progress to invasive disease, effective early detection of PC remains difficult using current biomarkers and imaging techniques. Biomarkers with satisfactory diagnostic efficacy and convenient analysis methods are urgently required. In this review, we summarized recent advances in the identification of biomarkers in circulation for early detection of PC. A number of novel circulating biomarkers, such as metabolites, cell-free DNA (cfDNA), noncoding RNA, and exosomes, that show promising diagnostic value have been discovered using advances in sequencing techniques and "omics" analyses. Panels comprising several biomarkers may also exhibit better diagnostic performance. In the future, we need more efficient circulating biomarkers for the identification of noninvasive precursor lesions and early disease. Collaborative large-scale studies are also required to show the clinical validity and applicability of potential biomarkers.
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Affiliation(s)
- Xu Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer CenterShanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan UniversityShanghai 200032, China
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer CenterShanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan UniversityShanghai 200032, China
- Pancreatic Cancer Institute, Fudan UniversityShanghai 200032, China
- Shanghai Pancreatic Cancer InstituteShanghai, China
| | - Bo Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer CenterShanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan UniversityShanghai 200032, China
- Pancreatic Cancer Institute, Fudan UniversityShanghai 200032, China
- Shanghai Pancreatic Cancer InstituteShanghai, China
| | - Quanxing Ni
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer CenterShanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan UniversityShanghai 200032, China
- Pancreatic Cancer Institute, Fudan UniversityShanghai 200032, China
- Shanghai Pancreatic Cancer InstituteShanghai, China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer CenterShanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan UniversityShanghai 200032, China
- Pancreatic Cancer Institute, Fudan UniversityShanghai 200032, China
- Shanghai Pancreatic Cancer InstituteShanghai, China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer CenterShanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan UniversityShanghai 200032, China
- Pancreatic Cancer Institute, Fudan UniversityShanghai 200032, China
- Shanghai Pancreatic Cancer InstituteShanghai, China
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16
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Mayerle J, Kalthoff H, Reszka R, Kamlage B, Peter E, Schniewind B, González Maldonado S, Pilarsky C, Heidecke CD, Schatz P, Distler M, Scheiber JA, Mahajan UM, Weiss FU, Grützmann R, Lerch MM. Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis. Gut 2018; 67:128-137. [PMID: 28108468 PMCID: PMC5754849 DOI: 10.1136/gutjnl-2016-312432] [Citation(s) in RCA: 169] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 12/22/2016] [Accepted: 12/26/2016] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose. DESIGN For a case-control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry. RESULTS A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93-0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%-97.0%). In the test set, an AUC of 0.94 (95% CI 0.91-0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%-95.5%) and a specificity of 91.3% (95% CI 82.8%-96.4%) were achieved, successfully validating the biomarker signature. CONCLUSIONS In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%-99.9%) (training set) and 99.8% (95% CI 99.6%-99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9.
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Affiliation(s)
- Julia Mayerle
- Department of Medicine A, University Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany,Medizinische Klinik und Poliklinik II, Klinikum der LMU München-Grosshadern, München, Germany
| | - Holger Kalthoff
- Section for Molecular Oncology, Institut for Experimental Cancer Research (IET), UKSH, Kiel, Germany
| | | | | | | | - Bodo Schniewind
- Section for Molecular Oncology, Institut for Experimental Cancer Research (IET), UKSH, Kiel, Germany
| | | | | | - Claus-Dieter Heidecke
- Department of General, Visceral, Thoracic and Vascular Surgery University Medicine Greifswald, Ernst-Moritz-Arndt University, Greifswald, Germany
| | | | - Marius Distler
- Clinic and Outpatient Clinic for Visceral-, Thorax- and Vascular Surgery, Medizinische Fakultät, TU Dresden, Dresden, Germany
| | - Jonas A Scheiber
- Department of Medicine A, University Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany
| | - Ujjwal M Mahajan
- Department of Medicine A, University Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany,Medizinische Klinik und Poliklinik II, Klinikum der LMU München-Grosshadern, München, Germany
| | - F Ulrich Weiss
- Department of Medicine A, University Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany
| | | | - Markus M Lerch
- Department of Medicine A, University Medicine, Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany
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17
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Zhou B, Xu JW, Cheng YG, Gao JY, Hu SY, Wang L, Zhan HX. Early detection of pancreatic cancer: Where are we now and where are we going? Int J Cancer 2017; 141:231-241. [PMID: 28240774 DOI: 10.1002/ijc.30670] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 01/25/2017] [Accepted: 02/20/2017] [Indexed: 12/11/2022]
Abstract
Pancreatic cancer (PC) is one of the most lethal malignancies. Recent studies indicate that patients with incidentally diagnosed PC have better prognosis than those with symptoms and that there is a sufficient window for early detection. However, effective early diagnosis remains difficult and depends mainly on imaging modalities and the development of screening methodologies with highly sensitive and specific biomarkers. This review summarizes recent advances in effective screening for early diagnosis of PC using imaging modalities and novel molecular biomarkers discovered from various "omics" studies including genomics, epigenomics, non-coding RNA, metabonomics, liquid biopsy (CTC, ctDNA and exosomes) and microbiomes, and their use in body fluids (feces, urine and saliva). Although many biomarkers for early detection of PC have been discovered through various methods, larger scale and rigorous validation is required before their application in the clinic. In addition, more effective and specific biomarkers of PC are urgently needed.
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Affiliation(s)
- Bin Zhou
- Department of Hepatopancreatobiliary Surgery, the Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, 266003, China
| | - Jian-Wei Xu
- Department of General Surgery, Qilu hospital, Shandong University, Jinan, Shandong Province, 250012, China
| | - Yu-Gang Cheng
- Department of General Surgery, Qilu hospital, Shandong University, Jinan, Shandong Province, 250012, China
| | - Jing-Yue Gao
- Department of Basic Medicine, Medical College of Shandong University, Jinan, 250012, China
| | - San-Yuan Hu
- Department of General Surgery, Qilu hospital, Shandong University, Jinan, Shandong Province, 250012, China
| | - Lei Wang
- Department of General Surgery, Qilu hospital, Shandong University, Jinan, Shandong Province, 250012, China
| | - Han-Xiang Zhan
- Department of General Surgery, Qilu hospital, Shandong University, Jinan, Shandong Province, 250012, China
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18
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Conrad TOF, Genzel M, Cvetkovic N, Wulkow N, Leichtle A, Vybiral J, Kutyniok G, Schütte C. Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. BMC Bioinformatics 2017; 18:160. [PMID: 28274197 PMCID: PMC5343371 DOI: 10.1186/s12859-017-1565-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Accepted: 02/24/2017] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. RESULTS We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.
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Affiliation(s)
- Tim O. F. Conrad
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany
- Zuse Institute Berlin, Takustr. 7, Berlin, Germany
| | - Martin Genzel
- Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany
| | - Nada Cvetkovic
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany
| | - Niklas Wulkow
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany
| | - Alexander Leichtle
- Center of Laboratory Medicine, Inselspital - Bern University Hospital, Düsternbrooker Weg 20, Bern, 24105 Switzerland
| | - Jan Vybiral
- Department of Mathematical Analysis, Charles University, Düsternbrooker Weg 20, Prague, Czech Republic
| | - Gitta Kutyniok
- Department of Mathematics, Technische Universität Berlin, Düsternbrooker Weg 20, Berlin, Germany
| | - Christof Schütte
- Department of Mathematics, Freie Universität Berlin, Arnimallee 6, Berlin, Germany
- Zuse Institute Berlin, Takustr. 7, Berlin, Germany
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19
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McConnell YJ, Farshidfar F, Weljie AM, Kopciuk KA, Dixon E, Ball CG, Sutherland FR, Vogel HJ, Bathe OF. Distinguishing Benign from Malignant Pancreatic and Periampullary Lesions Using Combined Use of ¹H-NMR Spectroscopy and Gas Chromatography-Mass Spectrometry. Metabolites 2017; 7:metabo7010003. [PMID: 28098776 PMCID: PMC5372206 DOI: 10.3390/metabo7010003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 12/09/2016] [Accepted: 01/08/2017] [Indexed: 12/13/2022] Open
Abstract
Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary adenocarcinoma could be distinguished from benign masses and biliary strictures. Sera from 157 patients with malignant and benign pancreatic and periampullary lesions were analyzed using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gas chromatography–mass spectrometry (GC-MS). Multivariate projection modeling using SIMCA-P+ software in training datasets (n = 80) was used to generate the best models to differentiate disease states. Models were validated in test datasets (n = 77). The final 1H-NMR spectroscopy and GC-MS metabolomic profiles consisted of 14 and 18 compounds, with AUROC values of 0.74 (SE 0.06) and 0.62 (SE 0.08), respectively. The combination of 1H-NMR spectroscopy and GC-MS metabolites did not substantially improve this performance (AUROC 0.66, SE 0.08). In patients with adenocarcinoma, glutamate levels were consistently higher, while glutamine and alanine levels were consistently lower. Pancreatic and periampullary adenocarcinomas can be distinguished from benign lesions. To further enhance the discriminatory power of metabolomics in this setting, it will be important to identify the metabolomic changes that characterize each of the subclasses of this heterogeneous group of cancers.
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Affiliation(s)
- Yarrow J McConnell
- Department of Oncology, University of Calgary, Calgary, AB T2N 4N2, Canada.
- Department of Surgery, University of Calgary, Calgary, AB T2N 4N2, Canada.
| | - Farshad Farshidfar
- Department of Oncology, University of Calgary, Calgary, AB T2N 4N2, Canada.
| | - Aalim M Weljie
- Department of Biological Sciences, University of Calgary, Calgary, AB T2N 4N2, Canada.
- Department of Pharmacology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Karen A Kopciuk
- Department of Oncology, University of Calgary, Calgary, AB T2N 4N2, Canada.
- Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 4N2, Canada.
| | - Elijah Dixon
- Department of Oncology, University of Calgary, Calgary, AB T2N 4N2, Canada.
- Department of Surgery, University of Calgary, Calgary, AB T2N 4N2, Canada.
| | - Chad G Ball
- Department of Surgery, University of Calgary, Calgary, AB T2N 4N2, Canada.
| | | | - Hans J Vogel
- Department of Biological Sciences, University of Calgary, Calgary, AB T2N 4N2, Canada.
| | - Oliver F Bathe
- Department of Oncology, University of Calgary, Calgary, AB T2N 4N2, Canada.
- Department of Surgery, University of Calgary, Calgary, AB T2N 4N2, Canada.
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20
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Abstract
Metabolomics based on direct mass spectrometry (MS) analysis, either by direct infusion or flow injection of crude sample extracts, shows a great potential for metabolic fingerprinting because of its high-throughput screening capability, wide metabolite coverage and reduced time of analysis. Considering that numerous metabolic pathways are significantly perturbed during the initiation and progression of diseases, these metabolomic tools can be used to get a deeper understanding about disease pathogenesis and discover potential biomarkers for early diagnosis. In this work, we describe the most common metabolomic platforms used in biomedical research, with special focus on strategies based on direct MS analysis. Then, a comprehensive review on the application of direct MS fingerprinting in clinical issues is provided.
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21
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Tumas J, Kvederaviciute K, Petrulionis M, Kurlinkus B, Rimkus A, Sakalauskaite G, Cicenas J, Sileikis A. Metabolomics in pancreatic cancer biomarkers research. Med Oncol 2016; 33:133. [PMID: 27807722 DOI: 10.1007/s12032-016-0853-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 10/27/2016] [Indexed: 12/14/2022]
Abstract
Pancreatic cancer is one of the worst prognoses of all malignancies. More than 40,000 deaths a year from this disease are observed in European Union alone. The only possibly curative treatment of pancreatic cancer is surgery, yet only 15-20% of patients have operable disease and even patients, which go through surgery and adjuvant chemotherapy, survival is less than 30%. The sensitive and specific biomarkers which could be used for the advance of early diagnostics are needed and constantly researched. Metabolomics is a technology which analyzes the concentrations of low-molecular-weight metabolites (the metabolome) has lately effectively developed due to the improvements in analytical technology. Metabolome analysis can be a one of the useful approaches for the biomarker discovery and disease diagnosis. Here we discuss recent discoveries in the field of pancreatic cancer metabolomics as well as the most promising biomarkers for diagnostics, prognosis and prediction.
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Affiliation(s)
- Jaroslav Tumas
- Center of Abdominal Surgery, Vilnius University Hospital, Santariskiu Klinikos Santariskiu str. 2, 08661, Vilnius, Lithuania
| | - Kotryna Kvederaviciute
- Institute of Biotechnology, Vilnius University, Saulėtekio ave. 7, 01222, Vilnius, Lithuania
| | - Marius Petrulionis
- Center of Abdominal Surgery, Vilnius University Hospital, Santariskiu Klinikos Santariskiu str. 2, 08661, Vilnius, Lithuania
| | - Benediktas Kurlinkus
- Center of Hepatology, Gastroenterology and Dietology, Vilnius University Hospital, Santariskiu Klinikos Santariskiu str. 2, 08661, Vilnius, Lithuania
| | - Arnas Rimkus
- Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | | | - Jonas Cicenas
- Vetsuisse Faculty, Institute of Animal Pathology, University of Bern, 3012, Bern, Switzerland. .,MAP Kinase Resource, Bioinformatics, Melchiorstrasse 9, 3027, Bern, Switzerland. .,Proteomics Centre, Institute of Biochemistry, Vilnius University, 08662, Vilnius, Lithuania.
| | - Audrius Sileikis
- Center of Abdominal Surgery, Vilnius University Hospital, Santariskiu Klinikos Santariskiu str. 2, 08661, Vilnius, Lithuania. .,Center of Hepatology, Gastroenterology and Dietology, Vilnius University Hospital, Santariskiu Klinikos Santariskiu str. 2, 08661, Vilnius, Lithuania.
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22
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Sakai A, Suzuki M, Kobayashi T, Nishiumi S, Yamanaka K, Hirata Y, Nakagawa T, Azuma T, Yoshida M. Pancreatic cancer screening using a multiplatform human serum metabolomics system. Biomark Med 2016; 10:577-86. [DOI: 10.2217/bmm-2016-0020] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Aim: To examine a novel screening method for pancreatic cancer involving gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry-based metabolomics analysis. Materials & methods: Sera from pancreatic cancer patients (n = 59) and healthy volunteers (n = 59) were allocated to the training set or validation set. Serum metabolome analysis was carried out using our multiplatform metabolomics system. A diagnostic model was constructed using a two-phase screening method that was newly advocated. Results: When the training set was used, the constructed diagnostic model exhibited high sensitivity (100%) and specificity (80%) for pancreatic cancer. When the validation set was used, the model displayed high sensitivity (84.1%) and specificity (84.1%). Conclusion: We successfully developed a diagnostic model for pancreatic cancer using a multiplatform serum metabolomics system.
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Affiliation(s)
- Arata Sakai
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Makoto Suzuki
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Takashi Kobayashi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Shin Nishiumi
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Kodai Yamanaka
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Yuichi Hirata
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Takashi Nakagawa
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Takeshi Azuma
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
| | - Masaru Yoshida
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
- Division of Metabolomics Research, Department of Internal Related, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
- AMED-CREST, AMED, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, Hyogo 650-0017, Japan
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23
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Yang Q, Sun J, Chen YQ. Multi-dimensional, comprehensive sample extraction combined with LC-GC/MS analysis for complex biological samples: application in the metabolomics study of acute pancreatitis. RSC Adv 2016. [DOI: 10.1039/c5ra26708k] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Multi-dimensional sample extraction and optimal LC-GC/MS were combined to obtain as much sample information as possible for metabolomics applications.
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Affiliation(s)
- Qin Yang
- State Key Laboratory of Food Science and Technology
- School of Food Science and Technology
- Jiangnan University
- Wuxi
- China
| | - Jia Sun
- State Key Laboratory of Food Science and Technology
- School of Food Science and Technology
- Jiangnan University
- Wuxi
- China
| | - Yong Q. Chen
- State Key Laboratory of Food Science and Technology
- School of Food Science and Technology
- Jiangnan University
- Wuxi
- China
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24
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Abstract
Metabonomic techniques have considerable potential in the field of clinical diagnostics, typifying the application of a translational research paradigm. Care must be taken at all stages to apply appropriate methodology with accurate patient selection and profiling, and rigorous data acquisition and handling, to ensure clinical validity.An ever-increasing number of publications in a wide range of diseases and diverse patient groups suggest a variety of potential clinical uses; prospective studies in large validation cohorts are required to bring metabonomics into routine clinical practice. In this chapter, the utility of metabonomics as a diagnostic tool will be discussed.
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Affiliation(s)
- Lucy C Hicks
- Department of Medicine, Imperial College London, London, UK
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Fukutake N, Ueno M, Hiraoka N, Shimada K, Shiraishi K, Saruki N, Ito T, Yamakado M, Ono N, Imaizumi A, Kikuchi S, Yamamoto H, Katayama K. A Novel Multivariate Index for Pancreatic Cancer Detection Based On the Plasma Free Amino Acid Profile. PLoS One 2015; 10:e0132223. [PMID: 26133769 PMCID: PMC4489861 DOI: 10.1371/journal.pone.0132223] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Accepted: 06/12/2015] [Indexed: 11/21/2022] Open
Abstract
Background The incidence of pancreatic cancer (PC) continues to increase in the world, while most patients are diagnosed with advanced stages and survive <12 months. This poor prognosis is attributable to difficulty of early detection. Here we developed and evaluated a multivariate index composed of plasma free amino acids (PFAAs) for early detection of PC. Methods We conducted a cross-sectional study in multi-institutions in Japan. Fasting plasma samples from PC patients (n = 360), chronic pancreatitis (CP) patients (n = 28), and healthy control (HC) subjects (n = 8372) without apparent cancers who were undergoing comprehensive medical examinations were collected. Concentrations of 19 PFAAs were measured by liquid chromatography–mass spectrometry. We generated an index consisting of the following six PFAAs: serine, asparagine, isoleucine, alanine, histidine, and tryptophan as variables for discrimination in a training set (120 PC and matching 600 HC) and evaluation in a validation set (240 PC, 28 CP, and 7772 HC). Results Several amino acid concentrations in plasma were significantly altered in PC. Plasma tryptophan and histidine concentrations in PC were particularly low, while serine was particularly higher than that of HC. The area under curve (AUC) based on receiver operating characteristic (ROC) curve analysis of the resulting index to discriminate PC from HC were 0.89 [95% confidence interval (CI), 0.86–0.93] in the training set. In the validation set, AUCs based on ROC curve analysis of the PFAA index were 0.86 (95% CI, 0.84–0.89) for all PC patients versus HC subjects, 0.81 (95% CI, 0.75–0.86) for PC patients from stage IIA to IIB versus HC subjects, and 0.87 (95% CI, 0.80–0.93) for all PC patients versus CP patients. Conclusions These findings suggest that the PFAA profile of PC was significantly different from that of HC. The PFAA index is a promising biomarker for screening and diagnosis of PC.
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Affiliation(s)
- Nobuyasu Fukutake
- Department of Hepatobiliary and Pancreatic Oncology, Osaka Medical Center of Cancer and Cardiovascular Diseases, Osaka, Japan
| | - Makoto Ueno
- Division of Hepatobiliary and Pancreatic Medical Oncology, Kanagawa Cancer Center, Kanagawa, Japan
| | - Nobuyoshi Hiraoka
- Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan
| | - Kazuaki Shimada
- Hepatobiliary and Pancreatic Surgery Division, National Cancer Center Hospital, Tokyo, Japan
| | - Koichi Shiraishi
- Division of Gastroenterology, Department of Internal Medicine, Tokai University Oiso Hospital, Kanagawa, Japan
| | - Nobuhiro Saruki
- Department of Anesthesia, Gunma Prefectural Cancer Center, Gunma, Japan
| | - Toshifumi Ito
- Department of Gastroenterology and Hepatology, Japan Community Healthcare Organization (JCHO), Osaka Hospital, Osaka, Japan
| | - Minoru Yamakado
- Center for Multiphasic Health Testing and Services, Mitsui Memorial Hospital, Tokyo, Japan
| | - Nobukazu Ono
- Institute for Innovation, Ajinomoto Co., Inc., Kanagawa, Japan
- * E-mail: (NO); (KK)
| | - Akira Imaizumi
- Institute for Innovation, Ajinomoto Co., Inc., Kanagawa, Japan
| | - Shinya Kikuchi
- Institute for Innovation, Ajinomoto Co., Inc., Kanagawa, Japan
| | | | - Kazuhiro Katayama
- Department of Hepatobiliary and Pancreatic Oncology, Osaka Medical Center of Cancer and Cardiovascular Diseases, Osaka, Japan
- * E-mail: (NO); (KK)
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Zhang L, Li M, Zhan L, Lu X, Liang L, Su B, Sui H, Gao Z, Li Y, Liu Y, Wu B, Liu Q. Plasma metabolomic profiling of patients with diabetes-associated cognitive decline. PLoS One 2015; 10:e0126952. [PMID: 25974350 PMCID: PMC4431856 DOI: 10.1371/journal.pone.0126952] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 04/09/2015] [Indexed: 12/16/2022] Open
Abstract
Diabetes related cognitive dysfunction (DACD), one of the chronic complications of diabetes, seriously affect the quality of life in patients and increase family burden. Although the initial stage of DACD can lead to metabolic alterations or potential pathological changes, DACD is difficult to diagnose accurately. Moreover, the details of the molecular mechanism of DACD remain somewhat elusive. To understand the pathophysiological changes that underpin the development and progression of DACD, we carried out a global analysis of metabolic alterations in response to DACD. The metabolic alterations associated with DACD were first investigated in humans, using plasma metabonomics based on high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis. The related pathway of each metabolite of interest was searched in database online. The network diagrams were established KEGGSOAP software package. Receiver operating characteristic (ROC) analysis was used to evaluate diagnostic accuracy of metabolites. This is the first report of reliable biomarkers of DACD, which were identified using an integrated strategy. The identified biomarkers give new insights into the pathophysiological changes and molecular mechanisms of DACD. The disorders of sphingolipids metabolism, bile acids metabolism, and uric acid metabolism pathway were found in T2DM and DACD. On the other hand, differentially expressed plasma metabolites offer unique metabolic signatures for T2DM and DACD patients. These are potential biomarkers for disease monitoring and personalized medication complementary to the existing clinical modalities.
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Affiliation(s)
- Lin Zhang
- Academy of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China
| | - Meng Li
- Academy of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China
| | - Libin Zhan
- Department of Traditional Chinese Medicine, the Second Affiliated Hospital, Dalian Medical University, Dalian, Liaoning, China; Academy of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China
| | - Xiaoguang Lu
- Department of Emergency Medicine, Zhongshan Hospital, Dalian University, Dalian, Liaoning, China
| | - Lina Liang
- Academy of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China
| | - Benli Su
- Department of endocrinology, the Second Affiliated Hospital, Dalian Medical University, Dalian, Liaoning, China
| | - Hua Sui
- Academy of Integrative Medicine, Dalian Medical University, Dalian, Liaoning, China
| | - Zhengnan Gao
- Department of endocrinology, Dalian Municipal Central Hospital Affillated of Dalian Medical University, Dalian, Liaoning, China
| | - Yuzhong Li
- Examination Department, the Second Affiliated Hospital, Dalian Medical University, Dalian, Liaoning, China
| | - Ying Liu
- Medical Examination Center, the Second Affiliated Hospital, Dalian Medical University, Dalian, Liaoning, China
| | - Benhui Wu
- Medical Examination Center, the Second Affiliated Hospital, Dalian Medical University, Dalian, Liaoning, China
| | - Qigui Liu
- Public Health, Dalian Medical University, Dalian, Liaoning, China
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Xiang J, Liu L, Wang W, Xu H, Wu C, Xu J, Liu C, Long J, Ni Q, Yu X. Metabolic tumor burden: A new promising way to reach precise personalized therapy in PDAC. Cancer Lett 2015; 359:165-8. [DOI: 10.1016/j.canlet.2015.01.023] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 01/18/2015] [Accepted: 01/19/2015] [Indexed: 02/06/2023]
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Olivares O, Vasseur S. Metabolic rewiring of pancreatic ductal adenocarcinoma: New routes to follow within the maze. Int J Cancer 2015; 138:787-96. [PMID: 25732227 DOI: 10.1002/ijc.29501] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Revised: 02/10/2015] [Accepted: 02/17/2015] [Indexed: 12/13/2022]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a debilitating and almost universally fatal malignancy. Despite advances in understanding of the oncogenetics of the disease, very few clinical benefits have been shown. One of the main characteristics of PDAC is the tumor architecture where tumor cells are surrounded by a firm desmoplasia. By reducing vascularization, thus both oxygen and nutrients delivery to the tumor, this stroma causes the appearance of hypoxic zones driving metabolic adaptation in surviving tumor cells in order to cope with challenging conditions. This metabolic reprogramming promoted by environmental constraints enhances PDAC aggressiveness. In this review, we provide a brief overview of previous works regarding the importance of glucose and glutamine addiction of PDAC cells. In particular we aim to highlight the need for exploring the impact of metabolites other than glucose and glutamine, such as non-essential amino acids and oncometabolites, to find new treatments. We also discuss the need for progress in methodology for metabolites detection. The overall purpose of our review is to emphasize the need to look beyond what is currently known, with a focus on amino acid availability, in order to improve our understanding of PDAC biology.
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Affiliation(s)
- Orianne Olivares
- INSERM U1068, Centre De Recherche En Cancérologie De Marseille (CRCM), F-13009, Marseille, France.,Institut Paoli-Calmettes, F-13009, Marseille, France.,CNRS, UMR7258, CRCM, F-13009, Marseille, France.,Université Aix-Marseille, F-13284, Marseille, France
| | - Sophie Vasseur
- INSERM U1068, Centre De Recherche En Cancérologie De Marseille (CRCM), F-13009, Marseille, France.,Institut Paoli-Calmettes, F-13009, Marseille, France.,CNRS, UMR7258, CRCM, F-13009, Marseille, France.,Université Aix-Marseille, F-13284, Marseille, France
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Patel S, Ahmed S. Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery. J Pharm Biomed Anal 2014; 107:63-74. [PMID: 25569286 DOI: 10.1016/j.jpba.2014.12.020] [Citation(s) in RCA: 122] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Revised: 12/07/2014] [Accepted: 12/14/2014] [Indexed: 02/07/2023]
Abstract
Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.
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Affiliation(s)
- Seema Patel
- Bioinformatics and Medical Informatics Research Center, San Diego State University, San Diego 92182, USA.
| | - Shadab Ahmed
- Institute of Bioinformatics and Biotechnology, Savitribai Phule Pune University, Pune 411007, India
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Søreide K, Sund M. Epidemiological-molecular evidence of metabolic reprogramming on proliferation, autophagy and cell signaling in pancreas cancer. Cancer Lett 2014; 356:281-8. [PMID: 24704294 DOI: 10.1016/j.canlet.2014.03.028] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2014] [Revised: 02/28/2014] [Accepted: 03/25/2014] [Indexed: 02/07/2023]
Abstract
Pancreatic cancer remains one of the deadliest human cancers with little progress made in survival over the past decades, and 5-year survival usually below 5%. Despite this dismal scenario, progresses have been made in understanding of the underlying tumor biology through among other definition of precursor lesions, delineation of molecular pathways, and advances in genome-wide technology. Further, exploring the relationship between epidemiological risk factors involving metabolic features to that of an altered cancer metabolism may provide the foundation for new therapies. Here we explore how nutrients and caloric intake may influence the KRAS-driven ductal carcinogenesis through mediators of metabolic stress, including autophagy in presence of TP53, advanced glycation end products (AGE) and the receptors (RAGE) and ligands (HMGB1), as well as glutamine pathways, among others. Effective understanding the cancer metabolism mechanisms in pancreatic cancer may propose new ways of prevention and treatment.
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Affiliation(s)
- Kjetil Søreide
- Department of Gastrointestinal Surgery, Stavanger University Hospital, Stavanger, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Malin Sund
- Department of Surgical and Perioperative Sciences, Surgery, Umeå University, Umeå, Sweden
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