1
|
Borgmästars E, Ulfenborg B, Johansson M, Jonsson P, Billing O, Franklin O, Lundin C, Jacobson S, Simm M, Lubovac-Pilav Z, Sund M. Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study. Transl Oncol 2024; 48:102059. [PMID: 39018772 DOI: 10.1016/j.tranon.2024.102059] [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: 04/05/2024] [Revised: 05/20/2024] [Accepted: 07/05/2024] [Indexed: 07/19/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.
Collapse
Affiliation(s)
- Emmy Borgmästars
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden.
| | - Benjamin Ulfenborg
- School of Bioscience, Department of Biology and Bioinformatics, University of Skövde, Skövde, Sweden
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Pär Jonsson
- Department of Chemistry, Umeå University, Umeå, Sweden
| | - Ola Billing
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden
| | - Oskar Franklin
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Division of Surgical Oncology, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Christina Lundin
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden
| | - Sara Jacobson
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden
| | - Maja Simm
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Department of Clinical Sciences/ Obstetrics and Gynecology, Umeå University, Umeå, Sweden
| | - Zelmina Lubovac-Pilav
- School of Bioscience, Department of Biology and Bioinformatics, University of Skövde, Skövde, Sweden
| | - Malin Sund
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| |
Collapse
|
2
|
Taunk K, Jajula S, Bhavsar PP, Choudhari M, Bhanuse S, Tamhankar A, Naiya T, Kalita B, Rapole S. The prowess of metabolomics in cancer research: current trends, challenges and future perspectives. Mol Cell Biochem 2024:10.1007/s11010-024-05041-w. [PMID: 38814423 DOI: 10.1007/s11010-024-05041-w] [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: 12/21/2023] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.
Collapse
Affiliation(s)
- Khushman Taunk
- Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune, Maharashtra, 411007, India
- Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, West Bengal, NH12 Simhat, Haringhata, Nadia, West Bengal, 741249, India
| | - Saikiran Jajula
- Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune, Maharashtra, 411007, India
| | - Praneeta Pradip Bhavsar
- Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune, Maharashtra, 411007, India
| | - Mahima Choudhari
- Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune, Maharashtra, 411007, India
| | - Sadanand Bhanuse
- Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune, Maharashtra, 411007, India
| | - Anup Tamhankar
- Department of Surgical Oncology, Deenanath Mangeshkar Hospital and Research Centre, Erandawne, Pune, Maharashtra, 411004, India
| | - Tufan Naiya
- Department of Biotechnology, Maulana Abul Kalam Azad University of Technology, West Bengal, NH12 Simhat, Haringhata, Nadia, West Bengal, 741249, India
| | - Bhargab Kalita
- Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune, Maharashtra, 411007, India.
- Amrita School of Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Ponekkara, Kochi, Kerala, 682041, India.
| | - Srikanth Rapole
- Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune, Maharashtra, 411007, India.
| |
Collapse
|
3
|
Jain SK, Bansal S, Bansal S, Singh B, Klotzbier W, Mehta KY, Cheema AK. An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer. Int J Mol Sci 2024; 25:5901. [PMID: 38892088 PMCID: PMC11172734 DOI: 10.3390/ijms25115901] [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: 04/06/2024] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024] Open
Abstract
Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC-MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.
Collapse
Affiliation(s)
- Shreyans K. Jain
- Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University Medical Center, E-415, New Research Building, 3900 Reservoir Road NW, Washington, DC 20057, USA; (S.K.J.); (S.B.); (S.B.); (B.S.); (W.K.); (K.Y.M.)
| | - Shivani Bansal
- Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University Medical Center, E-415, New Research Building, 3900 Reservoir Road NW, Washington, DC 20057, USA; (S.K.J.); (S.B.); (S.B.); (B.S.); (W.K.); (K.Y.M.)
| | - Sunil Bansal
- Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University Medical Center, E-415, New Research Building, 3900 Reservoir Road NW, Washington, DC 20057, USA; (S.K.J.); (S.B.); (S.B.); (B.S.); (W.K.); (K.Y.M.)
| | - Baldev Singh
- Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University Medical Center, E-415, New Research Building, 3900 Reservoir Road NW, Washington, DC 20057, USA; (S.K.J.); (S.B.); (S.B.); (B.S.); (W.K.); (K.Y.M.)
| | - William Klotzbier
- Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University Medical Center, E-415, New Research Building, 3900 Reservoir Road NW, Washington, DC 20057, USA; (S.K.J.); (S.B.); (S.B.); (B.S.); (W.K.); (K.Y.M.)
| | - Khyati Y. Mehta
- Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University Medical Center, E-415, New Research Building, 3900 Reservoir Road NW, Washington, DC 20057, USA; (S.K.J.); (S.B.); (S.B.); (B.S.); (W.K.); (K.Y.M.)
| | - Amrita K. Cheema
- Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University Medical Center, E-415, New Research Building, 3900 Reservoir Road NW, Washington, DC 20057, USA; (S.K.J.); (S.B.); (S.B.); (B.S.); (W.K.); (K.Y.M.)
- Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Centre, Washington, DC 20057, USA
| |
Collapse
|
4
|
Dorrani M, Zhao J, Bekhti N, Trimigno A, Min S, Ha J, Han A, O’Day E, Kamphorst JJ. Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery. Metabolites 2024; 14:280. [PMID: 38786757 PMCID: PMC11123370 DOI: 10.3390/metabo14050280] [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: 04/11/2024] [Revised: 05/02/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.
Collapse
Affiliation(s)
- Masoumeh Dorrani
- Olaris, Inc., 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA; (M.D.); (J.Z.); (N.B.); (A.T.); (E.O.)
| | - Jifang Zhao
- Olaris, Inc., 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA; (M.D.); (J.Z.); (N.B.); (A.T.); (E.O.)
| | - Nihel Bekhti
- Olaris, Inc., 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA; (M.D.); (J.Z.); (N.B.); (A.T.); (E.O.)
| | - Alessia Trimigno
- Olaris, Inc., 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA; (M.D.); (J.Z.); (N.B.); (A.T.); (E.O.)
| | - Sangil Min
- Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; (S.M.); (J.H.); (A.H.)
| | - Jongwon Ha
- Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; (S.M.); (J.H.); (A.H.)
| | - Ahram Han
- Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; (S.M.); (J.H.); (A.H.)
| | - Elizabeth O’Day
- Olaris, Inc., 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA; (M.D.); (J.Z.); (N.B.); (A.T.); (E.O.)
| | - Jurre J. Kamphorst
- Olaris, Inc., 175 Crossing Boulevard Suite 410, Framingham, MA 01702, USA; (M.D.); (J.Z.); (N.B.); (A.T.); (E.O.)
| |
Collapse
|
5
|
Anh NH, Long NP, Min YJ, Ki Y, Kim SJ, Jung CW, Park S, Kwon SW, Lee SJ. Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis. Metabolites 2023; 13:1112. [PMID: 37999208 PMCID: PMC10672761 DOI: 10.3390/metabo13111112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/20/2023] [Accepted: 10/24/2023] [Indexed: 11/25/2023] Open
Abstract
Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.
Collapse
Affiliation(s)
- Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Young Jin Min
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Yujin Ki
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul 08826, Republic of Korea; (Y.K.); (S.P.)
| | - Sun Jo Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Cheol Woon Jung
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Seongoh Park
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul 08826, Republic of Korea; (Y.K.); (S.P.)
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Seul Ji Lee
- College of Pharmacy, Kangwon National University, Chuncheon 24341, Republic of Korea
| |
Collapse
|
6
|
Perazzoli G, García-Valdeavero OM, Peña M, Prados J, Melguizo C, Jiménez-Luna C. Evaluating Metabolite-Based Biomarkers for Early Diagnosis of Pancreatic Cancer: A Systematic Review. Metabolites 2023; 13:872. [PMID: 37512579 PMCID: PMC10384620 DOI: 10.3390/metabo13070872] [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: 06/10/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with five-year survival rates around 10%. The only curative option remains complete surgical resection, but due to the delay in diagnosis, less than 20% of patients are eligible for surgery. Therefore, discovering diagnostic biomarkers for early detection is crucial for improving clinical outcomes. Metabolomics has become a powerful technology for biomarker discovery, and several metabolomic-based panels have been proposed for PDAC diagnosis, but these advances have not yet been translated into the clinic. Therefore, this review focused on summarizing metabolites identified for the early diagnosis of PDAC in the last five years. Bibliographic searches were performed in the PubMed, Scopus and WOS databases, using the terms "Biomarkers, Tumor", "Pancreatic Neoplasms", "Early Diagnosis", "Metabolomics" and "Lipidome" (January 2018-March 2023), and resulted in the selection of fourteen original studies that compared PDAC patients with subjects with other pancreatic diseases. These investigations showed amino acid and lipid metabolic pathways as the most commonly altered, reflecting their potential for biomarker research. Furthermore, other relevant metabolites such as glucose and lactate were detected in the pancreas tissue and body fluids from PDAC patients. Our results suggest that the use of metabolomics remains a robust approach to improve the early diagnosis of PDAC. However, these studies showed heterogeneity with respect to the metabolomics techniques used and further studies will be needed to validate the clinical utility of these biomarkers.
Collapse
Affiliation(s)
- Gloria Perazzoli
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Olga M García-Valdeavero
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
| | - Mercedes Peña
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Jose Prados
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Consolación Melguizo
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| | - Cristina Jiménez-Luna
- Institute of Biopathology and Regenerative Medicine (IBIMER), Center of Biomedical Research (CIBM), University of Granada, 18100 Granada, Spain
- Department of Anatomy and Embryology, Faculty of Medicine, University of Granada, 18071 Granada, Spain
- Instituto Biosanitario de Granada (ibs.GRANADA), 18014 Granada, Spain
| |
Collapse
|
7
|
Babic A, Rosenthal MH, Sundaresan TK, Khalaf N, Lee V, Brais LK, Loftus M, Caplan L, Denning S, Gurung A, Harrod J, Schawkat K, Yuan C, Wang QL, Lee AA, Biller LH, Yurgelun MB, Ng K, Nowak JA, Aguirre AJ, Bhatia SN, Vander Heiden MG, Van Den Eeden SK, Caan BJ, Wolpin BM. Adipose tissue and skeletal muscle wasting precede clinical diagnosis of pancreatic cancer. Nat Commun 2023; 14:4317. [PMID: 37463915 PMCID: PMC10354105 DOI: 10.1038/s41467-023-40024-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 07/04/2023] [Indexed: 07/20/2023] Open
Abstract
Patients with pancreatic cancer commonly develop weight loss and muscle wasting. Whether adipose tissue and skeletal muscle losses begin before diagnosis and the potential utility of such losses for earlier cancer detection are not well understood. We quantify skeletal muscle and adipose tissue areas from computed tomography (CT) imaging obtained 2 months to 5 years before cancer diagnosis in 714 pancreatic cancer cases and 1748 matched controls. Adipose tissue loss is identified up to 6 months, and skeletal muscle wasting is identified up to 18 months before the clinical diagnosis of pancreatic cancer and is not present in the matched control population. Tissue losses are of similar magnitude in cases diagnosed with localized compared with metastatic disease and are not correlated with at-diagnosis circulating levels of CA19-9. Skeletal muscle wasting occurs in the 1-2 years before pancreatic cancer diagnosis and may signal an upcoming diagnosis of pancreatic cancer.
Collapse
Affiliation(s)
- Ana Babic
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michael H Rosenthal
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Natalia Khalaf
- Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E. DeBakey Veterans Affairs Medical Center; Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Valerie Lee
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lauren K Brais
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Maureen Loftus
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leah Caplan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sarah Denning
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anamol Gurung
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Joanna Harrod
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Khoschy Schawkat
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Chen Yuan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Qiao-Li Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
| | - Alice A Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Leah H Biller
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Matthew B Yurgelun
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jonathan A Nowak
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew J Aguirre
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sangeeta N Bhatia
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Wyss Institute, Harvard University, Boston, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
| | - Matthew G Vander Heiden
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Bette J Caan
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
8
|
Skubisz K, Dąbkowski K, Samborowska E, Starzyńska T, Deskur A, Ambrozkiewicz F, Karczmarski J, Radkiewicz M, Kusnierz K, Kos-Kudła B, Sulikowski T, Cybula P, Paziewska A. Serum Metabolite Biomarkers for Pancreatic Tumors: Neuroendocrine and Pancreatic Ductal Adenocarcinomas-A Preliminary Study. Cancers (Basel) 2023; 15:3242. [PMID: 37370852 DOI: 10.3390/cancers15123242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/02/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Pancreatic cancer is the most common pancreatic solid malignancy with an aggressive clinical course and low survival rate. There are a limited number of reliable prognostic biomarkers and a need to understand the pathogenesis of pancreatic tumors; neuroendocrine (PNET) and pancreatic ductal adenocarcinomas (PDAC) encouraged us to analyze the serum metabolome of pancreatic tumors and disturbances in the metabolism of PDAC and PNET. METHODS Using the AbsoluteIDQ® p180 kit (Biocrates Life Sciences AG, Innsbruck, Austria) with liquid chromatography-mass spectrometry (LC-MS), we identified changes in metabolite profiles and disrupted metabolic pathways serum of NET and PDAC patients. RESULTS The concentration of six metabolites showed statistically significant differences between the control group and PDAC patients (p.adj < 0.05). Glutamine (Gln), acetylcarnitine (C2), and citrulline (Cit) presented a lower concentration in the serum of PDAC patients, while phosphatidylcholine aa C32:0 (PC aa C32:0), sphingomyelin C26:1 (SM C26:1), and glutamic acid (Glu) achieved higher concentrations compared to serum samples from healthy individuals. Five of the tested metabolites: C2 (FC = 8.67), and serotonin (FC = 2.68) reached higher concentration values in the PNET serum samples compared to PDAC, while phosphatidylcholine aa C34:1 (PC aa C34:1) (FC = -1.46 (0.68)) had a higher concentration in the PDAC samples. The area under the curves (AUC) of the receiver operating characteristic (ROC) curves presented diagnostic power to discriminate pancreatic tumor patients, which were highest for acylcarnitines: C2 with AUC = 0.93, serotonin with AUC = 0.85, and PC aa C34:1 with AUC = 0.86. CONCLUSIONS The observations presented provide better insight into the metabolism of pancreatic tumors, and improve the diagnosis and classification of tumors. Serum-circulating metabolites can be easily monitored without invasive procedures and show the present clinical patients' condition, helping with pharmacological treatment or dietary strategies.
Collapse
Affiliation(s)
- Karolina Skubisz
- Institute of Health Sciences, Faculty of Medical and Health Sciences, Siedlce University of Natural Sciences and Humanities, 08-110 Siedlce, Poland
- Department of Laboratory Diagnostics and Clinical Immunology of Developmental Age, Pediatric Hospital of Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Krzysztof Dąbkowski
- Department of Gastroenterology, Pomeranian Medical University in Szczecin, 70-204 Szczecin, Poland
| | - Emilia Samborowska
- Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Teresa Starzyńska
- Department of Gastroenterology, Pomeranian Medical University in Szczecin, 70-204 Szczecin, Poland
| | - Anna Deskur
- Department of Gastroenterology, Pomeranian Medical University in Szczecin, 70-204 Szczecin, Poland
| | - Filip Ambrozkiewicz
- Laboratory of Translational Cancer Genomics, Biomedical Center, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 1665/76, 32300 Pilsen, Czech Republic
| | - Jakub Karczmarski
- Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Mariusz Radkiewicz
- Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
| | - Katarzyna Kusnierz
- The Department of Gastrointestinal Surgery, Medical University of Silesia, 40-752 Katowice, Poland
| | - Beata Kos-Kudła
- Department of Endocrinology and Neuroendocrine Tumours, Department of Pathophysiology and Endocrinology, Medical University of Silesia, 40-752 Katowice, Poland
| | - Tadeusz Sulikowski
- Department of General, Minimally Invasive and Gastroenterological Surgery, Pomeranian Medical University in Szczecin, 70-204 Szczecin, Poland
| | - Patrycja Cybula
- Institute of Health Sciences, Faculty of Medical and Health Sciences, Siedlce University of Natural Sciences and Humanities, 08-110 Siedlce, Poland
- Molecular Biology Laboratory, Department of Diagnostic Hematology, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
| | - Agnieszka Paziewska
- Institute of Health Sciences, Faculty of Medical and Health Sciences, Siedlce University of Natural Sciences and Humanities, 08-110 Siedlce, Poland
| |
Collapse
|
9
|
Wang D, Zhu J, Li N, Lu H, Gao Y, Zhuang L, Chen Z, Mao W. GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma. PeerJ 2023; 11:e15302. [PMID: 37220527 PMCID: PMC10200095 DOI: 10.7717/peerj.15302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 04/05/2023] [Indexed: 05/25/2023] Open
Abstract
Background Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma. Methods By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers. Results Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid. Conclusions To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.
Collapse
Affiliation(s)
- Ding Wang
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
- Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, China
| | - Jing Zhu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, China
| | - Na Li
- Shaoxing No. 2 Hospital Medical Community General Hospital, Shaoxing, China
| | - Hongyang Lu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, China
| | - Yun Gao
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, China
| | - Lei Zhuang
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
- Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, China
| | - Zhongjian Chen
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, China
| | - Weimin Mao
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, China
| |
Collapse
|
10
|
Camps J, Iftimie S, Arenas M, Castañé H, Jiménez-Franco A, Castro A, Joven J. Paraoxonase-1: How a xenobiotic detoxifying enzyme has become an actor in the pathophysiology of infectious diseases and cancer. Chem Biol Interact 2023; 380:110553. [PMID: 37201624 DOI: 10.1016/j.cbi.2023.110553] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/08/2023] [Accepted: 05/15/2023] [Indexed: 05/20/2023]
Abstract
Both infectious and non-infectious diseases can share common molecular mechanisms, including oxidative stress and inflammation. External factors, such as bacterial or viral infections, excessive calorie intake, inadequate nutrients, or environmental factors, can cause metabolic disorders, resulting in an imbalance between free radical production and natural antioxidant systems. These factors may lead to the production of free radicals that can oxidize lipids, proteins, and nucleic acids, causing metabolic alterations that influence the pathogenesis of the disease. The relationship between oxidation and inflammation is crucial, as they both contribute to the development of cellular pathology. Paraoxonase 1 (PON1) is a vital enzyme in regulating these processes. PON1 is an enzyme that is bound to high-density lipoproteins and protects the organism against oxidative stress and toxic substances. It breaks down lipid peroxides in lipoproteins and cells, enhances the protection of high-density lipoproteins against different infectious agents, and is a critical component of the innate immune system. Impaired PON1 function can affect cellular homeostasis pathways and cause metabolically driven chronic inflammatory states. Therefore, understanding these relationships can help to improve treatments and identify new therapeutic targets. This review also examines the advantages and disadvantages of measuring serum PON1 levels in clinical settings, providing insight into the potential clinical use of this enzyme.
Collapse
Affiliation(s)
| | | | - Meritxell Arenas
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
| | | | | | | | | |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Rischke S, Hahnefeld L, Burla B, Behrens F, Gurke R, Garrett TJ. Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects. J Mass Spectrom Adv Clin Lab 2023; 28:47-55. [PMID: 36872952 PMCID: PMC9982001 DOI: 10.1016/j.jmsacl.2023.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
Collapse
Key Words
- (U)HPLC (Ultra-), High pressure liquid chromatography
- Biomarker Discovery Study
- HILIC, Hydrophilic interaction liquid chromatography
- HRMS, High resolution mass spectrometry
- LC-MS, Liquid chromatography – mass spectrometry
- LC-MS-Based Clinical Research
- Lipidomics
- MRM, Multiple reaction monitoring
- Metabolomics
- PCA, Principal component analysis
- QA, Quality assurance
- QC, Quality control
- RF, Random Forest
- RP, Reversed phase
- SVA, Support vector machine
Collapse
Affiliation(s)
- S Rischke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - L Hahnefeld
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - B Burla
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - F Behrens
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.,Division of Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - R Gurke
- pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
| | - T J Garrett
- Department of Pathology, Immunology and Laboratory Medicine and Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL 32611, USA
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022; 146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 02/07/2023]
Abstract
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable architecture with proven cancer prevention estimate and provision for early diagnosis of cancer is the need of hours. Advent of machine learning methods enriched cancer diagnosis area with its overwhelmed efficiency & low error-rate then humans. A significant revolution has been witnessed in the development of machine learning & deep learning assisted system for segmentation & classification of various cancers during past decade. This research paper includes a review of various types of cancer detection via different data modalities using machine learning & deep learning-based methods along with different feature extraction techniques and benchmark datasets utilized in the recent six years studies. The focus of this study is to review, analyse, classify, and address the recent development in cancer detection and diagnosis of six types of cancers i.e., breast, lung, liver, skin, brain and pancreatic cancer, using machine learning & deep learning techniques. Various state-of-the-art technique are clustered into same group and results are examined through key performance indicators like accuracy, area under the curve, precision, sensitivity, dice score on benchmark datasets and concluded with future research work challenges.
Collapse
Affiliation(s)
- Deepak Painuli
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India.
| | - Suyash Bhardwaj
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India
| | - Utku Köse
- Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey
| |
Collapse
|
16
|
Yan Z, Zhang K, Zhang K, Wang G, Wang L, Zhang J, Qiu Z, Guo Z, Song X, Li J. Integrated 16S rDNA Gene Sequencing and Untargeted Metabolomics Analyses to Investigate the Gut Microbial Composition and Plasma Metabolic Phenotype in Calves With Dampness-Heat Diarrhea. Front Vet Sci 2022; 9:703051. [PMID: 35242833 PMCID: PMC8885629 DOI: 10.3389/fvets.2022.703051] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/10/2022] [Indexed: 12/26/2022] Open
Abstract
Dampness-heat diarrhea (DHD), a common syndrome in Chinese dairy farms, is mainly resulted from digestive system disorders, and accompanied with metabolic disorders in some cases. However, the underlying mechanisms in the intestinal microbiome and plasma metabolome in calves with DHD remain unclear. In order to investigate the pathogenesis of DHD in calves, multi-omics techniques including the 16S rDNA gene sequencing and metabolomics were used to analyze gut microbial compositions and plasma metabolic changes in calves. The results indicated that DHD had a significant effect on the intestinal microbial compositions in calves, which was confirmed by changes in microbial population and distribution. A total of 14 genera were changed, including Escherichia-Shigella, Bacteroides, and Fournierella, in calves with DHD (P < 0.05). Functional analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations indicated that 11 metabolic functions (level 2) were significantly enriched in DHD cases. The untargeted metabolomics analysis showed that 440 metabolites including bilineurin, phosphatidylcholine, and glutamate were significantly different between two groups (VIP > 1 and P < 0.05), and they were related to 67 signal pathways. Eight signal pathways including alpha-linolenic acid, linoleic acid, and glycerophospholipid metabolism were significantly enriched (P < 0.05), which may be potential biomarkers of plasma in calves with DHD. Further, 107 pairs of intestinal microbiota-plasma metabolite correlations were determined, e.g., Escherichia-Shigella was significantly associated with changes of sulfamethazine, butyrylcarnitine, and 14 other metabolites, which reflected that metabolic activity was influenced by the microbiome. These microbiota-metabolite pairs might have a relationship with DHD in calves. In conclusion, the findings revealed that DHD had effect on intestinal microbial compositions and plasma metabolome in calves, and the altered metabolic pathways and microorganisms might serve as diagnostic markers and potential therapeutic targets for DHD in calves.
Collapse
Affiliation(s)
- Zunxiang Yan
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Kang Zhang
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Kai Zhang
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Guibo Wang
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Lei Wang
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Jingyan Zhang
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Zhengying Qiu
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Zhiting Guo
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
| | - Xiaoping Song
- College of Veterinary Medicine, Northwest A&F University, Yangling, China
- Xiaoping Song
| | - Jianxi Li
- Engineering and Technology Research Center of Traditional Chinese Veterinary Medicine of Gansu Province, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, China
- *Correspondence: Jianxi Li
| |
Collapse
|
17
|
Cao Y, Zhao R, Guo K, Ren S, Zhang Y, Lu Z, Tian L, Li T, Chen X, Wang Z. Potential Metabolite Biomarkers for Early Detection of Stage-I Pancreatic Ductal Adenocarcinoma. Front Oncol 2022; 11:744667. [PMID: 35127469 PMCID: PMC8807510 DOI: 10.3389/fonc.2021.744667] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 12/31/2021] [Indexed: 12/21/2022] Open
Abstract
Background & Objectives Pancreatic ductal adenocarcinoma remains an extremely malignant tumor having a poor prognosis. The 5-year survival rate of PDAC is related to its stage (about 80% for stage I vs 20% for other stages). However, detection of PDAC in an early stage is difficult due to the lack of effective screening methods. In this study, we aimed to construct a novel metabolic model for stage-I PDAC detection, using both serum and tissue samples. Methods We employed an untargeted technique, UHPLC-Q-TOF-MS, to identify the potential metabolite, and then used a targeted technique, GC-TOF-MS, to quantitatively validate. Multivariate and univariate statistics were performed to analyze the metabolomic profiles between stage-I PDAC and healthy controls, including 90 serum and 53 tissue samples. 28 patients with stage-I PDAC and 62 healthy controls were included in this study. Results A total of 10 potential metabolites presented the same expression levels both in serum and in tissue. Among them, a 2-metabolites-model (isoleucine and adrenic acid) for stage-I PDAC was constructed. The area under the curve (AUC) value was 0.93 in the discovery set and 0.90 in the independent validation set. Especially, the serum metabolite model had a better diagnostic performance than CA19-9 (AUC = 0.79). Pathway analysis revealed 11 altered pathways in both serum and tissue of stage-I PDAC. Conclusions This study developed a novel serum metabolites model that could early separate stage-I PDAC from healthy controls.
Collapse
Affiliation(s)
- Yingying Cao
- 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
| | - Kai Guo
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yaping Zhang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zipeng 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
| | - Tao Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiao Chen
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- *Correspondence: Xiao Chen, ; Zhongqiu Wang,
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- *Correspondence: Xiao Chen, ; Zhongqiu Wang,
| |
Collapse
|
18
|
Luu TT. Review of Immunohistochemistry Biomarkers in Pancreatic Cancer Diagnosis. Front Oncol 2022; 11:799025. [PMID: 34988027 PMCID: PMC8720928 DOI: 10.3389/fonc.2021.799025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/02/2021] [Indexed: 12/20/2022] Open
Abstract
Pancreatic cancer is one of the cancer types with poor prognosis and high rate of mortality. Diagnostic modalities for early detection of pancreatic cancer have been among the academic concerns. On account of the potential role of immunohistochemistry (IHC) biomarkers in overcoming certain limitations of imaging diagnostic tools in discriminating pancreatic cancer tissues from benign ones, a growing scholarly attention has been given to the diagnostic efficacy of IHC biomarkers for pancreatic cancer. This review will analyze and synthesize published articles to provide an insight into potential IHC biomarkers for pancreatic cancer diagnosis.
Collapse
Affiliation(s)
- Tuan Trong Luu
- Management & Marketing Department, Swinburne University of Technology, Melbourne, VIC, Australia
| |
Collapse
|
19
|
Chacko S, Haseeb YB, Haseeb S. Metabolomics Work Flow and Analytics in Systems Biology. Curr Mol Med 2021; 22:870-881. [PMID: 34923941 DOI: 10.2174/1566524022666211217102105] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/26/2021] [Accepted: 09/24/2021] [Indexed: 11/22/2022]
Abstract
Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.
Collapse
Affiliation(s)
- Sanoj Chacko
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Yumna B Haseeb
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada
| | - Sohaib Haseeb
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| |
Collapse
|
20
|
Glaab E, Rauschenberger A, Banzi R, Gerardi C, Garcia P, Demotes J. Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review. BMJ Open 2021; 11:e053674. [PMID: 34873011 PMCID: PMC8650485 DOI: 10.1136/bmjopen-2021-053674] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/09/2021] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects. DESIGN Scoping review. METHODS We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests. RESULTS Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation. CONCLUSIONS While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.
Collapse
Affiliation(s)
- Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Armin Rauschenberger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rita Banzi
- Center for Health Regulatory Policies, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Chiara Gerardi
- Center for Health Regulatory Policies, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Paula Garcia
- European Clinical Research Infrastructure Network, ECRIN, Paris, France
| | - Jacques Demotes
- European Clinical Research Infrastructure Network, ECRIN, Paris, France
| |
Collapse
|
21
|
Wang S, Li M, Yan L, He M, Lin H, Xu Y, Wan Q, Qin G, Chen G, Xu M, Wang G, Qin Y, Luo Z, Tang X, Wang T, Zhao Z, Xu Y, Chen Y, Huo Y, Hu R, Ye Z, Dai M, Shi L, Gao Z, Su Q, Mu Y, Zhao J, Chen L, Zeng T, Yu X, Li Q, Shen F, Chen L, Zhang Y, Wang Y, Deng H, Liu C, Wu S, Yang T, Li D, Ning G, Wu T, Wang W, Bi Y, Lu J. Metabolomics Study Reveals Systematic Metabolic Dysregulation and Early Detection Markers Associated with Incident Pancreatic Cancer. Int J Cancer 2021; 150:1091-1100. [PMID: 34792202 DOI: 10.1002/ijc.33877] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/12/2021] [Accepted: 11/05/2021] [Indexed: 12/18/2022]
Abstract
Biomarkers for early detection of pancreatic cancer are in urgent need. To explore systematic circulating metabolites unbalance and identify potential biomarkers for pancreatic cancer in prospective Chinese cohorts, we conducted an untargeted metabolomics study in subjects with incident pancreatic cancer and matched controls (n=192) from the China Cardiometabolic Disease and Cancer Cohort (4C) Study. We characterized 998 metabolites in baseline serum and calculated 156 product-to-precursor ratios based on the KEGG database. The identified metabolic profiling revealed systematic metabolic network disorders before pancreatic cancer diagnosis. Forty-five metabolites or product-to-precursor ratios showed significant associations with pancreatic cancer (P < 0.05 and FDR < 0.1), revealing abnormal metabolism of amino acids (especially alanine, aspartate and glutamate), lipids (especially steroid hormone, vitamins, nucleotides and peptides. A novel metabolite panel containing aspartate/alanine (OR [95% CI]: 1.97 [1.31-2.94]), androstenediol monosulfate (0.69 [0.49-0.97]) and glycylvaline (1.68 [1.04-2.70]) was significantly associated with risk of pancreatic cancer. Area under the receiver operating characteristic curves (AUCs) was improved from 0.573 (reference model of CA 19-9) to 0.721. The novel metabolite panel was validated in an independent cohort with AUC improved from 0.529 to 0.661. These biomarkers may have a potential value in early detection of pancreatic cancer. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Shuangyuan Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Li Yan
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hong Lin
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Qin Wan
- The Affiliated Hospital of Luzhou Medical College, Luzhou, China
| | - Guijun Qin
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Gang Chen
- Fujian Provincial Hospital, Fujian Medical University, Fuzhou, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Guixia Wang
- The First Hospital of Jilin University, Changchun, China
| | - Yingfen Qin
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zuojie Luo
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xulei Tang
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yiping Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yanan Huo
- Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, China
| | - Ruying Hu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhen Ye
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Meng Dai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Lixin Shi
- Affiliated Hospital of Guiyang Medical College, Guiyang, China
| | - Zhengnan Gao
- Dalian Municipal Central Hospital, Dalian, China
| | - Qing Su
- Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yiming Mu
- Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jiajun Zhao
- Shandong Provincial Hospital affiliated to Shandong University, Jinan, China
| | - Lulu Chen
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tianshu Zeng
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xuefeng Yu
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiang Li
- The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Feixia Shen
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Li Chen
- Qilu Hospital of Shandong University, Jinan, China
| | - Yinfei Zhang
- Central Hospital of Shanghai Jiading District, Shanghai, China
| | - Youmin Wang
- The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Huacong Deng
- The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chao Liu
- Jiangsu Province Hospital on Integration of Chinese and Western Medicine, Nanjing, China
| | - Shengli Wu
- Karamay Municipal People's Hospital, Xinjiang, China
| | - Tao Yang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, the University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | | |
Collapse
|
22
|
On the Role of Paraoxonase-1 and Chemokine Ligand 2 (C-C motif) in Metabolic Alterations Linked to Inflammation and Disease. A 2021 Update. Biomolecules 2021; 11:biom11070971. [PMID: 34356595 PMCID: PMC8301931 DOI: 10.3390/biom11070971] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/23/2021] [Accepted: 06/29/2021] [Indexed: 02/08/2023] Open
Abstract
Infectious and many non-infectious diseases share common molecular mechanisms. Among them, oxidative stress and the subsequent inflammatory reaction are of particular note. Metabolic disorders induced by external agents, be they bacterial or viral pathogens, excessive calorie intake, poor-quality nutrients, or environmental factors produce an imbalance between the production of free radicals and endogenous antioxidant systems; the consequence being the oxidation of lipids, proteins, and nucleic acids. Oxidation and inflammation are closely related, and whether oxidative stress and inflammation represent the causes or consequences of cellular pathology, both produce metabolic alterations that influence the pathogenesis of the disease. In this review, we highlight two key molecules in the regulation of these processes: Paraoxonase-1 (PON1) and chemokine (C-C motif) ligand 2 (CCL2). PON1 is an enzyme bound to high-density lipoproteins. It breaks down lipid peroxides in lipoproteins and cells, participates in the protection conferred by HDL against different infectious agents, and is considered part of the innate immune system. With PON1 deficiency, CCL2 production increases, inducing migration and infiltration of immune cells in target tissues and disturbing normal metabolic function. This disruption involves pathways controlling cellular homeostasis as well as metabolically-driven chronic inflammatory states. Hence, an understanding of these relationships would help improve treatments and, as well, identify new therapeutic targets.
Collapse
|
23
|
Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin 2021; 71:333-358. [PMID: 33982817 PMCID: PMC8298088 DOI: 10.3322/caac.21670] [Citation(s) in RCA: 275] [Impact Index Per Article: 91.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/07/2021] [Accepted: 03/09/2021] [Indexed: 12/12/2022] Open
Abstract
Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.
Collapse
Affiliation(s)
- Daniel R. Schmidt
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- Corresponding author:-
| | - Rutulkumar Patel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - David G. Kirsch
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC 27708 USA
| | - Caroline A. Lewis
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Matthew G. Vander Heiden
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Jason W. Locasale
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC 27708 USA
- Corresponding author:-
| |
Collapse
|
24
|
Liu S, Tang H, Liu H, Wang J. Multi-label Learning for the Diagnosis of Cancer and Identification of Novel Biomarkers with High-throughput Omics. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200623130416] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
The advancement of bioinformatics and machine learning has facilitated the
diagnosis of cancer and the discovery of omics-based biomarkers.
Objective:
Our study employed a novel data-driven approach to classifying the normal samples and
different types of gastrointestinal cancer samples, to find potential biomarkers for effective diagnosis
and prognosis assessment of gastrointestinal cancer patients.
Methods:
Different feature selection methods were used, and the diagnostic performance of the proposed
biosignatures was benchmarked using support vector machine (SVM) and random forest (RF)
models.
Results:
All models showed satisfactory performance in which Multilabel-RF appeared to be the best.
The accuracy of the Multilabel-RF based model was 83.12%, with precision, recall, F1, and Hamming-
Loss of 79.70%, 68.31%, 0.7357 and 0.1688, respectively. Moreover, proposed biomarker signatures
were highly associated with multifaceted hallmarks in cancer. Functional enrichment analysis and impact
of the biomarker candidates in the prognosis of the patients were also examined.
Conclusion:
We successfully introduced a solid workflow based on multi-label learning with High-
Throughput Omics for diagnosis of cancer and identification of novel biomarkers. Novel transcriptome
biosignatures that may improve the diagnostic accuracy in gastrointestinal cancer are introduced for
further validations in various clinical settings.
Collapse
Affiliation(s)
- Shicai Liu
- State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China
| | - Hailin Tang
- State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China
| | - Hongde Liu
- State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China
| | - Jinke Wang
- State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China
| |
Collapse
|
25
|
Khan T, Loftus TJ, Filiberto AC, Ozrazgat-Baslanti T, Ruppert MM, Bandhyopadyay S, Laiakis EC, Arnaoutakis DJ, Bihorac A. Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review. Ann Surg 2021; 273:258-268. [PMID: 32482979 PMCID: PMC7704904 DOI: 10.1097/sla.0000000000003935] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making. BACKGROUND Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants. METHODS PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods. RESULTS Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses. CONCLUSIONS Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.
Collapse
Affiliation(s)
- Tabassum Khan
- Department of Surgery, University of Florida, Gainesville,
FL, USA
| | - Tyler J. Loftus
- Department of Surgery, University of Florida, Gainesville,
FL, USA
| | | | - Tezcan Ozrazgat-Baslanti
- Department of Medicine, University of Florida, Gainesville,
FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP),
University of Florida, Gainesville, FL
| | | | - Sabyasachi Bandhyopadyay
- Department of Medicine, University of Florida, Gainesville,
FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP),
University of Florida, Gainesville, FL
| | - Evagelia C. Laiakis
- Department of Oncology, Georgetown University, Washington
DC, USA
- Department of Biochemistry and Molecular & Cellular
Biology, Georgetown University, Washington DC, USA
| | | | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville,
FL, USA
- Precision and Intelligent Systems in Medicine (PrismaP),
University of Florida, Gainesville, FL
| |
Collapse
|
26
|
Abstract
In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.
Collapse
Affiliation(s)
- Tusharkanti Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Weiming Zhang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| |
Collapse
|
27
|
Wang S, Chen J, Li H, Qi X, Liu X, Guo X. Metabolomic Detection Between Pancreatic Cancer and Liver Metastasis Nude Mouse Models Constructed by Using the PANC1-KAI1/CD 82 Cell Line. Technol Cancer Res Treat 2021; 20:15330338211045204. [PMID: 34605330 PMCID: PMC8493323 DOI: 10.1177/15330338211045204] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Background: Pancreatic cancer (PC) has a poor prognosis and is prone to liver metastasis. The KAI1/CD82 gene inhibits PC metastasis. This study aimed to explore differential metabolites and enrich the pathways in serum samples between PC and liver metastasis nude mouse models stably expressing KAI1/CD82. Methods: KAI1/CD82-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD82 were constructed for the first time. This cell line was used to construct 3 PC nude mouse models and 3 liver metastasis nude mouse models. The different metabolites and Kyoto encyclopedia of genes and genomes (KEGG) and human metabolome database (HMDB) enrichment pathways were analyzed using the serum samples of the 2 groups of nude mouse models on the basis of untargeted ultra-performance liquid chromatography-tandem mass spectrometry platform. Results: KAI1/CD82-PLV-EF1α-MCS-IRES-Puro vector and PANC1 cell line stably expressing KAI1/CD82 were constructed successfully, and all nude mouse models survived and developed cancers. Among the 1233 metabolites detected, 18 metabolites (9 upregulated and 9 downregulated) showed differences. In agreement with the literature data, the most significant differences between both groups were found in the levels of bile acids (taurocholic acid, chenodeoxycholic acid), glycine, prostaglandin E2, vitamin D, guanosine monophosphate, and inosine. Bile recreation, primary bile acid biosynthesis, and purine metabolism KEGG pathways and a series of HMDB pathways (P < .05) contained differential metabolites that may be associated with liver metastasis from PC. However, the importance of these metabolites on PC liver metastases remains to be elucidated. Conclusions: Our findings suggested that the metabolomic approach may be a useful method to detect potential biomarkers in PC.
Collapse
Affiliation(s)
- Shuo Wang
- General Hospital of Northern Theater Command of China Medical University, Shenyang, Liaoning Province, P.R. China
| | - Jiang Chen
- General Hospital of Northern Theater Command of China Medical University, Shenyang, Liaoning Province, P.R. China
| | - Hongyu Li
- General Hospital of Northern Theater Command of China Medical University, Shenyang, Liaoning Province, P.R. China
| | - Xingshun Qi
- General Hospital of Northern Theater Command of China Medical University, Shenyang, Liaoning Province, P.R. China
| | - Xu Liu
- General Hospital of Northern Theater Command of China Medical University, Shenyang, Liaoning Province, P.R. China
| | - Xiaozhong Guo
- General Hospital of Northern Theater Command of China Medical University, Shenyang, Liaoning Province, P.R. China
- Xiaozhong Guo, PhD, Department of Gastroenterology, General Hospital of Northern Theater Command of China Medical University, No. 83 Wenhua Road, Shenyang, 110840 Liaoning Province, China.
| |
Collapse
|
28
|
Hou XW, Wang Y, Pan CW. Metabolomics in Age-Related Macular Degeneration: A Systematic Review. Invest Ophthalmol Vis Sci 2020; 61:13. [PMID: 33315052 PMCID: PMC7735950 DOI: 10.1167/iovs.61.14.13] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 11/25/2020] [Indexed: 12/22/2022] Open
Abstract
Purpose Age-related macular degeneration (AMD) is one of the leading causes of blindness among the elderly, and the exact pathogenesis of the AMD remains unclear. The purpose of this review is to summarize potential metabolic biomarkers and pathways of AMD that might facilitate risk predictions and clinical diagnoses of AMD. Methods We obtained relevant publications of metabolomics studies of human beings by systematically searching the MEDLINE (PubMed) database before June 2020. Studies were included if they performed mass spectrometry-based or nuclear magnetic resonance-based metabolomics approach for humans. In addition, AMD was assessed from fundus photographs based on standardized protocols. The metabolic pathway analysis was performed using MetaboAnalyst 3.0. Results Thirteen studies were included in this review. Repeatedly identified metabolites including phenylalanine, adenosine, hypoxanthine, tyrosine, creatine, citrate, carnitine, proline, and maltose have the possibility of being biomarkers of AMD. Validation of the biomarker panels was observed in one study. Dysregulation of metabolic pathways involves lipid metabolism, carbohydrate metabolism, nucleotide metabolism, amino acid metabolism, and translation, which might play important roles in the development and progression of AMD. Conclusions This review summarizes the potential metabolic biomarkers and pathways related to AMD, providing opportunities for the construction of diagnostic or predictive models for AMD and the discovery of new therapeutic targets.
Collapse
Affiliation(s)
- Xiao-Wen Hou
- School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Ying Wang
- School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Chen-Wei Pan
- School of Public Health, Medical College of Soochow University, Suzhou, China
| |
Collapse
|
29
|
Evans ED, Duvallet C, Chu ND, Oberst MK, Murphy MA, Rockafellow I, Sontag D, Alm EJ. Predicting human health from biofluid-based metabolomics using machine learning. Sci Rep 2020; 10:17635. [PMID: 33077825 PMCID: PMC7572502 DOI: 10.1038/s41598-020-74823-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/05/2020] [Indexed: 12/15/2022] Open
Abstract
Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.
Collapse
Affiliation(s)
- Ethan D Evans
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Claire Duvallet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Biobot Analytics, Somerville, MA, 02143, USA
| | - Nathaniel D Chu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael K Oberst
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael A Murphy
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Isaac Rockafellow
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Superpedestrian, Cambridge, MA, 02139, USA
| | - David Sontag
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| |
Collapse
|
30
|
Jiang W, Bai W, Li J, Liu J, Zhao K, Ren L. Leukemia inhibitory factor is a novel biomarker to predict lymph node and distant metastasis in pancreatic cancer. Int J Cancer 2020; 148:1006-1013. [PMID: 32914874 DOI: 10.1002/ijc.33291] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 08/24/2020] [Accepted: 08/27/2020] [Indexed: 12/13/2022]
Abstract
Pancreatic cancer has a low survival rate, and most patients have lymph node metastasis and distant metastasis at the time of diagnosis. Despite efforts to improve overall survival (OS) and recurrence free survival (RFS), the prognosis of pancreatic cancer remains poor, underscoring the importance of identifying new biomarkers to predict metastasis in patients with pancreatic cancer. Leukemia inhibitory factor (LIF) is overexpressed in many types of cancer and is involved in the development of various malignancies including pancreatic cancer. However, the role of LIF as a biomarker to predict metastasis in pancreatic cancer remains unclear. In this study, univariate and multivariate Cox regression analyses identified LIF expression in pancreatic tumor tissues as an independent risk factor related to worse OS and RFS. LIF overexpression was related to poor clinicopathological features such as lymph node metastasis and Pathological stage (pTNM) stage. Serum LIF levels were higher in pancreatic cancer patients than in healthy controls. The area under the receiver operating characteristic curve indicated that serum LIF is more effective than other biomarkers (CA199 and CEA) for predicting lymph node and distant metastasis.
Collapse
Affiliation(s)
- Wenna Jiang
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Weiwei Bai
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jianhua Li
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jing Liu
- Department of Breast Oncoplastic Surgery, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Kaili Zhao
- Department of Pancreatic Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Li Ren
- Department of Clinical Laboratory, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| |
Collapse
|
31
|
Wang W, Han Z, Guo D, Xiang Y. UHPLC-QTOFMS-based metabolomic analysis of serum and urine in rats treated with musalais containing varying ethyl carbamate content. Anal Bioanal Chem 2020; 412:7627-7637. [PMID: 32897411 DOI: 10.1007/s00216-020-02900-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/07/2020] [Accepted: 08/18/2020] [Indexed: 02/06/2023]
Abstract
The aim of this work is to investigate the effect of the ethyl carbamate (EC) content in musalais on the metabolism of rats. Electron beam irradiation was performed to decrease the content of EC in musalais, and Sprague Dawley rats were subjected to intragastric administration of musalais with varying EC content (high, medium, and low groups). Control rats were fed normally without any treatment. Serum and urine samples were analyzed using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Principal component analysis and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were performed to detect changes in the metabolite profile in the serum and urine in order to identify the differential metabolites and metabolic pathways. The results demonstrated clear differences in the serum and urine metabolic patterns between control and treatment groups. Ions in treatment groups with variable importance in the projection of >1 (selected from the OPLS-DA loading plots) and Ps < 0.05 (Student t test) compared to control group were identified as candidate metabolites. Analysis of the metabolic pathways relevant to the identified differential metabolites revealed that high EC content in musalais (10 mg/kg) mainly affected rats through valine, leucine, and isoleucine biosynthesis and nicotinate and nicotinamide metabolism, which were associated with energy metabolism. In addition, this work suggests that EC can induce oxidative stress via inhibition of glycine content.
Collapse
Affiliation(s)
- Weihua Wang
- College of Life Science, Tarim University, Alaer, Xinjiang, 843300, China
| | - ZhanJiang Han
- College of Life Science, Tarim University, Alaer, Xinjiang, 843300, China.
| | - Dongqi Guo
- College of Life Science, Tarim University, Alaer, Xinjiang, 843300, China
| | - Yanju Xiang
- College of Life Science, Tarim University, Alaer, Xinjiang, 843300, China
| |
Collapse
|
32
|
Advances in Liquid Chromatography–Mass Spectrometry-Based Lipidomics: A Look Ahead. JOURNAL OF ANALYSIS AND TESTING 2020. [DOI: 10.1007/s41664-020-00135-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
33
|
Enhanced single-cell metabolomics by capillary electrophoresis electrospray ionization-mass spectrometry with field amplified sample injection. Anal Chim Acta 2020; 1118:36-43. [PMID: 32418602 DOI: 10.1016/j.aca.2020.04.028] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/08/2020] [Accepted: 04/10/2020] [Indexed: 12/26/2022]
Abstract
Single-cell metabolomics provides information on the biochemical state of an individual cell and its relationship with the surrounding environment. Characterization of metabolic cellular heterogeneity is challenging, in part due to the small amounts of analytes and their wide dynamic concentration ranges within individual cells. CE-ESI-MS is well suited to single-cell assays because of its low sample-volume requirements and low detection limits. While the volume of a cell is in the picoliter range, after isolation, the typical volume of the lysed cell sample is on the order of a microliter; however, only nanoliters are injected into the CE system, with the volume mismatch limiting analytical performance. Here we developed an approach for the detection of intracellular metabolites from a single neuron using field amplified sample injection (FASI) CE-ESI-MS. Through the application of FASI, we achieved 100- to 300-fold detection limit enhancement compared to hydrodynamic injections. We further enhanced the analyte identification and quantification accuracy via introduction of two internal standards. As a result, the relative standard deviations of migration times were reduced to <5%, aiding identification. Finally, we successfully applied FASI CE-ESI-MS to the untargeted profiling of metabolites of Aplysia californica pleural sensory neurons with <50 μm diameter cell somata. As a result, twenty one neurotransmitters and metabolites have been quantified in these neurons.
Collapse
|
34
|
Xiong Y, Shi C, Zhong F, Liu X, Yang P. LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer. Clin Chim Acta 2020; 506:214-221. [PMID: 32243985 DOI: 10.1016/j.cca.2020.03.043] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 02/12/2020] [Accepted: 03/30/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Pancreatic cancer (PC) is the fourth leading cause of cancer death because of its subtle clinical symptoms in the early stage. To discover particular serum metabolites as potential biomarkers to differentiate pancreatic carcinoma from benign disease (BD) is on urgent demand. METHOD To comprehensively analyze serum metabolites obtained from 14 patients with PC, 10 patients with BD and 10 healthy individuals (normal control, NC), we separated the metabolites using both reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC). The data were acquired on a high-resolution quadrupole time-of-flight mass spectrometer operated in negative (ESI-) and positive (ESI+) ionization modes, respectively. Differential metabolites were selected by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Sequential window acquisition of all theoretical spectra (SWATH) analysis was further utilized to validate the metabolites found in discovery stage. The receiver operator characteristics (ROC) curve analysis was performed to evaluate predictive clinical usefulness of 8 metabolites. RESULTS A total of 8 metabolites including taurocholic acid, glycochenodexycholic acid, glycocholic acid, L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine were identified and relatively quantified as differential metabolites for discriminating PC, BD and NC. The 8 metabolites and their combination discriminated PC from BD and NC with well-performed area under the curve (AUC) values, sensitivity and specificity. CONCLUSION Bile acids (especially taurocholic acid) performed to be potential biomarkers in PC diagnosis. Other amino acids (such as L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine) in serum samples from PC patients might provide a sensitive, blood-borne diagnostic signature for the presence of PC or its precursor lesions.
Collapse
Affiliation(s)
- Yueting Xiong
- Institutes of Biomedical Sciences and The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200032, China
| | - Chao Shi
- Shanghai Dermatology Hospital, No. 1278th Baode Road, Jing'an District, Shanghai 200443, China
| | - Fan Zhong
- Institutes of Biomedical Sciences and The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200032, China; Department of Systems Biology for Medicine, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xiaohui Liu
- Institutes of Biomedical Sciences and The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200032, China.
| | - Pengyuan Yang
- Institutes of Biomedical Sciences and The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200032, China.
| |
Collapse
|
35
|
Long NP, Nghi TD, Kang YP, Anh NH, Kim HM, Park SK, Kwon SW. Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine. Metabolites 2020; 10:E51. [PMID: 32013105 PMCID: PMC7074059 DOI: 10.3390/metabo10020051] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/17/2020] [Accepted: 01/21/2020] [Indexed: 12/18/2022] Open
Abstract
Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.
Collapse
Affiliation(s)
- Nguyen Phuoc Long
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Tran Diem Nghi
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea; (T.D.N.); (S.K.P.)
| | - Yun Pyo Kang
- Department of Cancer Physiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Hyung Min Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| | - Sang Ki Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea; (T.D.N.); (S.K.P.)
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea; (N.P.L.); (N.H.A.); (H.M.K.)
| |
Collapse
|
36
|
Rodríguez-Tomàs E, Arguís M, Arenas M, Fernández-Arroyo S, Murcia M, Sabater S, Torres L, Baiges-Gayà G, Hernández-Aguilera A, Camps J, Joven J. Alterations in plasma concentrations of energy-balance-related metabolites in patients with lung, or head & neck, cancers: Effects of radiotherapy. J Proteomics 2019; 213:103605. [PMID: 31841666 DOI: 10.1016/j.jprot.2019.103605] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/04/2019] [Accepted: 12/07/2019] [Indexed: 02/06/2023]
Abstract
We investigated the alterations in the plasma concentrations of energy-balance-related metabolites in patients with lung (LC) or head & neck (HNC) cancer and the changes on these parameters induced by radiotherapy. The study was conducted in 33 patients with non-small cell LC and 28 patients with HNC. We analyzed the concentrations of 17 metabolites involved in glycolysis, citric acid cycle and amino acid metabolism using targeted gas chromatography coupled to quadrupole time-of-flight mass spectrometry. For comparison, a control group of 50 healthy individuals was included in the present study. Patients with LC or HNC had significant alterations in the plasma levels of several energy-balance-related metabolites. Radiotherapy partially normalized these alterations in patients with LC, but not in those with HNC. The measurement of plasma glutamate concentration was an excellent predictor of the presence of LC or HNC, with sensitivity >90% and specificity >80%. Also, associations with disease prognosis were observed with plasma glutamate, amino acids and β-hydroxybutyrate concentrations. SIGNIFICANCE: This study analyzed the changes produced in the plasma concentrations of energy-balance-related metabolites in patients with lung cancer or head and neck cancer. The results obtained identified glutamate as the parameter with the highest discrimination capacity between patients and the control group. The relationships between various metabolites and clinical outcomes were also analyzed. These results extend the knowledge of metabolic alterations in cancer, thus facilitating the search for biomarkers and therapeutic targets.
Collapse
Affiliation(s)
- Elisabet Rodríguez-Tomàs
- Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Tarragona, Spain; Department of Radiation Oncology, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Josep Laporte s/n, 43204 Reus, Tarragona, Spain
| | - Mònica Arguís
- Department of Radiation Oncology, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Josep Laporte s/n, 43204 Reus, Tarragona, Spain
| | - Meritxell Arenas
- Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Tarragona, Spain; Department of Radiation Oncology, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Josep Laporte s/n, 43204 Reus, Tarragona, Spain.
| | - Salvador Fernández-Arroyo
- Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Tarragona, Spain
| | - Mauricio Murcia
- Department of Radiation Oncology, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Josep Laporte s/n, 43204 Reus, Tarragona, Spain
| | - Sebastià Sabater
- Department of Radiation Oncology, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Josep Laporte s/n, 43204 Reus, Tarragona, Spain
| | - Laura Torres
- Department of Radiation Oncology, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Av. Josep Laporte s/n, 43204 Reus, Tarragona, Spain
| | - Gerard Baiges-Gayà
- Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Tarragona, Spain
| | - Anna Hernández-Aguilera
- Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Tarragona, Spain
| | - Jordi Camps
- Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Tarragona, Spain.
| | - Jorge Joven
- Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, C. Sant Joan s/n, 43201 Reus, Tarragona, Spain
| |
Collapse
|
37
|
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.
Collapse
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.
| |
Collapse
|
38
|
Kim HM, Long NP, Yoon SJ, Nguyen HT, Kwon SW. Metabolomics and phenotype assessment reveal cellular toxicity of triclosan in Caenorhabditis elegans. CHEMOSPHERE 2019; 236:124306. [PMID: 31319312 DOI: 10.1016/j.chemosphere.2019.07.037] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/29/2019] [Accepted: 07/04/2019] [Indexed: 05/20/2023]
Abstract
Triclosan (TCS) is an antibiotic that is added to household and personal care products. Recently, it has become more popular, turning into one of the major contaminants of the environment. This raises a dawning awareness regarding health and environmental issues. In this study, the toxicity of TCS to Caenorhabditis elegans was evaluated using a metabolomics approach. Additionally, the lifespan, locomotion, and reproduction of C. elegans were monitored for a better interpretation of toxic effects. In C. elegans exposed to TCS at the concentration of 1 mg/L, the average lifespan decreased in approximately 3 days. Reproduction and locomotion were also decreased with TCS exposure. The number of progenies, head thrashes, and body bends decreased to 45.15 ± 11.63, 39.60 ± 5.90, and 9.20 ± 1.56 with the exposure to TCS, respectively. Oxidative stress was induced by TCS exposure, which was confirmed by using DAF-16:GFP strain and H2DCF-DA-based ROS assay. Metabolomics analysis revealed that carbohydrates and amino acids related to energy production were considerably affected by TCS exposure. Additionally, levels of tyrosine, serine, and polyamines, responsible for neurotransmitter and stress response, were significantly altered. Collectively, our findings suggest that TCS induces toxic effects by various mechanisms and exerts a strong influence in various phenotypes of the tested model.
Collapse
Affiliation(s)
- Hyung Min Kim
- College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Nguyen Phuoc Long
- College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Sang Jun Yoon
- College of Pharmacy, Seoul National University, Seoul, 08826, South Korea
| | - Huy Truong Nguyen
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul, 08826, South Korea.
| |
Collapse
|
39
|
Sadighbayan D, Sadighbayan K, Khosroushahi AY, Hasanzadeh M. Recent advances on the DNA-based electrochemical biosensing of cancer biomarkers: Analytical approach. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.07.020] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
40
|
Anh NH, Long NP, Kim SJ, Min JE, Yoon SJ, Kim HM, Yang E, Hwang ES, Park JH, Hong SS, Kwon SW. Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis. Metabolites 2019; 9:E199. [PMID: 31546652 PMCID: PMC6835899 DOI: 10.3390/metabo9100199] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 09/09/2019] [Accepted: 09/17/2019] [Indexed: 02/07/2023] Open
Abstract
Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference.
Collapse
Affiliation(s)
- Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | | | - Sun Jo Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Jung Eun Min
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Sang Jun Yoon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Hyung Min Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Eugine Yang
- College of Pharmacy, Ewha Womans University, Seoul 03760, Korea.
| | - Eun Sook Hwang
- College of Pharmacy, Ewha Womans University, Seoul 03760, Korea.
| | - Jeong Hill Park
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Soon-Sun Hong
- Department of Biomedical Sciences, College of Medicine, Inha University, Incheon 22212, Korea.
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| |
Collapse
|
41
|
Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways. Cancers (Basel) 2019; 11:cancers11091244. [PMID: 31450698 PMCID: PMC6770126 DOI: 10.3390/cancers11091244] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 08/20/2019] [Accepted: 08/22/2019] [Indexed: 12/24/2022] Open
Abstract
Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.
Collapse
|
42
|
Kang YP, Yoon JH, Long NP, Koo GB, Noh HJ, Oh SJ, Lee SB, Kim HM, Hong JY, Lee WJ, Lee SJ, Hong SS, Kwon SW, Kim YS. Spheroid-Induced Epithelial-Mesenchymal Transition Provokes Global Alterations of Breast Cancer Lipidome: A Multi-Layered Omics Analysis. Front Oncol 2019; 9:145. [PMID: 30949448 PMCID: PMC6437068 DOI: 10.3389/fonc.2019.00145] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 02/20/2019] [Indexed: 01/06/2023] Open
Abstract
Metabolic rewiring has been recognized as an important feature to the progression of cancer. However, the essential components and functions of lipid metabolic networks in breast cancer progression are not fully understood. In this study, we investigated the roles of altered lipid metabolism in the malignant phenotype of breast cancer. Using a spheroid-induced epithelial-mesenchymal transition (EMT) model, we conducted multi-layered lipidomic and transcriptomic analysis to comprehensively describe the rewiring of the breast cancer lipidome during the malignant transformation. A tremendous homeostatic disturbance of various complex lipid species including ceramide, sphingomyelin, ether-linked phosphatidylcholines, and ether-linked phosphatidylethanolamine was found in the mesenchymal state of cancer cells. Noticeably, polyunsaturated fatty acids composition in spheroid cells was significantly decreased, accordingly with the gene expression patterns observed in the transcriptomic analysis of associated regulators. For instance, the up-regulation of SCD, ACOX3, and FADS1 and the down-regulation of PTPLB, PECR, and ELOVL2 were found among other lipid metabolic regulators. Significantly, the ratio of C22:6n3 (docosahexaenoic acid, DHA) to C22:5n3 was dramatically reduced in spheroid cells analogously to the down-regulation of ELOVL2. Following mechanistic study confirmed the up-regulation of SCD and down-regulation of PTPLB, PECR, ELOVL2, and ELOVL3 in the spheroid cells. Furthermore, the depletion of ELOVL2 induced metastatic characteristics in breast cancer cells via the SREBPs axis. A subsequent large-scale analysis using 51 breast cancer cell lines demonstrated the reduced expression of ELOVL2 in basal-like phenotypes. Breast cancer patients with low ELOVL2 expression exhibited poor prognoses (HR = 0.76, CI = 0.67–0.86). Collectively, ELOVL2 expression is associated with the malignant phenotypes and appear to be a novel prognostic biomarker in breast cancer. In conclusion, the present study demonstrates that there is a global alteration of the lipid composition during EMT and suggests the down-regulation of ELOVL2 induces lipid metabolism reprogramming in breast cancer and contributes to their malignant phenotypes.
Collapse
Affiliation(s)
- Yun Pyo Kang
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Jung-Ho Yoon
- Department of Biochemistry, Ajou University School of Medicine, Suwon, South Korea.,Department of Biomedical Sciences, Graduate School, Ajou University, Suwon, South Korea
| | | | - Gi-Bang Koo
- Department of Biochemistry, Ajou University School of Medicine, Suwon, South Korea.,Department of Biomedical Sciences, Graduate School, Ajou University, Suwon, South Korea
| | - Hyun-Jin Noh
- Department of Biochemistry, Ajou University School of Medicine, Suwon, South Korea.,Department of Biomedical Sciences, Graduate School, Ajou University, Suwon, South Korea
| | - Seung-Jae Oh
- Department of Biochemistry, Ajou University School of Medicine, Suwon, South Korea.,Department of Biomedical Sciences, Graduate School, Ajou University, Suwon, South Korea
| | - Sae Bom Lee
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Hyung Min Kim
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Ji Yeon Hong
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Won Jun Lee
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Seul Ji Lee
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Soon-Sun Hong
- Department of Biomedical Sciences, College of Medicine, Inha University, Incheon, South Korea
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul, South Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
| | - You-Sun Kim
- Department of Biochemistry, Ajou University School of Medicine, Suwon, South Korea.,Department of Biomedical Sciences, Graduate School, Ajou University, Suwon, South Korea
| |
Collapse
|
43
|
An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer. Cancers (Basel) 2019; 11:cancers11020155. [PMID: 30700038 PMCID: PMC6407035 DOI: 10.3390/cancers11020155] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 01/18/2019] [Accepted: 01/21/2019] [Indexed: 12/20/2022] Open
Abstract
Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.
Collapse
|
44
|
Long NP, Park S, Anh NH, Nghi TD, Yoon SJ, Park JH, Lim J, Kwon SW. High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer. Int J Mol Sci 2019; 20:E296. [PMID: 30642095 PMCID: PMC6358915 DOI: 10.3390/ijms20020296] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 12/31/2018] [Accepted: 01/04/2019] [Indexed: 02/07/2023] Open
Abstract
The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in Helicobacter pylori infection and inflammatory processes. The overexpression of TGFBI and S100A2 was associated with poor disease-free survival while the down-regulation of NR5A2, SLC4A4, and CD177 was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.
Collapse
Affiliation(s)
- Nguyen Phuoc Long
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Seongoh Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea.
| | - Nguyen Hoang Anh
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Tran Diem Nghi
- School of Medicine, Vietnam National University, Ho Chi Minh 70000, Vietnam.
| | - Sang Jun Yoon
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Jeong Hill Park
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Johan Lim
- Department of Statistics, Seoul National University, Seoul 08826, Korea.
| | - Sung Won Kwon
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| |
Collapse
|
45
|
Long NP, Park S, Anh NH, Min JE, Yoon SJ, Kim HM, Nghi TD, Lim DK, Park JH, Lim J, Kwon SW. Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis. J Clin Med 2019; 8:E50. [PMID: 30621359 PMCID: PMC6352223 DOI: 10.3390/jcm8010050] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 12/20/2018] [Accepted: 01/02/2019] [Indexed: 12/15/2022] Open
Abstract
Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients.
Collapse
Affiliation(s)
- Nguyen Phuoc Long
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Seongoh Park
- Department of Statistics, Seoul National University, Seoul 08826, Korea.
| | - Nguyen Hoang Anh
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Jung Eun Min
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Sang Jun Yoon
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Hyung Min Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Tran Diem Nghi
- School of Medicine, Vietnam National University, Ho Chi Minh 700000, Vietnam.
| | - Dong Kyu Lim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Jeong Hill Park
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| | - Johan Lim
- Department of Statistics, Seoul National University, Seoul 08826, Korea.
| | - Sung Won Kwon
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea.
| |
Collapse
|
46
|
Bach DH, Long NP, Luu TTT, Anh NH, Kwon SW, Lee SK. The Dominant Role of Forkhead Box Proteins in Cancer. Int J Mol Sci 2018; 19:E3279. [PMID: 30360388 PMCID: PMC6213973 DOI: 10.3390/ijms19103279] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 10/19/2018] [Accepted: 10/20/2018] [Indexed: 12/16/2022] Open
Abstract
Forkhead box (FOX) proteins are multifaceted transcription factors that are significantly implicated in cancer, with various critical roles in biological processes. Herein, we provide an overview of several key members of the FOXA, FOXC, FOXM1, FOXO and FOXP subfamilies. Important pathophysiological processes of FOX transcription factors at multiple levels in a context-dependent manner are discussed. We also specifically summarize some major aspects of FOX transcription factors in association with cancer research such as drug resistance, tumor growth, genomic alterations or drivers of initiation. Finally, we suggest that targeting FOX proteins may be a potential therapeutic strategy to combat cancer.
Collapse
Affiliation(s)
- Duc-Hiep Bach
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | | | | | - Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| | - Sang Kook Lee
- College of Pharmacy, Seoul National University, Seoul 08826, Korea.
| |
Collapse
|