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Parnas M, McLane-Svoboda AK, Cox E, McLane-Svoboda SB, Sanchez SW, Farnum A, Tundo A, Lefevre N, Miller S, Neeb E, Contag CH, Saha D. Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor. Biosens Bioelectron 2024; 261:116466. [PMID: 38850736 DOI: 10.1016/j.bios.2024.116466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 05/24/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.
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Affiliation(s)
- Michael Parnas
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Autumn K McLane-Svoboda
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Elyssa Cox
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Summer B McLane-Svoboda
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Simon W Sanchez
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Alexander Farnum
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Anthony Tundo
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Noël Lefevre
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Sydney Miller
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Emily Neeb
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Christopher H Contag
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA; Department of Microbiology, Genetics & Immunology, Michigan State University, East Lansing, MI, USA
| | - Debajit Saha
- Department of Biomedical Engineering and the Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA; Neuroscience Program, Michigan State University, East Lansing, MI, USA.
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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.
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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.
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Dong X, Qu Y, Sheng T, Fan Y, Chen S, Yuan Q, Ma G, Ge Y. HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers. Aging (Albany NY) 2024; 16:7487-7504. [PMID: 38683118 PMCID: PMC11087094 DOI: 10.18632/aging.205779] [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: 10/27/2023] [Accepted: 01/03/2024] [Indexed: 05/01/2024]
Abstract
Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.
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Affiliation(s)
- Xun Dong
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yaoyao Qu
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Tongtong Sheng
- School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuanming Fan
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Silu Chen
- School of Public Health, Nanjing Medical University, Nanjing, China
| | - Qinbo Yuan
- Department of Urology, Wuxi Fifth People’s Hospital, Wuxi, China
| | - Gaoxiang Ma
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
- The Clinical Metabolomics Center, China Pharmaceutical University, Nanjing, China
- Deparment of Oncology, Pukou Hospital of Chinese Medicine affiliated to China Pharmaceutical University, Nanjing, China
| | - Yuqiu Ge
- Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, China
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Taylor MJ, Chitwood CP, Xie Z, Miller HA, van Berkel VH, Fu XA, Frieboes HB, Suliman SA. Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath. Respir Med 2024; 222:107534. [PMID: 38244700 DOI: 10.1016/j.rmed.2024.107534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
BACKGROUND Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity. METHODS Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe). RESULTS The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity. CONCLUSIONS Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.
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Affiliation(s)
- Matthew J Taylor
- Division of Pulmonary Medicine, University of Louisville, Louisville, KY, USA
| | - Corey P Chitwood
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Zhenzhen Xie
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA
| | - Hunter A Miller
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Victor H van Berkel
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY, USA
| | - Xiao-An Fu
- Department of Chemical Engineering, University of Louisville, Louisville, KY, USA.
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, USA; Department of Pharmacology/Toxicology, University of Louisville, Louisville, KY, USA; James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA; Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
| | - Sally A Suliman
- Banner University Medical Center, Phoenix, AZ, USA; Formerly at: Division of Pulmonary Medicine, University of Louisville, Louisville, KY, USA.
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Linh VTN, Kim H, Lee MY, Mun J, Kim Y, Jeong BH, Park SG, Kim DH, Rho J, Jung HS. 3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening. Biosens Bioelectron 2024; 244:115779. [PMID: 37922808 DOI: 10.1016/j.bios.2023.115779] [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: 08/08/2023] [Revised: 10/16/2023] [Accepted: 10/21/2023] [Indexed: 11/07/2023]
Abstract
A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care.
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Affiliation(s)
- Vo Thi Nhat Linh
- Department of Nano-Bio Convergence, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea
| | - Hongyoon Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Min-Young Lee
- Department of Nano-Bio Convergence, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea
| | - Jungho Mun
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Yeseul Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Byeong-Ho Jeong
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Sung-Gyu Park
- Department of Nano-Bio Convergence, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea
| | - Dong-Ho Kim
- Department of Nano-Bio Convergence, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea; Advanced Materials Engineering Division, University of Science and Technology (UST), Daejeon, 34113, South Korea.
| | - Junsuk Rho
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea; Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea; POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics, Pohang, 37673, South Korea.
| | - Ho Sang Jung
- Department of Nano-Bio Convergence, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea; Advanced Materials Engineering Division, University of Science and Technology (UST), Daejeon, 34113, South Korea; School of Convergence Science and Technology, Medical Science and Engineering, POSTECH, Pohang, 37673, South Korea.
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6
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Madama D, Carrageta DF, Guerra-Carvalho B, Botelho MF, Oliveira PF, Cordeiro CR, Alves MG, Abrantes AM. Impact of Different Treatment Regimens and Timeframes in the Plasmatic Metabolic Profiling of Patients with Lung Adenocarcinoma. Metabolites 2023; 13:1180. [PMID: 38132862 PMCID: PMC10744969 DOI: 10.3390/metabo13121180] [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: 10/27/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023] Open
Abstract
In recent years, the treatment of advanced non-small cell lung cancer (NSCLC) has suffered a variety of alterations. Chemotherapy (CTX), immunotherapy (IT) and tyrosine kinase inhibitors (TKI) have shown remarkable results. However, not all patients with NSCLC respond to these drug treatments or receive durable benefits. In this framework, metabolomics has been applied to improve the diagnosis, treatment, and prognosis of lung cancer and particularly lung adenocarcinoma (AdC). In our study, metabolomics was used to analyze plasma samples from 18 patients with AdC treated with CTX or IT via 1H-NMR spectroscopy. Relevant clinical information was gathered, and several biochemical parameters were also evaluated throughout the treatments. During the follow-up of patients undergoing CTX or IT, imaging control is recommended in order to assess the effectiveness of the therapy. This evaluation is usually performed every three treatments. Based on this procedure, all the samples were collected before the beginning of the treatment and after three and six treatments. The identified and quantified metabolites in the analyzed plasma samples were the following: isoleucine, valine, alanine, acetate, lactate, glucose, tyrosine, and formate. Multivariate/univariate statistical analyses were performed. Our data are in accordance with previous published results, suggesting that the plasma glucose levels of patients under CTX become higher throughout the course of treatment, which we hypothesize could be related to the tumor response to the therapy. It was also found that alanine levels become lower during treatment with CTX regimens, a fact that could be associated with frailty. NMR spectra of long responders' profiles also showed similar results. Based on the results of the study, metabolomics can represent a potential option for future studies, in order to facilitate patient selection and the monitoring of therapy efficacy in treated patients with AdC. Further studies are needed to improve the prospective identification of predictive markers, particularly glucose and alanine levels, as well as confer guidance to NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.
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Affiliation(s)
- Daniela Madama
- Clinical Academic Centre of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal
| | - David F. Carrageta
- Clinical and Experimental Endocrinology, UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal (M.G.A.)
- Laboratory for Integrative and Translational Research in Population Health (ITR), University of Porto, 4050-600 Porto, Portugal
| | - Bárbara Guerra-Carvalho
- Clinical and Experimental Endocrinology, UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal (M.G.A.)
- Laboratory for Integrative and Translational Research in Population Health (ITR), University of Porto, 4050-600 Porto, Portugal
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Maria F. Botelho
- Clinical Academic Centre of Coimbra (CACC), Centre for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal
| | - Pedro F. Oliveira
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Carlos R. Cordeiro
- Clinical Academic Centre of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Marco G. Alves
- Clinical and Experimental Endocrinology, UMIB—Unit for Multidisciplinary Research in Biomedicine, ICBAS—School of Medicine and Biomedical Sciences, University of Porto, 4050-313 Porto, Portugal (M.G.A.)
| | - Ana M. Abrantes
- Clinical Academic Centre of Coimbra (CACC), Centre for Innovative Biomedicine and Biotechnology (CIBB), Coimbra Institute for Clinical and Biomedical Research (iCBR), Biophysics Institute of Faculty of Medicine of University of Coimbra, Area of Environmental Genetics and Oncobiology (CIMAGO), 3000-548 Coimbra, Portugal
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Miller DM, Yadanapudi K, Rai V, Rai SN, Chen J, Frieboes HB, Masters A, McCallum A, Williams BJ. Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach. Am J Med Sci 2023; 366:185-198. [PMID: 37330006 DOI: 10.1016/j.amjms.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/01/2023] [Accepted: 06/13/2023] [Indexed: 06/19/2023]
Abstract
Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.
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Affiliation(s)
- Donald M Miller
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA.
| | - Kavitha Yadanapudi
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA
| | - Veeresh Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA
| | - Shesh N Rai
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Biostatistics and Informatics Shared Resources, University of Cincinnati Cancer Center, Cincinnati, OH, USA; Cancer Data Science Center of University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Joseph Chen
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA; Center for Preventative Medicine, University of Louisville, Louisville, KY, USA
| | - Adrianna Masters
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Radiation Oncology, University of Louisville, Louisville, KY, USA
| | - Abigail McCallum
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
| | - Brian J Williams
- Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA
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8
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Joshi AD, Rahnavard A, Kachroo P, Mendez KM, Lawrence W, Julián-Serrano S, Hua X, Fuller H, Sinnott-Armstrong N, Tabung FK, Shutta KH, Raffield LM, Darst BF. An epidemiological introduction to human metabolomic investigations. Trends Endocrinol Metab 2023; 34:505-525. [PMID: 37468430 PMCID: PMC10527234 DOI: 10.1016/j.tem.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.
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Affiliation(s)
- Amit D Joshi
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wayne Lawrence
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sachelly Julián-Serrano
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA
| | - Xinwei Hua
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nasa Sinnott-Armstrong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fred K Tabung
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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9
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Shestakova KM, Moskaleva NE, Boldin AA, Rezvanov PM, Shestopalov AV, Rumyantsev SA, Zlatnik EY, Novikova IA, Sagakyants AB, Timofeeva SV, Simonov Y, Baskhanova SN, Tobolkina E, Rudaz S, Appolonova SA. Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer. Sci Rep 2023; 13:11072. [PMID: 37422585 PMCID: PMC10329697 DOI: 10.1038/s41598-023-38140-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/04/2023] [Indexed: 07/10/2023] Open
Abstract
Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.
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Affiliation(s)
- Ksenia M Shestakova
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Natalia E Moskaleva
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Andrey A Boldin
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Pavel M Rezvanov
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | | | - Sergey A Rumyantsev
- Pirogov Russian National Research Medical University, Moscow, Russia, 117997
| | - Elena Yu Zlatnik
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Inna A Novikova
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Alexander B Sagakyants
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Sofya V Timofeeva
- National Medical Research Centre for Oncology (Rostov-On-Don, Russia), 14 Liniya, 63, Rostov-on-Don, Russia, 344019
| | - Yuriy Simonov
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
| | - Sabina N Baskhanova
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
| | - Elena Tobolkina
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206, Geneva 4, Switzerland.
| | - Serge Rudaz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206, Geneva 4, Switzerland
| | - Svetlana A Appolonova
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia, 119435
- I.M. Sechenov First Moscow State Medical University, Moscow, Russia, 119435
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10
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Kim KS, Moon SW, Moon MH, Hyun KY, Kim SJ, Kim YK, Kim KY, Jekarl DW, Oh EJ, Kim Y. Metabolic profiles of lung adenocarcinoma via peripheral blood and diagnostic model construction. Sci Rep 2023; 13:7304. [PMID: 37147444 PMCID: PMC10163250 DOI: 10.1038/s41598-023-34575-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/03/2023] [Indexed: 05/07/2023] Open
Abstract
The metabolic profile of cancerous cells is shifted to meet the cellular demand required for proliferation and growth. Here we show the features of cancer metabolic profiles using peripheral blood of healthy control subjects (n = 78) and lung adenocarcinoma (LUAD) patients (n = 64). Among 121 detected metabolites, diagnosis of LUAD is based on arginine, lysophosphatidylcholine-acyl (Lyso.PC.a) C16:0, and PC-diacyl (PC.aa) C38:3. Network analysis revealed that network heterogeneity, diameter, and shortest path were decreased in LUAD. On the contrary, these parameters were increased in advanced-stage compared to early-stage LUAD. Clustering coefficient, network density, and average degree were increased in LUAD compared to the healthy control, whereas these topologic parameters were decreased in advanced-stage compared to early-stage LUAD. Public LUAD data verified that the genes encoding enzymes for arginine (NOS, ARG, AZIN) and for Lyso.PC and PC (CHK, PCYT, LPCAT) were related with overall survival. Further studies are required to verify these results with larger samples and other histologic types of lung cancer.
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Affiliation(s)
- Kyung Soo Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seok Whan Moon
- Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Mi Hyung Moon
- Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kwan Yong Hyun
- Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung Joon Kim
- Department of Pulmonology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Young Koon Kim
- Department of Pulmonology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kwang Youl Kim
- Department of Clinical Pharmacology, Inha University Hospital, Inha University, 27 Inhang-ro, Jung-gu, Incheon, 22332, Republic of Korea.
| | - Dong Wook Jekarl
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-Daero, Seocho-gu, Seoul, 06591, Republic of Korea.
- Research and Development Institute for In Vitro Diagnostic Medical Devices, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Eun-Jee Oh
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-Daero, Seocho-gu, Seoul, 06591, Republic of Korea
- Research and Development Institute for In Vitro Diagnostic Medical Devices, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yonggoo Kim
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-Daero, Seocho-gu, Seoul, 06591, Republic of Korea
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11
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Miller HA, Miller DM, van Berkel VH, Frieboes HB. Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling. Ann Biomed Eng 2023; 51:820-832. [PMID: 36224485 PMCID: PMC10023290 DOI: 10.1007/s10439-022-03096-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/02/2022] [Indexed: 11/28/2022]
Abstract
The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
| | - Donald M Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
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12
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Danzi F, Pacchiana R, Mafficini A, Scupoli MT, Scarpa A, Donadelli M, Fiore A. To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduct Target Ther 2023; 8:137. [PMID: 36949046 PMCID: PMC10033890 DOI: 10.1038/s41392-023-01380-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 03/24/2023] Open
Abstract
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
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Affiliation(s)
- Federica Danzi
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Raffaella Pacchiana
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Andrea Mafficini
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Maria T Scupoli
- Department of Neurosciences, Biomedicine and Movement Sciences, Biology and Genetics Section, University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
- ARC-NET Research Centre, University and Hospital Trust of Verona, Verona, Italy
| | - Massimo Donadelli
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
| | - Alessandra Fiore
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
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13
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Shen QM, Tan YT, Wang J, Fang J, Liu DK, Li HL, Xiang YB. Cross-sectional relationships between general and central adiposity and plasma amino acids in Chinese adults. Amino Acids 2023:10.1007/s00726-023-03258-5. [PMID: 36881189 DOI: 10.1007/s00726-023-03258-5] [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] [Received: 07/03/2022] [Accepted: 02/23/2023] [Indexed: 03/08/2023]
Abstract
Adiposity is an important determinant of blood metabolites, but little is known about the variations of blood amino acids according to general and central adiposity status among Chinese population. This study included 187 females and 322 males who were cancer-free subjects randomly selected from two cohorts in Shanghai, China. Participants' plasma concentrations of amino acids were measured by ultra-performance liquid chromatography coupled to tandem mass spectrometry. Linear regression models were used to examine the cross-sectional correlations between general and central adiposity and amino acid levels. A total of 35 amino acids in plasma were measured in this study. In females, alanine, aspartic acid and pyroglutamic acid were positively correlated with general adiposity. In males, glutamic acid, aspartic acid, valine and pyroglutamic acid showed positive correlations, and glutamine, serine and glycine showed negative correlations with both general and central adiposity; phenylalanine, isoleucine and leucine were positively correlated and N-phenylacetylglutamine was negatively correlated with general adiposity; asparagine was negatively correlated with central adiposity. In summary, general adiposity and central adiposity were correlated with the concentrations of specific plasma amino acids among cancer-free female and male adults in China. Adiposity-metabolite characteristics and relationships should be considered when studying blood biomarkers for adiposity-related health outcomes.
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Affiliation(s)
- Qiu-Ming Shen
- School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 25, Lane 2200, Xie Tu Road, Shanghai, 200032, China
| | - Yu-Ting Tan
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 25, Lane 2200, Xie Tu Road, Shanghai, 200032, China
| | - Jing Wang
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 25, Lane 2200, Xie Tu Road, Shanghai, 200032, China
| | - Jie Fang
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 25, Lane 2200, Xie Tu Road, Shanghai, 200032, China
| | - Da-Ke Liu
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 25, Lane 2200, Xie Tu Road, Shanghai, 200032, China
| | - Hong-Lan Li
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 25, Lane 2200, Xie Tu Road, Shanghai, 200032, China
| | - Yong-Bing Xiang
- School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 25, Lane 2200, Xie Tu Road, Shanghai, 200032, China.
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14
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Xie RL, Wang Y, Zhao YN, Zhang J, Chen GB, Fei J, Fu Z. Lung nodule pre-diagnosis and insertion path planning for chest CT images. BMC Med Imaging 2023; 23:22. [PMID: 36737717 PMCID: PMC9896815 DOI: 10.1186/s12880-023-00973-z] [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: 05/07/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture. The overall process included lung parenchyma segmentation, classification and pre-diagnosis, 3-D reconstruction and path planning, and experimental verification. First, accurate lung parenchyma segmentation in chest CT images was achieved using digital image processing technologies, such as adaptive gray threshold, connected area labeling, and mathematical morphological boundary repair. Multi-feature weight assignment was then adopted to establish a multi-level classification criterion to complete the classification and pre-diagnosis of pulmonary nodules. Next, 3-D reconstruction of lung regions was performed using voxelization, and on its basis, a feasible local optimal insertion path with an insertion point could be found by avoiding sternums and/or key tissues in terms of the needle-inserting path. Finally, CT images of 900 patients from Lung Image Database Consortium and Image Database Resource Initiative were chosen to verify the validity of pulmonary nodule diagnosis. Our previously designed surgical robotic system and a custom thoracic model were used to validate the effectiveness of the insertion path. This work can not only assist doctors in completing the pre-diagnosis of pulmonary nodules but also provide a reference for clinical biopsy puncture of suspected malignant nodules considered by doctors.
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Affiliation(s)
- Rong-Li Xie
- grid.16821.3c0000 0004 0368 8293Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Yao Wang
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Yan-Na Zhao
- grid.24516.340000000123704535Department of Ultrasound, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065 China
| | - Jun Zhang
- grid.16821.3c0000 0004 0368 8293Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Guang-Biao Chen
- grid.16821.3c0000 0004 0368 8293State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Jian Fei
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Zhuang Fu
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China.
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15
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Yao Y, Zhang H, Tu L, Yu T, Chen B, Huang P, Hu Y, Luan T. Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch Samples. Anal Chem 2023; 95:1309-1317. [PMID: 36538611 DOI: 10.1021/acs.analchem.2c04188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography-mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.
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Affiliation(s)
- Yao Yao
- Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou510275, China
| | - Hui Zhang
- Metabolic Innovation Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou510080, China.,School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou510006, China.,Platform of Metabolomics, Center for Precision Medicine, Sun Yat-Sen University, Guangzhou510080, China
| | - Lanyin Tu
- Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou510275, China
| | - Tiantian Yu
- Metabolic Innovation Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou510080, China
| | - Baowei Chen
- Southern Marine Science and Engineering Guangdong Laboratory, School of Marine Sciences, Sun Yat-Sen University, Zhuhai519082, China
| | - Peng Huang
- State Key Laboratory of Oncology in South China, Cancer Metabolism and Intervention Research Center, Sun Yat-Sen University Cancer Center, Guangzhou510060, China.,Metabolic Innovation Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou510080, China
| | - Yumin Hu
- State Key Laboratory of Oncology in South China, Cancer Metabolism and Intervention Research Center, Sun Yat-Sen University Cancer Center, Guangzhou510060, China.,Metabolic Innovation Center, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou510080, China
| | - Tiangang Luan
- Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou510275, China.,Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou510006, China
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16
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Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:9460019. [PMID: 36046366 PMCID: PMC9420632 DOI: 10.1155/2022/9460019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/15/2022] [Indexed: 11/18/2022]
Abstract
Background The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases. Methods Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified. Results In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions. Conclusion Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.
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17
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López-López Á, Ciborowski M, Niklinski J, Barbas C, López-Gonzálvez Á. Optimization of capillary electrophoresis coupled to negative mode electrospray ionization-mass spectrometry using polyvinyl alcohol coated capillaries. Application to a study on non-small cell lung cancer. Anal Chim Acta 2022; 1226:340259. [DOI: 10.1016/j.aca.2022.340259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/13/2022] [Accepted: 08/11/2022] [Indexed: 11/01/2022]
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18
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Siriwong N, Sukaram T, Tansawat R, Apiparakoon T, Tiyarattanachai T, Marukatat S, Rerknimitr R, Chaiteerakij R. Exhaled volatile organic compounds for cholangiocarcinoma diagnosis. LIVER RESEARCH 2022. [DOI: 10.1016/j.livres.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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19
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Brouwer AF, Engle JM, Jeon J, Meza R. Sociodemographic Survival Disparities for Lung Cancer in the United States, 2000-2016. J Natl Cancer Inst 2022; 114:1492-1500. [PMID: 35866998 PMCID: PMC9664170 DOI: 10.1093/jnci/djac144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/02/2022] [Accepted: 07/19/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Understanding the impact of patient and tumor characteristics on lung cancer survival can help build personalized prognostic models and identify health disparities. METHODS We identified 557 555 patients aged 25 years and older diagnosed with lung or bronchus carcinoma from the Surveillance, Epidemiology, and End Results database, 2000-2016. We estimated hazard ratios (HR) for demographic (sex, age, race and ethnicity), tumor (stage, histology, year of diagnosis), and geographic characteristics (census tract-level urbanicity, socioeconomic status [SES]), as well as selected interactions, on the rate of lung cancer-specific death using multivariable proportional hazards models. RESULTS Women had a higher survival (lower hazard) of lung cancer-specific death than men (HR = 0.83, 95% confidence interval [CI] = 0.82 to 0.83). Hazards differed by race and ethnicity. Regional (HR = 2.41, 95% CI = 2.37 to 2.44) and distant (HR = 6.61, 95% CI = 6.53 to 6.69) tumors were associated with a lower survival (higher hazard) than localized tumors. Small cell tumors were associated with a lower survival (HR = 1.19, 95% CI = 1.18 to 1.20) than non-small cell tumors. Patients diagnosed after 2009 had lower hazards (HR = 0.86, 95% CI = 085 to 0.86) than those diagnosed 2000-2009. Lung cancer-specific survival did not depend on urbanicity after adjusting for census tract-level SES, but survival decreased with decreasing census tract-level SES. Differences in survival between non-Hispanic Black and White patients were greater for younger patients and localized tumors and increased with census tract-level SES. Differences by sex were greatest for young patients and localized tumors. CONCLUSIONS Disparities in survival after lung cancer diagnosis remain, with intersectional patterns suggesting differential access to and quality of care. Efforts are needed to ensure that high-risk groups receive guideline-concordant treatment.
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Affiliation(s)
- Andrew F Brouwer
- Correspondence to: Andrew F. Brouwer, PhD, Department of Epidemiology, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA (e-mail: )
| | - Jason M Engle
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
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Miller HA, van Berkel VH, Frieboes HB. Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data. Metabolomics 2022; 18:57. [PMID: 35857204 PMCID: PMC9737952 DOI: 10.1007/s11306-022-01918-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 06/30/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value. OBJECTIVES Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies. METHODS Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility. RESULTS Overall survival (OS) is predicted in external validation cohort with AUROCTEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROCTEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased. CONCLUSION This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
| | - Victor H van Berkel
- UofL Health-Brown Cancer Center, University of Louisville, Louisville, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA.
- UofL Health-Brown Cancer Center, University of Louisville, Louisville, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, USA.
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21
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Yonar D, Severcan M, Gurbanov R, Sandal A, Yilmaz U, Emri S, Severcan F. Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum. Biochim Biophys Acta Mol Basis Dis 2022; 1868:166473. [PMID: 35753541 DOI: 10.1016/j.bbadis.2022.166473] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 06/06/2022] [Accepted: 06/19/2022] [Indexed: 02/01/2023]
Abstract
Malignant pleural mesothelioma (MPM), an aggressive cancer associated with exposure to fibrous minerals, can only be diagnosed in the advanced stage because its early symptoms are also connected with other respiratory diseases. Hence, understanding the molecular mechanism and the discrimination of MPM from other lung diseases at an early stage is important to apply effective treatment strategies and for the increase in survival rate. This study aims to develop a new approach for characterization and diagnosis of MPM among lung diseases from serum by Fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis. The detailed spectral characterization studies indicated the changes in lipid biosynthesis and nucleic acids levels in the malignant serum samples. Furthermore, the results showed that healthy, benign exudative effusion, lung cancer, and MPM groups were successfully separated from each other by applying principal component analysis (PCA), support vector machine (SVM), and especially linear discriminant analysis (LDA) to infrared spectra.
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Affiliation(s)
- Dilek Yonar
- Middle East Technical University, Department of Biological Sciences, Ankara, Turkey; Yuksek Ihtisas University, Faculty of Medicine, Biophysics Department, Ankara, Turkey
| | - Mete Severcan
- Middle East Technical University, Department of Electrical and Electronics Engineering, Ankara, Turkey
| | - Rafig Gurbanov
- Bilecik Seyh Edebali University, Department of Bioengineering, Bilecik, Turkey
| | - Abdulsamet Sandal
- Hacettepe University, Faculty of Medicine, Department of Chest Diseases, Ankara, Turkey; Ankara Occupational and Environmental Diseases Hospital, Ankara, Turkey
| | - Ulku Yilmaz
- Atatürk Chest Diseases and Chest Surgery Training and Research Hospital, Ankara, Turkey
| | - Salih Emri
- Hacettepe University, Faculty of Medicine, Department of Chest Diseases, Ankara, Turkey; Medicana Hospital, Department of Chest Diseases, Kadikoy, Istanbul, Turkey
| | - Feride Severcan
- Middle East Technical University, Department of Biological Sciences, Ankara, Turkey; Altinbas University, Faculty of Medicine, Biophysics Department, Istanbul, Turkey.
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22
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Exhaled Breath Volatile Organic Compound Analysis for the Detection of Lung Cancer- A Systematic Review. JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING 2022. [DOI: 10.4028/p-dab04j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A rapid and effective diagnostic method is essential for lung cancer since it shows symptoms only at its advanced stage. Research is being carried out in the area of exhaled breath analysis for the diagnosis of various pulmonary diseases including lung cancer. In this method exhaled breath volatile organic compounds (VOC) are analyzed with various techniques such as gas chromatography-mass spectrometry, ion mobility spectrometry, and electronic noses. The VOC analysis is suitable for lung cancer detection since it is non-invasive, fast, and also a low-cost method. In addition, this technique can detect primary stage nodules. This paper presents a systematic review of the various method employed by researchers in the breath analysis field. The articles were selected through various search engines like EMBASE, Google Scholar, Pubmed, and Google. In the initial screening process, 214 research papers were selected using various inclusion and exclusion criteria and finally, 55 articles were selected for the review. The results of the reviewed studies show that detection of lung cancer can be effectively done using the VOC analysis of exhaled breath. The results also show that this method can be used for detecting the different stages and histology of lung cancer. The exhaled breath VOC analysis technique will be popular in the future, bypassing the existing imaging techniques. This systematic review conveys the recent research opportunities, obstacles, difficulties, motivations, and suggestions associated with the breath analysis method for lung cancer detection.
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23
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Miller HA, Rai SN, Yin X, Zhang X, Chesney JA, van Berkel VH, Frieboes HB. Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival. Metabolomics 2022; 18:31. [PMID: 35567637 PMCID: PMC9724684 DOI: 10.1007/s11306-022-01891-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/19/2022] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive. OBJECTIVES This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS. METHODS Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events. RESULTS Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates). CONCLUSION Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
| | - Shesh N Rai
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
| | - Xinmin Yin
- Department of Chemistry, University of Louisville, Louisville, USA
| | - Xiang Zhang
- Department of Chemistry, University of Louisville, Louisville, USA
| | - Jason A Chesney
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
- Division of Medical Oncology and Hematology, Department of Medicine, University of Louisville, Louisville, USA
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, USA
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, USA.
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Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets. Cancers (Basel) 2022; 14:cancers14061564. [PMID: 35326714 PMCID: PMC8945961 DOI: 10.3390/cancers14061564] [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: 02/21/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Thymomas and thymic carcinomas (TCs) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. This is the first metabolomics investigation on thymic epithelial tumors employing nuclear magnetic resonance spectroscopy of tissue samples. We could detect and quantify up to 37 metabolites in the major tumor subtypes, including acetylcholine that was not previously detected in other non-endocrine cancers. A metabolite-based cluster analysis distinguished three clinically relevant tumor subgroups, namely indolent and aggressive thymomas, as well as TCs. A metabolite-based metabolic pathway analysis also gave hints to activated metabolic pathways shared between aggressive thymomas and TCs. This finding was largely backed by enrichment of these pathways at the transcriptomic level in a large, publicly available, independent TET dataset. Due to the differential expression of metabolites in thymic epithelial tumors versus normal thymus, pathways related to proline, cysteine, glutathione, lactate and glutamine appear as promising therapeutic targets. From these findings, inhibitors of glutaminolysis and of the downstream TCA cycle are anticipated to be rational therapeutic strategies. If our results can be confirmed in future, sufficiently powered studies, metabolic signatures may contribute to the identification of new therapeutic options for aggressive thymomas and TCs. Abstract Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public “The Cancer Genome Atlas” (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong “Warburg effect”, glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.
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25
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Yu C, Zhang Q, Zhang Y, Wang L, Xu H, Bi K, Li D, Li Q. Isotope Labelled in suit Derivatization-Extraction Integrated System for Amine/Phenol Submetabolome Analysis based on Nanoconfinement Effect: Application to Lung Cancer. J Chromatogr A 2022; 1670:462954. [DOI: 10.1016/j.chroma.2022.462954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 02/22/2022] [Accepted: 03/09/2022] [Indexed: 10/18/2022]
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Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data. Ann Biomed Eng 2022; 50:314-329. [PMID: 35083584 PMCID: PMC9743982 DOI: 10.1007/s10439-022-02904-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 01/01/2022] [Indexed: 12/15/2022]
Abstract
Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.
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Singh A, Prakash V, Gupta N, Kumar A, Kant R, Kumar D. Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach. ACS OMEGA 2022; 7:5510-5520. [PMID: 35187366 PMCID: PMC8851899 DOI: 10.1021/acsomega.1c06941] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/18/2022] [Indexed: 06/01/2023]
Abstract
Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.
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Affiliation(s)
- Anjana Singh
- All
India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand 249201, India
- Pulmonary
& Critical Care Medicine, King George’s
Medical University, Lucknow, Uttar Pradesh 226003, India
| | - Ved Prakash
- Pulmonary
& Critical Care Medicine, King George’s
Medical University, Lucknow, Uttar Pradesh 226003, India
| | - Nikhil Gupta
- Centre
of Biomedical Research (CBMR), SGPGIMS, Lucknow, Uttar Pradesh 226014, India
- Department
of Chemistry, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
| | - Ashish Kumar
- Department
of Chemistry, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
| | - Ravi Kant
- All
India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand 249201, India
| | - Dinesh Kumar
- Centre
of Biomedical Research (CBMR), SGPGIMS, Lucknow, Uttar Pradesh 226014, India
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Metabolomics and the Multi-Omics View of Cancer. Metabolites 2022; 12:metabo12020154. [PMID: 35208228 PMCID: PMC8880085 DOI: 10.3390/metabo12020154] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 11/17/2022] Open
Abstract
Cancer is widely regarded to be a genetic disease. Indeed, over the past five decades, the genomic perspective on cancer has come to almost completely dominate the field. However, this genome-only view is incomplete and tends to portray cancer as a disease that is highly heritable, driven by hundreds of complex genetic interactions and, consequently, difficult to prevent or treat. New evidence suggests that cancer is not as heritable or purely genetic as once thought and that it really is a multi-omics disease. As highlighted in this review, the genome, the exposome, and the metabolome all play roles in cancer’s development and manifestation. The data presented here show that >90% of cancers are initiated by environmental exposures (the exposome) which lead to cancer-inducing genetic changes. The resulting genetic changes are, then, propagated through the altered DNA of the proliferating cancer cells (the genome). Finally, the dividing cancer cells are nourished and sustained by genetically reprogrammed, cancer-specific metabolism (the metabolome). As shown in this review, all three “omes” play roles in initiating cancer. Likewise, all three “omes” interact closely, often providing feedback to each other to sustain or enhance tumor development. Thanks to metabolomics, these multi-omics feedback loops are now much more evident and their roles in explaining the hallmarks of cancer are much better understood. Importantly, this more holistic, multi-omics view portrays cancer as a disease that is much more preventable, easier to understand, and potentially, far more treatable.
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Analysis on the Effects of CT- and Ultrasound-Guided Percutaneous Transthoracic Needle Biopsy Combined with Serum CA125 and CEA on the Diagnosis of Lung Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2289432. [PMID: 35035813 PMCID: PMC8759864 DOI: 10.1155/2022/2289432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/29/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022]
Abstract
The number of patients with lung cancer is difficultly diagnosed in the early stage. The purpose of the study was to investigate the effects of CT- and ultrasound-guided percutaneous transthoracic needle biopsy combined with serum CA125 and CEA on the diagnosis of lung cancer. 120 patients with suspected lung cancer admitted to our hospital from January 2019 to January 2020 were selected and divided into an ultrasound group (n = 60) and CT group (n = 60), according to different percutaneous transthoracic needle biopsy modalities. All patients received serum tumor markers detection, so as to compare the CT- and ultrasound-guided percutaneous transthoracic needle biopsy results and pathology results, levels of serum tumor markers among all patients and the patients with different lung cancer types, and diagnostic efficacy of tumor markers, as well as complication rate (CR) in patients. The sensitivity and specificity of ultrasound-guided percutaneous transthoracic needle biopsy were 0.880 and 0.800, respectively, while those of CT-guided percutaneous transthoracic needle biopsy were 0.909 and 0.625, respectively; the CA125 and CEA levels in the lung cancer group were higher than those in the benign group (P < 0.001); the CA125 and CEA levels of the patients with adenocarcinoma were higher than those with squamous carcinoma, and the CEA levels of the patients with small-cell carcinoma were lower than those with adenocarcinoma (P < 0.05); the sensitivity, specificity, and Youden indexes of CA125 were 0.638, 0.833, and 0.471, respectively, while those of CEA were 0.766, 0.778, and 0.544, respectively; there were no significant differences in CR between the two groups (P > 0.05). CT- and ultrasound-guided percutaneous transthoracic needle biopsy is a safe and feasible diagnostic modality for lung cancer, and its combination with serum CA125 and CEA can significantly improve the accuracy of the detection results, which is worthy of promotion and application in clinical practice.
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Wu WS, Wu HY, Wang PH, Chen TY, Chen KR, Chang CW, Lee DE, Lin BH, Chang WCW, Liao PC. LCMD: Lung Cancer Metabolome Database. Comput Struct Biotechnol J 2022; 20:65-78. [PMID: 34976312 PMCID: PMC8683384 DOI: 10.1016/j.csbj.2021.12.002] [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: 08/21/2021] [Revised: 11/24/2021] [Accepted: 12/03/2021] [Indexed: 01/26/2023] Open
Abstract
Lung cancer, one of the most common causes of cancer-related death worldwide, has been associated with high treatment cost and imposed great burdens. The 5-year postoperative survival rate of lung cancer (13%) is lower than many other leading cancers indicating the urgent needs to dissect its pathogenic mechanisms and discover specific biomarkers. Although several proteins have been proposed to be potential candidates for the diagnosis of lung cancer, they present low accuracy in clinical settings. Metabolomics has thus emerged as a very promising tool for biomarker discovery. To date, many lung cancer-related metabolites have been highlighted in the literature but no database is available for scientists to retrieve this information. Herein, we construct and introduce the first Lung Cancer Metabolome Database (LCMD), a freely available online database depositing 2013 lung cancer-related metabolites identified from 65 mass spectrometry-based lung cancer metabolomics studies. Researchers are able to explore LCMD via two ways. Firstly, by applying various filters in the “Browse Metabolites” mode, users can access a list of lung cancer-related metabolites that satisfy the filter specifications. For each metabolite, users can acquire the value of the fold change (cancer/normal), statistical significance (p-value) of the fold change, and the comparative research designs of all the mass spectrometry-based lung cancer metabolomics studies that identify this metabolite. Secondly, by applying various filters in the “Browse Studies” mode, users can obtain a list of mass spectrometry-based lung cancer metabolomics studies that satisfy the filter specifications. For each study, users can view the type of studied specimen, mass spectrometry (MS) method, MS data processing software, and differential analysis method, as well as all the identified lung cancer-related metabolites. Furthermore, the overview of each study is clearly illustrated by a graphical summary. The LCMD (http://cosbi7.ee.ncku.edu.tw/LCMD/) is the first database that brings together the meaningful information of lung cancer-related metabolites. The development of the LCMD is envisioned to promote the biomarker discovery of lung cancer.
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Affiliation(s)
- Wei-Sheng Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Hsin-Yi Wu
- Instrumentation Center, National Taiwan University, Taipei 10617, Taiwan
| | - Pin-Hsuan Wang
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan 70101, Taiwan
| | - Ting-Yu Chen
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Kuan-Ru Chen
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Chih-Wei Chang
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan 70101, Taiwan
| | - Dong-En Lee
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Bo-Heng Lin
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - William Chih-Wei Chang
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan 70101, Taiwan.,School of Pharmacy, Kaohsiung Medical University, Kaohsiung 807, Taiwan.,Master Degree Program in Toxicology, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Pao-Chi Liao
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan 70101, Taiwan.,Department of Food Safety / Hygiene and Risk Management, National Cheng Kung University, Tainan 70101, Taiwan
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Choueiry F, Zhu J. Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) fingerprinting enabled treatment monitoring of pulmonary carcinoma cells in real time. Anal Chim Acta 2022; 1189:339230. [PMID: 34815037 PMCID: PMC8613447 DOI: 10.1016/j.aca.2021.339230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/05/2021] [Accepted: 10/28/2021] [Indexed: 01/04/2023]
Abstract
Lung cancer is one of the leading causes of cancer related deaths in the United States. A novel volatile analysis platform is needed to complement current diagnostic techniques and better elucidate chemical signatures of lung cancer and subsequent treatments. A systems biology bottom-up approach using cell culture volatilomics was employed to identify pathological volatile fingerprints of lung cancer in real time. An advanced secondary electrospray ionization (SESI) source, named SuperSESI was used in this study and directly attached to a Thermo Q-Exactive high-resolution mass spectrometer (HRMS). We performed a series of experiments to determine if our optimized SESI-HRMS platform can distinguish between cancer types by sampling their in vitro volatilome profiles. We detected 60 significant volatile organic compound (VOC) features in positive mode that were deemed of cancer cell origin. The cell derived features were used for subsequent analyses to distinguish between our two studied lung cancer types, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Partial least squares-discriminant analysis (PLS-DA) model revealed a good separation of the two cancer types, suggesting unique chemical composition of their headspace profiles. A receiver operating characteristic (ROC) curve using 10 prominent features was used to predict disease type, with an area under the curve (AUC) of 0.811. Cultures were also treated with cisplatin to determine the feasibility of classifying drug treatment from expelled gases. A PLS-DA model revealed independent clustering based on their headspace profiles. An ROC curve using the top features driving separation of PLS-DA model suggested good accuracy with an AUC of 1. It is thus possible to benefit from the advantages of this platform to distinguish the unique volatile fingerprints of cancers to uncover potential biomarkers for cancer type differentiation and treatment monitoring.
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Affiliation(s)
- Fouad Choueiry
- Department of Human Sciences, The Ohio State University; Columbus, OH 43210
| | - Jiangjiang Zhu
- Department of Human Sciences, The Ohio State University; Columbus, OH 43210, James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210
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Contribution of branched chain amino acids to energy production and mevalonate synthesis in cancer cells. Biochem Biophys Res Commun 2021; 585:61-67. [PMID: 34794035 DOI: 10.1016/j.bbrc.2021.11.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 11/09/2021] [Indexed: 12/25/2022]
Abstract
Leucine, isoleucine and valine, known as branched chain amino acids (BCAAs), have been reported to be degraded by different cancer cells, and their biodegradation pathways have been suggested as anticancer targets. However, the mechanisms by which the degradation of BCAAs could support the growth of cancer cells remains unclear. In this work, 13C experiments have been carried out in order to elucidate the metabolic role of BCAA degradation in two breast cancer cell lines (MCF-7 and BCC). The results revealed that up to 36% of the energy production via respiration by MCF-7 cells was supported by the degradation of BCAAs. Also, 67% of the mevalonate (the precursor of cholesterol) synthesized by the cells was coming from the degradation of leucine. The results were lower for BCC cells (14 and 30%, respectively). The non-tumorigenic epythelial cell line MCF-10A was used as a control, showing that 10% of the mitochondrial acetyl-CoA comes from the degradation of BCAAs and no mevalonate production. Metabolic flux analysis around the mevalonate node, also revealed that significant amounts of acetoacetate are being produced from BCAA derived carbon, which could be at the source of lipid synthesis. From these results we can conclude that the degradation of BCAAs is an important energy and carbon source for the proliferation of some cancer cells and its therapeutic targeting could be an interesting option.
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Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges. Biomed Pharmacother 2021; 146:112488. [PMID: 34894516 DOI: 10.1016/j.biopha.2021.112488] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 12/20/2022] Open
Abstract
Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.
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Pedersen S, Hansen JB, Maltesen RG, Szejniuk WM, Andreassen T, Falkmer U, Kristensen SR. Identifying metabolic alterations in newly diagnosed small cell lung cancer patients. Metabol Open 2021; 12:100127. [PMID: 34585134 PMCID: PMC8455369 DOI: 10.1016/j.metop.2021.100127] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/07/2021] [Accepted: 09/14/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Small cell lung cancer (SCLC) is a malignant disease with poor prognosis. At the time of diagnosis most patients are already in a metastatic stage. Current diagnosis is based on imaging, histopathology, and immunohistochemistry, but no blood-based biomarkers have yet proven to be clinically successful for diagnosis and screening. The precise mechanisms of SCLC are not fully understood, however, several genetic mutations, protein and metabolic aberrations have been described. We aim at identifying metabolite alterations related to SCLC and to expand our knowledge relating to this aggressive cancer. METHODS A total of 30 serum samples of patients with SCLC, collected at the time of diagnosis, and 25 samples of healthy controls were included in this study. The samples were analyzed with nuclear magnetic resonance spectroscopy. Multivariate, univariate and pathways analyses were performed. RESULTS Several metabolites were identified to be altered in the pre-treatment serum samples of small-cell lung cancer patients compared to healthy individuals. Metabolites involved in tricarboxylic acid cycle (succinate: fold change (FC) = 2.4, p = 0.068), lipid metabolism (LDL triglyceride: FC = 1.3, p = 0.001; LDL-1 triglyceride: FC = 1.3, p = 0.012; LDL-2 triglyceride: FC = 1.4, p = 0.009; LDL-6 triglyceride: FC = 1.5, p < 0.001; LDL-4 cholesterol: FC = 0.5, p = 0.007; HDL-3 free cholesterol: FC = 0.7, p = 0.002; HDL-4 cholesterol FC = 0.8, p < 0.001; HDL-4 apolipoprotein-A1: FC = 0.8, p = 0.005; HDL-4 apolipoprotein-A2: FC ≥ 0.7, p ≤ 0.001), amino acids (glutamic acid: FC = 1.7, p < 0.001; glutamine: FC = 0.9, p = 0.007, leucine: FC = 0.8, p < 0.001; isoleucine: FC = 0.8, p = 0.016; valine: FC = 0.9, p = 0.032; lysine: FC = 0.8, p = 0.004; methionine: FC = 0.8, p < 0.001; tyrosine: FC = 0.7, p = 0.002; creatine: FC = 0.9, p = 0.030), and ketone body metabolism (3-hydroxybutyric acid FC = 2.5, p < 0.001; acetone FC = 1.6, p < 0.001), among other, were found deranged in SCLC. CONCLUSIONS This study provides novel insight into the metabolic disturbances in pre-treatment SCLC patients, expanding our molecular understanding of this malignant disease.
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Affiliation(s)
- Shona Pedersen
- Department of Basic Medical Science, College of Medicine, Qatar University, QU Health, Doha, Qatar
| | | | - Raluca Georgiana Maltesen
- Translational Radiation Biology and Oncology Laboratory, Centre for Cancer Research, Westmead Institute of Medical Research, Westmead, 2145, Australia
| | - Weronika Maria Szejniuk
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark
| | - Trygve Andreassen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ursula Falkmer
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark
| | - Søren Risom Kristensen
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Department of Clinical Biochemistry, Aalborg University Hospital, Aalborg, Denmark
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Murashka DI, Tahanovich AD, Kauhanka MM, Prokhorova VI, Gotko OV. [Diagnostic efficiency of determining CXCR1, CXCR2 and hyaluronic acid blood level in non-small cell lung cancer patients]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2021; 67:434-442. [PMID: 34730557 DOI: 10.18097/pbmc20216705434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In the structure of lung cancer incidence most cases belong to non-small cell lung cancer (NSCLC) which is subdivided into two histological subtypes: adenocarcinoma (AC) and squamous cell carcinoma (SCC). A five-year survival rate of patients with stage I NSCLC is two times higher than in patients with stage II and more than five times higher than in stages III-IV. Currently, there are no informative blood biomarkers to diagnose early stages of NSCLC. The aim of the study was to evaluate complex determination of hyaluronic acid (HA), CXCR2 and CXCR1 levels blood of patients with AC and SCC. Blood samples from of 107 patients with SCC, 90 patients with AC, and 40 healthy people were used in this study. Concentration of HA in blood serum was determined by enzyme linked immunoassay. The level of CXCR2 and CXCR1 was determined by flow cytometry. Diagnostic parameters were determined by constructing mathematical models in the form of regression equations using the method of stepwise inclusion of predictors and subsequent ROC-analysis. Results of the study indicate that MFI CXCR1 in granulocytes, proportion of lymphocytes containing CXCR2 and concentration of HA in blood serum in stage I AC and SCC are significantly higher than in healthy people. The level of these parameter significantly increases at stage II of the disease compared to stage I and demonstrates further growth at its later stages. Based on the obtained results, regression equations were created: (i) including MFI CXCR1 in granulocytes, proportion of lymphocytes supplied with CXCR2 and HA concentration in the serum to detect stages I-II SCC (diagnostic sensitivity - 95.7%, specificity - 93.7%, threshold value - 0.59) and stages III-IV SCC (diagnostic sensitivity - 93.1%, specificity - 93.3%, threshold value - 0.64); (ii) including the proportion of lymphocytes supplied with CXCR2 MFI CXCR1 in granulocytes and CYFRA 21-1 blood level, which allows the detection of I-II stages of AC (sensitivity - 91.3%, specificity - 94.7%, threshold value - 0.61); (iii) including the proportion of lymphocytes supplied with CXCR2 and CYFRA 21-1 blood level, which allows the detection of AC stages III-IV (sensitivity - 94.6%, specificity - 91.3%, threshold value - 0.15); (iv) including the proportion of lymphocytes supplied with CXCR2 and HA level in the serum to differentiate stage II SCC from stage I (sensitivity - 94.4%, specificity - 87.5%, threshold value - 0.44) and II stage AC from stage I (sensitivity - 88.5%, specificity - 91.2%, threshold value - 0.46).
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Affiliation(s)
- D I Murashka
- Belarusian State Medical University, Minsk, Belarus
| | | | - M M Kauhanka
- Belarusian State Medical University, Minsk, Belarus
| | - V I Prokhorova
- N.N. Aleksandrov RSPC of Oncology and Medical Radiology, Minsk, Belarus
| | - O V Gotko
- N.N. Aleksandrov RSPC of Oncology and Medical Radiology, Minsk, Belarus
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Wadowska K, Błasiak P, Rzechonek A, Bil-Lula I, Śliwińska-Mossoń M. New Insights on Old Biomarkers Involved in Tumor Microenvironment Changes and Their Diagnostic Relevance in Non-Small Cell Lung Carcinoma. Biomolecules 2021; 11:1208. [PMID: 34439874 PMCID: PMC8391392 DOI: 10.3390/biom11081208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/03/2021] [Accepted: 08/10/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Lung cancer is a multifactorial disease with a heterogeneous tumor group that hampers diagnostic and therapeutic approaches, as well as understanding of the processes that underlie its pathogenesis. Current research efforts are focused on examining alterations in the tumor microenvironment, which may affect the pathogenesis and further malignant progression in lung cancer. The aim of this study was to investigate changes in the levels of biomarkers involved in the lung tumor microenvironment and their diagnostic utility in differentiating lung cancer subtypes and stages. METHODS This study comprised 112 lung cancer patients, 50 with adenocarcinoma, 35 with squamous cell carcinoma, 13 with other non-small cell lung carcinoma subtypes, and 14 with other lung neoplasms than non-small cell lung carcinoma. Tumor markers (CEA, CYFRA 21-1, and NSE) were measured in the patients' sera and plasmas, along with IL-6, TNF-α, SAA1, CRP, MMP-2, MMP-9, glucose, lactate, and LDH, utilizing enzyme-linked immunosorbent assays, enzyme immunoassays, and automated clinical chemistry and turbidimetry systems. The results were statistically analyzed across patient groups based on the subtype and stage of lung cancer. RESULTS Glucose concentrations showed statistically significant (p < 0.05) differences both between lung cancer subtypes and stages, with the highest levels in patients with other lung neoplasms (me = 130.5 mg/dL) and in patients with stage IIB lung cancer (me = 132.0 mg/dL). In patients with advanced lung cancer, IL-6 and LDH had considerably higher concentration and activity. There was also a significant positive correlation between IL-6 and MMP-9 in adenocarcinoma and SqCC, with correlation coefficients of 0.53 and 0.49, respectively. The ROC analyses showed that the best single biomarkers for distinguishing adenocarcinoma from squamous cell carcinoma are glucose, CRP, and CYFRA 21-1; however, their combination did not significantly improve sensitivity, specificity, and the AUC value. The combinations of IL-6, glucose, LDH and CEA, IL-6, SAA1, MMP-9, and lactate can distinguish patients with stage IIB lung cancer from those with stage IIA with 100% sensitivity, 100% specificity, and with an AUC value of 0.8333 and 1.0000, respectively, whereas the combination of CEA, IL-6, and LDH can identify patients with stage IIIA lung cancer from those with stage IIB with 72.73% sensitivity, 94.44% specificity, and an AUC value of 0.8686. CONCLUSION There is a link between biomarkers of tumor microenvironment changes and tumor markers, and combinations of these markers may be clinically useful in the differential diagnosis of adenocarcinoma and squamous cell carcinoma, as well as lung cancer stages IIB and IIA, and IIIA and IIB.
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Affiliation(s)
- Katarzyna Wadowska
- Department of Medical Laboratory Diagnostics, Division of Clinical Chemistry and Laboratory Haematology, Wroclaw Medical University, Borowska 211A, 50-556 Wroclaw, Poland; (I.B.-L.); (M.Ś.-M.)
| | - Piotr Błasiak
- Department and Clinic of Thoracic Surgery, Wroclaw Medical University, Grabiszyńska 105, 53-439 Wroclaw, Poland; (P.B.); (A.R.)
- Department of Thoracic Surgery, Lower Silesian Center for Lung Diseases, Grabiszyńska 105, 53-439 Wroclaw, Poland
| | - Adam Rzechonek
- Department and Clinic of Thoracic Surgery, Wroclaw Medical University, Grabiszyńska 105, 53-439 Wroclaw, Poland; (P.B.); (A.R.)
- Department of Thoracic Surgery, Lower Silesian Center for Lung Diseases, Grabiszyńska 105, 53-439 Wroclaw, Poland
| | - Iwona Bil-Lula
- Department of Medical Laboratory Diagnostics, Division of Clinical Chemistry and Laboratory Haematology, Wroclaw Medical University, Borowska 211A, 50-556 Wroclaw, Poland; (I.B.-L.); (M.Ś.-M.)
| | - Mariola Śliwińska-Mossoń
- Department of Medical Laboratory Diagnostics, Division of Clinical Chemistry and Laboratory Haematology, Wroclaw Medical University, Borowska 211A, 50-556 Wroclaw, Poland; (I.B.-L.); (M.Ś.-M.)
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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: 260] [Impact Index Per Article: 86.7] [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.
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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:-
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Metabolic Changes in Early-Stage Non-Small Cell Lung Cancer Patients after Surgical Resection. Cancers (Basel) 2021; 13:cancers13123012. [PMID: 34208545 PMCID: PMC8234274 DOI: 10.3390/cancers13123012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/04/2021] [Accepted: 06/11/2021] [Indexed: 12/19/2022] Open
Abstract
Simple Summary Considerable progress in the treatment of non–small cell lung cancer (NSCLC) has been made possible by large-scale technologies that scan the gene expression in tumor cells. While gene expression is informative, it is the changes to cellular metabolism that directly affect the initiation and the progression of the disease. Altered metabolic processes in cancer include how the tumor cells handle fat, proteins, and sugar, produce energy, divide (grow), or migrate. We have used nuclear magnetic resonance and mass spectrometry to survey and document the metabolic changes in blood and urine samples collected from NSCLC patients before and after their lung tumors were surgically removed. We found several molecular compounds that changed in abundance in the blood or urine after surgery, many of which are related to cancer cell metabolism. Further documentation of these changes in large patient populations will lead to non-invasive ways to screen, diagnose, or monitor disease progression in lung cancer patients. Abstract Metabolic alterations in malignant cells play a vital role in tumor initiation, proliferation, and metastasis. Biofluids from patients with non–small cell lung cancer (NSCLC) harbor metabolic biomarkers with potential clinical applications. In this study, we assessed the changes in the metabolic profile of patients with early-stage NSCLC using mass spectrometry and nuclear magnetic resonance spectroscopy before and after surgical resection. A single cohort of 35 patients provided a total of 29 and 32 pairs of urine and serum samples, respectively, pre-and post-surgery. We identified a profile of 48 metabolites that were significantly different pre- and post-surgery: 17 in urine and 31 in serum. A higher proportion of metabolites were upregulated than downregulated post-surgery (p < 0.01); however, the median fold change (FC) was higher for downregulated than upregulated metabolites (p < 0.05). Purines/pyrimidines and proteins had a larger dysregulation than other classes of metabolites (p < 0.05 for each class). Several of the dysregulated metabolites have been previously associated with cancer, including leucyl proline, asymmetric dimethylarginine, isopentenyladenine, fumaric acid (all downregulated post-surgery), as well as N6-methyladenosine and several deoxycholic acid moieties, which were upregulated post-surgery. This study establishes metabolomic analysis of biofluids as a path to non-invasive diagnostics, screening, and monitoring in NSCLC.
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Qi SA, Wu Q, Chen Z, Zhang W, Zhou Y, Mao K, Li J, Li Y, Chen J, Huang Y, Huang Y. High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis. Sci Rep 2021; 11:11805. [PMID: 34083687 PMCID: PMC8175557 DOI: 10.1038/s41598-021-91276-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/19/2021] [Indexed: 12/19/2022] Open
Abstract
Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.
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Affiliation(s)
- Shi-Ang Qi
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Qian Wu
- Shanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, Shanghai, 201203, China
- Shanghai Fenglin Clinical Laboratory Co., Ltd, Shanghai, 200231, China
| | - Zhenpu Chen
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Wei Zhang
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Yongchun Zhou
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Kaining Mao
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Jia Li
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China
| | - Yuanyuan Li
- Shanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, Shanghai, 201203, China
| | - Jie Chen
- Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
| | - Youguang Huang
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China.
| | - Yunchao Huang
- Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), Kunming, 650118, Yunnan, China.
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Park Y, Ryu B, Ki SJ, McCracken B, Pennington A, Ward KR, Liang X, Kurabayashi K. Few-Layer MoS 2 Photodetector Arrays for Ultrasensitive On-Chip Enzymatic Colorimetric Analysis. ACS NANO 2021; 15:7722-7734. [PMID: 33825460 DOI: 10.1021/acsnano.1c01394] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Enzymatic colorimetric analysis of metabolites provides signatures of energy conversion and biosynthesis associated with disease onsets and progressions. Miniaturized photodetectors based on emerging two-dimensional transition metal dichalcogenides (TMDCs) promise to advance point-of-care diagnosis employing highly sensitive enzymatic colorimetric detection. Reducing diagnosis costs requires a batched multisample assay. The construction of few-layer TMDC photodetector arrays with consistent performance is imperative to realize optical signal detection for a miniature batched multisample enzymatic colorimetric assay. However, few studies have promoted an optical reader with TMDC photodetector arrays for on-chip operation. Here, we constructed 4 × 4 pixel arrays of miniaturized molybdenum disulfide (MoS2) photodetectors and integrated them with microfluidic enzyme reaction chambers to create an optoelectronic biosensor chip device. The fabricated device allowed us to achieve arrayed on-chip enzymatic colorimetric detection of d-lactate, a blood biomarker signifying the bacterial translocation from the intestine, with a limit of detection that is 1000-fold smaller than the clinical baseline, a 10 min assay time, high selectivity, and reasonably small variability across the entire arrays. The enzyme (Ez)/MoS2 optoelectronic biosensor unit consistently detected d-lactate in clinically important biofluids, such as saliva, urine, plasma, and serum of swine and humans with a wide detection range (10-3-103 μg/mL). Furthermore, the biosensor enabled us to show that high serum d-lactate levels are associated with the symptoms of systemic infection and inflammation. The lensless, optical waveguide-free device architecture should readily facilitate development of a monolithically integrated hand-held module for timely, cost-effective diagnosis of metabolic disorders in near-patient settings.
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Affiliation(s)
- Younggeun Park
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Byunghoon Ryu
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Seung Jun Ki
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Brendan McCracken
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Amanda Pennington
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Kevin R Ward
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Xiaogan Liang
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Katsuo Kurabayashi
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
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Goldberg E, Ievari-Shariati S, Kidane B, Kim J, Banerji S, Qing G, Srinathan S, Murphy L, Aliani M. Comparative metabolomics studies of blood collected in streck and heparin tubes from lung cancer patients. PLoS One 2021; 16:e0249648. [PMID: 33891605 PMCID: PMC8064553 DOI: 10.1371/journal.pone.0249648] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 03/23/2021] [Indexed: 11/26/2022] Open
Abstract
Metabolomics analysis of blood from patients (n = 42) undergoing surgery for suspected lung cancer was performed in this study. Venous and arterial blood was collected in both Streck and Heparin tubes. A total of 96 metabolites were detected, affected by sex (n = 56), collection tube (n = 33), and blood location (n = 8). These metabolites belonged to a wide array of compound classes including lipids, acids, pharmaceutical agents, signalling molecules, vitamins, among others. Phospholipids and carboxylic acids accounted for 28% of all detected compounds. Out of the 33 compounds significantly affected by collection tube, 18 compounds were higher in the Streck tubes, including allantoin and ketoleucine, and 15 were higher in the Heparin tubes, including LysoPC(P-16:0), PS 40:6, and chenodeoxycholic acid glycine conjugate. Based on our results, it is recommended that replicate blood samples from each patient should be collected in different types of blood collection tubes for a broader range of the metabolome. Several metabolites were found at higher concentrations in cancer patients such as lactic acid in Squamous Cell Carcinoma, and lysoPCs in Adenocarcinoma and Acinar Cell Carcinoma, which may be used to detect early onset and/or to monitor the progress of the cancer patients.
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Affiliation(s)
- Erin Goldberg
- Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, MB, Canada
- The Canadian Centre for Agri-Food Research in Health and Medicine, (CCARM), St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada
| | - Shiva Ievari-Shariati
- The Canadian Centre for Agri-Food Research in Health and Medicine, (CCARM), St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada
- Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
| | - Biniam Kidane
- Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Julian Kim
- Department of Radiology, CancerCare Manitoba, Winnipeg, MB, Canada
| | - Shantanu Banerji
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Winnipeg, MB, Canada
| | - Gefei Qing
- Department of Pathology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Sadeesh Srinathan
- Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Leigh Murphy
- Research Institute in Oncology and Hematology, CancerCare Manitoba, Department of Biochemistry & Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Michel Aliani
- Department of Food and Human Nutritional Sciences, University of Manitoba, Winnipeg, MB, Canada
- The Canadian Centre for Agri-Food Research in Health and Medicine, (CCARM), St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada
- * E-mail:
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Miller HA, Yin X, Smith SA, Hu X, Zhang X, Yan J, Miller DM, van Berkel VH, Frieboes HB. Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data. Lung Cancer 2021; 156:20-30. [PMID: 33882406 DOI: 10.1016/j.lungcan.2021.04.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/26/2021] [Accepted: 04/12/2021] [Indexed: 01/17/2023]
Abstract
OBJECTIVES Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. MATERIALS AND METHODS Samples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and 13C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) in terms of first-line chemotherapy. Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests (RF)) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets. RESULTS The models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961-0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865-0.895); stage I/II/III vs. IV (SVM), 0.902(0.880-0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74). CONCLUSION This study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, United States
| | - Xinmin Yin
- Department of Chemistry, University of Louisville, United States
| | - Susan A Smith
- Department of Surgery, University of Louisville, United States
| | - Xiaoling Hu
- James Graham Brown Cancer Center, University of Louisville, United States; Division of Immunotherapy, Department of Surgery, University of Louisville, United States
| | - Xiang Zhang
- Department of Chemistry, University of Louisville, United States
| | - Jun Yan
- Department of Pharmacology and Toxicology, University of Louisville, United States; James Graham Brown Cancer Center, University of Louisville, United States; Division of Immunotherapy, Department of Surgery, University of Louisville, United States; Department of Microbiology and Immunology, University of Louisville, United States
| | - Donald M Miller
- Department of Pharmacology and Toxicology, University of Louisville, United States; James Graham Brown Cancer Center, University of Louisville, United States; Department of Medicine, University of Louisville, United States
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, United States; Department of Cardiovascular and Thoracic Surgery, University of Louisville, United States
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, United States; James Graham Brown Cancer Center, University of Louisville, United States; Department of Bioengineering, University of Louisville, United States; Center for Predictive Medicine, University of Louisville, United States.
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Miller HA, Emam R, Lynch CM, Bockhorst S, Frieboes HB. Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods. Metabolomics 2021; 17:37. [PMID: 33772663 PMCID: PMC8138701 DOI: 10.1007/s11306-021-01787-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 03/16/2021] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive. OBJECTIVE In this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers. METHODS Uniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patient-derived lung cancer core biopsies with true missing values. Eight pre-treatment methods, ten imputation methods, and two data analysis methods were applied in combination. RESULTS The combined choice of pre-treatment and imputation methods is critical in the definition of candidate biomarkers, with deficient or inappropriate selection of these methods leading to inconsistent results, and with important biomarkers either being overlooked or reported as a false positive. The log transformation appeared to normalize the original tumor data most effectively, but the performance of the imputation applied after the transformation was highly dependent on the characteristics of the data set. CONCLUSION The combined choice of pre-treatment and imputation methods may need careful evaluation prior to metabolomic data analysis of human tumors, in order to enable consistent identification of potential biomarkers predictive of response to therapy and of disease stage.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
| | - Ramy Emam
- Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Chip M Lynch
- Department of Computer Engineering and Computer Science, University of Louisville, Louisville, USA
| | - Samuel Bockhorst
- Department of Medicine, University of Louisville, Louisville, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
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Phyo JB, Woo A, Yu HJ, Lim K, Cho BH, Jung HS, Lee MY. Label-Free SERS Analysis of Urine Using a 3D-Stacked AgNW-Glass Fiber Filter Sensor for the Diagnosis of Pancreatic Cancer and Prostate Cancer. Anal Chem 2021; 93:3778-3785. [PMID: 33576598 DOI: 10.1021/acs.analchem.0c04200] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Metabolomics shows tremendous potential for the early diagnosis and screening of cancer. For clinical application as an effective diagnostic tool, however, improved analytical methods for complex biological fluids are required. Here, we developed a reliable rapid urine analysis system based on surface-enhanced Raman spectroscopy (SERS) using 3D-stacked silver nanowires (AgNWs) on a glass fiber filter (GFF) sensor and applied it to the diagnosis of pancreatic cancer and prostate cancer. Urine samples were pretreated with centrifugation to remove large debris and with calcium ion addition to improve the binding of metabolites to AgNWs. The label-free urine-SERS detection using the AgNW-GFF SERS sensor showed different spectral patterns and distinguishable specific peaks in three groups: normal control (n = 30), pancreatic cancer (n = 22), and prostate cancer (n = 22). Multivariate analyses of SERS spectra using unsupervised principal component analysis and supervised orthogonal partial least-squares discriminant analysis showed excellent discrimination between the pancreatic cancer group and the prostate cancer group as well as between the normal control group and the combined cancer groups. The results demonstrate the great potential of the urine-SERS analysis system using the AgNW-GFF SERS sensor for the noninvasive diagnosis and screening of cancers.
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Affiliation(s)
- Jung Bin Phyo
- Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.,Smart Healthcare Research Institute, Samsung Medical Center, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea
| | - Ayoung Woo
- Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea
| | - Ho Jae Yu
- Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea
| | - Kyongmook Lim
- Smart Healthcare Research Institute, Samsung Medical Center, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea
| | - Baek Hwan Cho
- Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.,Smart Healthcare Research Institute, Samsung Medical Center, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea
| | - Ho Sang Jung
- Department of Nano-Bio Convergence, Korea Institute of Materials Science (KIMS), Changwon, Gyeongnam 51508, Republic of Korea
| | - Min-Young Lee
- Department of Medical Device Management and Research, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.,Smart Healthcare Research Institute, Samsung Medical Center, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea
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Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol 2020; 14:100907. [PMID: 33217646 PMCID: PMC7683339 DOI: 10.1016/j.tranon.2020.100907] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/21/2020] [Accepted: 09/25/2020] [Indexed: 02/07/2023] Open
Abstract
Early diagnosis could improve lung cancer survival rate. The availability of blood-based screening could increase lung cancer patient uptake. An interdisciplinary mechanism combines metabolomics and machine learning methods. Metabolic biomarkers could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction.
Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients’ plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.
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Lin X, Xiao Z, Hu Y, Zhang X, Fan W. Combining 18F-FDG PET/CT and Serum Lactate Dehydrogenase for Prognostic Evaluation of Small Cell Lung Cancer. Front Pharmacol 2020; 11:592768. [PMID: 33192532 PMCID: PMC7656055 DOI: 10.3389/fphar.2020.592768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 09/28/2020] [Indexed: 11/13/2022] Open
Abstract
Objective: To investigate the value of using 18F-FDG PET/CT in combination with serum lactate dehydrogenase (LDH) for prognostic evaluation of newly diagnosed small cell lung cancer (SCLC). Methods: We reviewed 118 patients with pathologically proven SCLC who underwent 18F-FDG PET/CT imaging evaluation in our hospital. Among these patients, 64 patients had extensive disease (ED) and 54 patients had limited disease (LD). The maximum standardized uptake value (SUVmax) of primary tumor was measured. A Cox proportional hazards model was used to evaluate age, sex, performance status, serum LDH, tumor stage and SUVmax on the prediction of overall survival (OS) and median survival time (MST) of patients. Subgroup analysis was performed based on the SUVmax in combination with serum LDH. Results: According to the Receiver Operating Characteristic (ROC) curve, the optimal cut-off value of SUVmax was 10.95. The AUC was 0.535 (95% CI: 0.407-0.663). The patients were divided into four groups according to the SUVmax (higher or lower than 10.95) and LDH (higher or lower than 245 U/L). The univariate and multivariate analyses showed that curative thoracic radiotherapy, Prophylactic Cranial Irradiation (PCI) and the combination of primary tumor SUVmax ≤ 10.95 and LDH ≤ 245 U/L were prognostic factors of OS in patients with all patients (p < 0.05). Smoking status, PCI, the combination of primary tumor SUVmax ≤ 10.95 and LDH ≤ 245 U/L were prognostic factors of OS in patients with LD (p < 0.05). N stage and PCI were significant predictors in both of univariate and multivariate analysis of OS for ED SCLC (p < 0.05). Among all patients, 27 had low SUVmax and normal LDH, and their MST was 36 months (95% CI: 12.98-59.02). Ninety-one patients had high SUVmax and/or high LDH, and their MST was 20 months (95% CI: 15.47-24.53). The difference between these two groups was significant (p = 0.045). In patients with LD, 16 patients had low SUVmax and normal LDH, and their MST was 72 months (95% CI: 26.00-118.0). Thirty-eight patients had high SUVmax and/or high LDH, and their MST was 27 months (95% CI: 20.80-33.21). The difference between these two groups was significant (p = 0.012). In patients with ED SCLC, 10 patients had low SUVmax and normal LDH, with an MST of 18 months (95% CI: 13.69-22.32. Fifty-four patients had high SUVmax and/or high LDH, and their MST was 12 months (95% CI: 10.61-13.39). The difference of MST between these two groups was not statistically significant (p = 0.686). Conclusion: 18F-FDG PET/CT in combination with serum LDH were prognostic factors of overall survival in patients with SCLC. The prognosis of patients with LD SCLC who had low SUVmax of primary tumor and normal LDH was better than those with high SUVmax and/or high LDH.
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Affiliation(s)
- Xiaoping Lin
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zizheng Xiao
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yingying Hu
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xu Zhang
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Wei Fan
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
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Choe W, Chae JD, Lee BH, Kim SH, Park SY, Nimse SB, Kim J, Warkad SD, Song KS, Oh AC, Hong YJ, Kim T. 9G Test TM Cancer/Lung: A Desirable Companion to LDCT for Lung Cancer Screening. Cancers (Basel) 2020; 12:cancers12113192. [PMID: 33143045 PMCID: PMC7692999 DOI: 10.3390/cancers12113192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 10/26/2020] [Accepted: 10/29/2020] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Lung cancer is the most common cause of cancer-related deaths globally. Patients diagnosed at early-stage (0–I) have a higher survival rate than the metastasized stages (III–IV). Thus, there is great potential to reduce mortality by diagnosing lung cancer at stage 0~I through community screening. LDCT is a promising method, but it has a high false-positive rate. Therefore, a biomarker test that can be used in combination with LDCT for lung cancer screening to reduce false-positive rates is highly awaited. The present study evaluated the applicability of 9G testTM Cancer/Lung test to detect stage 0~IV lung cancer. 9G testTM Cancer/Lung test detects stage I, stage II, stage III, and stage IV cancers with the sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3%. These results indicate that the 9G testTM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Abstract A complimentary biomarker test that can be used in combination with LDCT for lung cancer screening is highly desirable to improve the diagnostic capacity of LDCT and reduce the false-positive rates. Most importantly, the stage I lung cancer detection rate can be dramatically increased by the simultaneous use of a biomarker test with LDCT. The present study was conducted to evaluate 9G testTM Cancer/Lung’s sensitivity and specificity in detecting Stage 0~IV lung cancer. The obtained results indicate that the 9G testTM Cancer/Lung can detect lung cancer with overall sensitivity and specificity of 75.0% (69.1~80.3) and 97.3% (95.0~98.8), respectively. The detection of stage I, stage II, stage III, and stage IV cancers with sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3% have never been reported before. The receiver operating characteristic curve analysis allowed us to determine the population-weighted AUC of 0.93 (95% CI, 0.91–0.95). These results indicate that the 9G testTM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Furthermore, obtained results indicate that the use of 9G testTM Cancer/Lung with LDCT for lung cancer screening can increase stage I cancer detection, which is crucial to improve the currently low 5-year survival rates.
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Affiliation(s)
- Wonho Choe
- Nowon Eulji Medical Center, Department of Laboratory Medicine, Eulji University, Seoul 01830, Korea
| | - Jeong Don Chae
- Nowon Eulji Medical Center, Department of Laboratory Medicine, Eulji University, Seoul 01830, Korea
| | - Byoung-Hoon Lee
- Nowon Eulji Medical Center, Department of Pulmonology and Allergy, Eulji University, Seoul 01830, Korea
| | - Sang-Hoon Kim
- Nowon Eulji Medical Center, Department of Pulmonology and Allergy, Eulji University, Seoul 01830, Korea
| | - So Young Park
- Nowon Eulji Medical Center, Department of Pulmonology and Allergy, Eulji University, Seoul 01830, Korea
| | - Satish Balasaheb Nimse
- Institute of Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 24252, Korea
| | - Junghoon Kim
- Institute of Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 24252, Korea
| | | | - Keum-Soo Song
- Biometrix Technology, Inc. 2-2 Bio Venture Plaza 56, Chuncheon 24232, Korea
| | - Ae-Chin Oh
- Departments of Laboratory Medicine, Korea Cancer Center Hospital, Seoul 01812, Korea
| | - Young Jun Hong
- Departments of Laboratory Medicine, Korea Cancer Center Hospital, Seoul 01812, Korea
| | - Taisun Kim
- Institute of Applied Chemistry and Department of Chemistry, Hallym University, Chuncheon 24252, Korea
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Association between Metabolites and the Risk of Lung Cancer: A Systematic Literature Review and Meta-Analysis of Observational Studies. Metabolites 2020; 10:metabo10090362. [PMID: 32899527 PMCID: PMC7570231 DOI: 10.3390/metabo10090362] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/17/2020] [Accepted: 08/24/2020] [Indexed: 12/12/2022] Open
Abstract
Globally, lung cancer is the most prevalent cancer type. However, screening and early detection is challenging. Previous studies have identified metabolites as promising lung cancer biomarkers. This systematic literature review and meta-analysis aimed to identify metabolites associated with lung cancer risk in observational studies. The literature search was performed in PubMed and EMBASE databases, up to 31 December 2019, for observational studies on the association between metabolites and lung cancer risk. Heterogeneity was assessed using the I2 statistic and Cochran’s Q test. Meta-analyses were performed using either a fixed-effects or random-effects model, depending on study heterogeneity. Fifty-three studies with 297 metabolites were included. Most identified metabolites (252 metabolites) were reported in individual studies. Meta-analyses were conducted on 45 metabolites. Five metabolites (cotinine, creatinine riboside, N-acetylneuraminic acid, proline and r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene) and five metabolite groups (total 3-hydroxycotinine, total cotinine, total nicotine, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (sum of concentrations of the metabolite and its glucuronides), and total nicotine equivalent (sum of total 3-hydroxycotinine, total cotinine and total nicotine)) were associated with higher lung cancer risk, while three others (folate, methionine and tryptophan) were associated with lower lung cancer risk. Significant heterogeneity was detected across most studies. These significant metabolites should be further evaluated as potential biomarkers for lung cancer.
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Bojko B, Looby N, Olkowicz M, Roszkowska A, Kupcewicz B, Reck Dos Santos P, Ramadan K, Keshavjee S, Waddell TK, Gómez-Ríos G, Tascon M, Goryński K, Cypel M, Pawliszyn J. Solid phase microextraction chemical biopsy tool for monitoring of doxorubicin residue during in vivo lung chemo-perfusion. J Pharm Anal 2020; 11:37-47. [PMID: 33717610 PMCID: PMC7930785 DOI: 10.1016/j.jpha.2020.08.011] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/24/2020] [Accepted: 08/24/2020] [Indexed: 12/24/2022] Open
Abstract
Development of a novel in vivo lung perfusion (IVLP) procedure allows localized delivery of high-dose doxorubicin (DOX) for targeting residual micrometastatic disease in the lungs. However, DOX delivery via IVLP requires careful monitoring of drug level to ensure tissue concentrations of this agent remain in the therapeutic window. A small dimension nitinol wire coated with a sorbent of biocompatible morphology (Bio-SPME) has been clinically evaluated for in vivo lung tissue extraction and determination of DOX and its key metabolites. The in vivo Bio-SPME-IVLP experiments were performed on pig model over various (150 and 225 mg/m2) drug doses, and during human clinical trial. Two patients with metastatic osteosarcoma were treated with a single 5 and 7 μg/mL (respectively) dose of DOX during a 3-h IVLP. In both pig and human cases, DOX tissue levels presented similar trends during IVLP. Human lung tissue concentrations of drug ranged between 15 and 293 μg/g over the course of the IVLP procedure. In addition to DOX levels, Bio-SPME followed by liquid chromatography-mass spectrometry analysis generated 64 metabolic features during endogenous metabolite screening, providing information about lung status during drug administration. Real-time monitoring of DOX levels in the lungs can be performed effectively throughout the IVLP procedure by in vivo Bio-SPME chemical biopsy approach. Bio-SPME also extracted various endogenous molecules, thus providing a real-time snapshot of the physiology of the cells, which might assist in the tailoring of personalized treatment strategy.
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Affiliation(s)
- Barbara Bojko
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada.,Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-089, Bydgoszcz, Poland
| | - Nikita Looby
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada
| | - Mariola Olkowicz
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada.,Jagiellonian Centre for Experimental Therapeutics (JCET), Jagiellonian University, 30-348 Krakow, Poland
| | - Anna Roszkowska
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada.,Department of Pharmaceutical Chemistry, Medical University of Gdansk, 80-416, Gdansk, Poland
| | - Bogumiła Kupcewicz
- Department of Inorganic and Analytical Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-089, Bydgoszcz, Poland
| | | | - Khaled Ramadan
- University Health Network - TGH, Toronto, ON M5G 2C4, Canada
| | - Shaf Keshavjee
- University Health Network - TGH, Toronto, ON M5G 2C4, Canada
| | | | - German Gómez-Ríos
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada
| | - Marcos Tascon
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada
| | - Krzysztof Goryński
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada.,Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-089, Bydgoszcz, Poland
| | - Marcelo Cypel
- University Health Network - TGH, Toronto, ON M5G 2C4, Canada
| | - Janusz Pawliszyn
- Department of Chemistry, University of Waterloo, Waterloo, ON M1B 6G3, Canada
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Yang D, Yang X, Li Y, Zhao P, Fu R, Ren T, Hu P, Wu Y, Yang H, Guo N. Clinical significance of circulating tumor cells and metabolic signatures in lung cancer after surgical removal. J Transl Med 2020; 18:243. [PMID: 32552826 PMCID: PMC7301449 DOI: 10.1186/s12967-020-02401-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 06/03/2020] [Indexed: 01/01/2023] Open
Abstract
Background Lung cancer (LC) remains the deadliest form of cancer globally. While surgery remains the optimal treatment strategy for individuals with early-stage LC, what the metabolic consequences are of such surgical intervention remains uncertain. Methods Negative enrichment-fluorescence in situ hybridization (NE-FISH) was used in an effort to detect circulating tumor cells (CTCs) in pre- and post-surgery peripheral blood samples from 51 LC patients. In addition, targeted metabolomics analyses, multivariate statistical analyses, and pathway analyses were used to explore surgery-associated metabolic changes. Results LC patients had significantly higher CTC counts relative to healthy controls with 66.67% of LC patients having at least 1 detected CTC before surgery. CTC counts were associated with clinical outcomes following surgery. In a targeted metabolomics analysis, we detected 34 amino acids, 147 lipids, and 24 fatty acids. When comparing LC patients before and after surgery to control patients, metabolic shifts were detected via PLS-DA and pathway analysis. Further surgery-associated metabolic changes were identified when comparing LA (LC patients after surgery) and LB (LC patients before surgery) groups. We identified SM 42:4, Ser, Sar, Gln, and LPC 18:0 for inclusion in a biomarker panel for early-stage LC detection based upon an AUC of 0.965 (95% CI 0.900–1.000). This analysis revealed that SM 42:2, SM 35:1, PC (16:0/14:0), PC (14:0/16:1), Cer (d18:1/24:1), and SM 38:3 may offer diagnostic and prognostic benefits in LC. Conclusions These findings suggest that CTC detection and plasma metabolite profiling may be an effective means of diagnosing early-stage LC and identifying patients at risk for disease recurrence.
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Affiliation(s)
- Dawei Yang
- Zhong Yuan Academy of Biological Medicine, Liaocheng People's Hospital, Liaocheng, 252000, People's Republic of China
| | - Xiaofang Yang
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China
| | - Yang Li
- Zhong Yuan Academy of Biological Medicine, Liaocheng People's Hospital, Liaocheng, 252000, People's Republic of China
| | - Peige Zhao
- Department of Respiratory Medicine, Liaocheng People's Hospital, Liaocheng, 252000, People's Republic of China
| | - Rao Fu
- Zhong Yuan Academy of Biological Medicine, Liaocheng People's Hospital, Liaocheng, 252000, People's Republic of China
| | - Tianying Ren
- Zhong Yuan Academy of Biological Medicine, Liaocheng People's Hospital, Liaocheng, 252000, People's Republic of China
| | - Ping Hu
- Zhong Yuan Academy of Biological Medicine, Liaocheng People's Hospital, Liaocheng, 252000, People's Republic of China
| | - Yaping Wu
- Zhong Yuan Academy of Biological Medicine, Liaocheng People's Hospital, Liaocheng, 252000, People's Republic of China
| | - Hongjun Yang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China.
| | - Na Guo
- Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing, 100700, People's Republic of China. .,State Key Laboratory of Generic Manufacture Technology of Traditional Chinese Medicine, Lunan Pharmaceutical Group Co. Ltd., Shandong, 276006, People's Republic of China.
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