1
|
Murcia-Mejía M, Canela-Capdevila M, García-Pablo R, Jiménez-Franco A, Jiménez-Aguilar JM, Badía J, Benavides-Villarreal R, Acosta JC, Arguís M, Onoiu AI, Castañé H, Camps J, Arenas M, Joven J. Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy. Biomolecules 2024; 14:898. [PMID: 39199286 DOI: 10.3390/biom14080898] [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: 06/14/2024] [Revised: 07/22/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC patients versus healthy controls and examined the effects of conventionally fractionated radiation therapy (CFRT) and stereotactic body radiation therapy (SBRT). We enrolled 91 NSCLC patients (38 CFRT and 53 SBRT) and 40 healthy controls. Plasma metabolite levels were assessed using semi-targeted metabolomics, revealing 32 elevated and 18 reduced metabolites in patients. Key discriminatory metabolites included ethylmalonic acid, maltose, 3-phosphoglyceric acid, taurine, glutamic acid, glycocolic acid, and d-arabinose, with a combined Receiver Operating Characteristics curve indicating perfect discrimination between patients and controls. CFRT and SBRT affected different metabolites, but both changes suggested a partial normalization of energy and amino acid metabolism pathways. In conclusion, metabolomics identified distinct metabolic signatures in NSCLC patients with potential as diagnostic biomarkers. The differing metabolic responses to CFRT and SBRT reflect their unique therapeutic impacts, underscoring the utility of this technique in enhancing NSCLC diagnosis and treatment monitoring.
Collapse
Affiliation(s)
- Mauricio Murcia-Mejía
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Marta Canela-Capdevila
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Raquel García-Pablo
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Andrea Jiménez-Franco
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Juan Manuel Jiménez-Aguilar
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Joan Badía
- Statistical Support Platform, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Rocío Benavides-Villarreal
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Johana C Acosta
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Mónica Arguís
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Alina-Iuliana Onoiu
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Helena Castañé
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Jordi Camps
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Meritxell Arenas
- Department of Radiation Oncology, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| | - Jorge Joven
- Unitat de Recerca Biomèdica, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, 43204 Reus, Spain
| |
Collapse
|
2
|
Klupczynska-Gabryszak A, Daskalaki E, Wheelock CE, Kasprzyk M, Dyszkiewicz W, Grabicki M, Brajer-Luftmann B, Pawlak M, Kokot ZJ, Matysiak J. Metabolomics-based search for lung cancer markers among patients with different smoking status. Sci Rep 2024; 14:15444. [PMID: 38965272 PMCID: PMC11224321 DOI: 10.1038/s41598-024-65835-2] [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: 04/07/2024] [Accepted: 06/24/2024] [Indexed: 07/06/2024] Open
Abstract
Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.
Collapse
Affiliation(s)
| | - Evangelia Daskalaki
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
| | - Mariusz Kasprzyk
- Department of Thoracic Surgery, Poznan University of Medical Sciences, Poznan, Poland
| | - Wojciech Dyszkiewicz
- Department of Thoracic Surgery, Poznan University of Medical Sciences, Poznan, Poland
| | - Marcin Grabicki
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, Poznan, Poland
| | - Beata Brajer-Luftmann
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, Poznan, Poland
| | - Magdalena Pawlak
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Poznan, Poland
| | - Zenon J Kokot
- Faculty of Health Sciences, Calisia University, Kalisz, Poland
| | - Jan Matysiak
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Poznan, Poland
| |
Collapse
|
3
|
Gadiyaram S, Aakshika Sree M, Sharma N, Amilan Jose D. An amphiphilic dansyl based multianalyte sensor for the detection of Hg 2+, PPi, and TNP: A three-in-one chemical sensor. Methods 2024; 223:45-55. [PMID: 38272245 DOI: 10.1016/j.ymeth.2024.01.007] [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: 11/29/2023] [Revised: 01/06/2024] [Accepted: 01/13/2024] [Indexed: 01/27/2024] Open
Abstract
A fluorescent dansyl-based amphiphilic probe, 5-(dimethylamino)-N-hexadecylnaphthalene-1-sulfonamide (DLC), was synthesized and characterized to detect multiple analytes at different sensing environments. In acetonitrile, DLC detects nitro explosives such as trinitrophenol (TNP) and 2,4-dinitrophenol (2,4-DNP) by an emission "on-off" response method, and the detection limits (LOD) were estimated to be as low as 4.3 µM and 17.4 µM, respectively. Amphiphilic long chains of the probe were embedded into lipid bilayers to form nanoscale vesicles DLC.Ves. Nanovesicular probe DLC.Ves was found to be highly selective for Hg2+ among other metal ions and for pyrophosphate (PPi) ions among various anions. DLC.Ves could detect Hg2+ with a turn "on-off" emission and PPi with ratiometric change in emission at 525 nm. It is proposed that DLC.Ves could detect Hg2+ via the Hg2+-induced aggregation quenching mechanism and PPi through the Hydrogen bonding. The LODs are estimated as 6.41 µM and 70.9 µM for Hg2+ and PPi, respectively. 1H NMR, SEM, and fluorescence lifetime measurements confirmed the binding mechanism. Thus, it is believed that the simple fluorescent probe DLC could be a prominent sensor to detect multiple analytes depending on the sensing medium.
Collapse
Affiliation(s)
- Srushti Gadiyaram
- Department of Chemistry, National Institute of Technology (NIT) Kurukshetra, Kurukshetra 136119, Haryana, India
| | - M Aakshika Sree
- Department of Chemistry, National Institute of Technology (NIT) Kurukshetra, Kurukshetra 136119, Haryana, India
| | - Nancy Sharma
- Department of Chemistry, National Institute of Technology (NIT) Kurukshetra, Kurukshetra 136119, Haryana, India
| | - D Amilan Jose
- Department of Chemistry, National Institute of Technology (NIT) Kurukshetra, Kurukshetra 136119, Haryana, India.
| |
Collapse
|
4
|
Shi W, Cheng Y, Zhu H, Zhao L. Metabolomics and lipidomics in non-small cell lung cancer. Clin Chim Acta 2024; 555:117823. [PMID: 38325713 DOI: 10.1016/j.cca.2024.117823] [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: 12/18/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/09/2024]
Abstract
Due to its insidious nature, lung cancer remains a leading cause of cancer-related deaths worldwide. Therefore, there is an urgent need to identify sensitive/specific biomarkers for early diagnosis and monitoring. The current study was designed to provide a current metabolic profile of non-small cell lung cancer (NSCLC) by systematically reviewing and summarizing various metabolomic/ lipidomic studies based on NSCLC blood samples, attempting to find biomarkers in human blood that can predict or diagnose NSCLC, and investigating the involvement of key metabolites in the pathogenesis of NSCLC. We searched all articles on lung cancer published in Elsevier, PubMed, Web of Science and the Cochrane Library between January 2012 and December 2022. After critical selection, a total of 31 studies (including 2768 NSCLC patients and 9873 healthy individuals) met the inclusion criteria, and 22 were classified as "high quality". Forty-six metabolites related to NSCLC were repeatedly identified, involving glucose metabolism, amino acid metabolism, lipid metabolism and nucleotide metabolism. Pyruvic acid, carnitine, phenylalanine, isoleucine, kynurenine and 3-hydroxybutyrate showed upward trends in all studies, citric acid, glycine, threonine, cystine, alanine, histidine, inosine, betaine and arachidic acid showed downward trends in all studies. This review summarizes the existing metabolomic/lipidomic studies related to the identification of blood biomarkers in NSCLC, examines the role of key metabolites in the pathogenesis of NSCLC, and provides an important reference for the clinical diagnosis and treatment of NSCLC. Due to the limited size and design heterogeneity of the existing studies, there is an urgent need for standardization of future studies, while validating existing findings with more studies.
Collapse
Affiliation(s)
- Wei Shi
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China
| | - Yizhen Cheng
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China
| | - Haihua Zhu
- Betta Pharmaceuticals Co., Ltd, 24 Wuzhou Road Yuhang Economic and Technological Development Area, Hangzhou, Zhejiang Province, PR China
| | - Longshan Zhao
- Shenyang Pharmaceutical University, 103 Wenhua Road Shenhe District, 110016 Shenyang, Liaoning Province, PR China.
| |
Collapse
|
5
|
Mlika M, Zorgati MM, Abdennadher M, Bouassida I, Mezni F, Mrabet A. The diagnostic performance of micro-RNA and metabolites in lung cancer: A meta-analysis. Asian Cardiovasc Thorac Ann 2024; 32:45-65. [PMID: 38009802 DOI: 10.1177/02184923231215538] [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] [Indexed: 11/29/2023]
Abstract
BACKGROUND The diagnosis of lung cancer is based on the microscopic exam of tissue or liquid. During the recent decade, many biomarkers have been pointed to have a potential diagnostic role. These biomarkers may be assessed in blood, pleural effusion or sputum and they could avoid biopsies or other risky procedures. The authors aimed to assess the diagnostic performances of biomarkers focusing on micro-RNA and metabolites. METHODS This meta-analysis was conducted under the PRISMA guidelines during a nine-year-period (2013-2022). the Meta-Disc software 5.4 (free version) was used. Q test and I2 statistics were carried out to explore the heterogeneity among studies. Meta-regression was performed in case of significant heterogeneity. Publication bias was assessed using the funnel plot test and the Egger's test (free version JASP). RESULTS According to our inclusion criteria, 165 studies from 79 articles were included. The pooled SEN, SPE and dOR accounted, respectively, for 0.76, 0.79 and 13.927. The AUC was estimated to 0.859 suggesting a good diagnostic accuracy. The heterogeneity in the pooled SEN and SPE was statistically significant. The meta-regression analysis focusing on the technique used, the sample, the number of biomarkers, the biomarker subtype, the tumor stage and the ethnicity revealed the biomarker number (p = 0.009) and the tumor stage (p = 0.0241) as potential sources of heterogeneity. CONCLUSION Even if this meta-analysis highlighted the potential diagnostic utility of biomarkers, more prospective studies should be performed, especially to assess the biomarkers' diagnostic potential in early-stage lung cancers.
Collapse
Affiliation(s)
- Mona Mlika
- Department of Pathology, Center of Traumatology and Major Burns, Ben Arous, Tunis, Tunisia
- University Tunis El Manar, Faculty of Medicine of Tunis, Tunisia
| | | | - Mehdi Abdennadher
- University Tunis El Manar, Faculty of Medicine of Tunis, Tunisia
- Department of Thoracic Surgery, Abderrahman Mami Hospital, Tunis, Tunisia
| | - Imen Bouassida
- University Tunis El Manar, Faculty of Medicine of Tunis, Tunisia
- Department of Thoracic Surgery, Abderrahman Mami Hospital, Tunis, Tunisia
| | - Faouzi Mezni
- University Tunis El Manar, Faculty of Medicine of Tunis, Tunisia
| | - Ali Mrabet
- University Tunis El Manar, Faculty of Medicine of Tunis, Tunisia
- Ministry of Health, Tunis, Tunisia
| |
Collapse
|
6
|
Choudhary A, Yu J, Kouznetsova VL, Kesari S, Tsigelny IF. Two-Stage Deep-Learning Classifier for Diagnostics of Lung Cancer Using Metabolites. Metabolites 2023; 13:1055. [PMID: 37887380 PMCID: PMC10609149 DOI: 10.3390/metabo13101055] [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: 08/24/2023] [Revised: 09/18/2023] [Accepted: 09/26/2023] [Indexed: 10/28/2023] Open
Abstract
We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a "divide and conquer strategy" gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step.
Collapse
Affiliation(s)
- Ashvin Choudhary
- School of Life Science, University of California, Los Angeles, CA 90095, USA;
| | - Jianpeng Yu
- School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Valentina L. Kouznetsova
- San Diego Supercomputer Center, University of California, San Diego, CA 92093, USA;
- IUL, La Jolla, CA 92038, USA
- CureScience Institute, San Diego, CA 92121, USA
| | - Santosh Kesari
- Pacific Neuroscience Institute, Santa Monica, CA 90404, USA;
| | - Igor F. Tsigelny
- San Diego Supercomputer Center, University of California, San Diego, CA 92093, USA;
- IUL, La Jolla, CA 92038, USA
- CureScience Institute, San Diego, CA 92121, USA
- Department of Neurosciences, University of California, San Diego, CA 92093, USA
| |
Collapse
|
7
|
Almalki AH. Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer. Metabolites 2023; 13:1037. [PMID: 37887362 PMCID: PMC10609104 DOI: 10.3390/metabo13101037] [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: 08/24/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.
Collapse
Affiliation(s)
- Atiah H. Almalki
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
- Addiction and Neuroscience Research Unit, Health Science Campus, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| |
Collapse
|
8
|
Zhang Y, Lin X, Gao Z, Wang T, Dong K, Zhang J. An omics data analysis method based on feature linear relationship and graph convolutional network. J Biomed Inform 2023; 145:104479. [PMID: 37634557 DOI: 10.1016/j.jbi.2023.104479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/26/2023] [Accepted: 08/23/2023] [Indexed: 08/29/2023]
Abstract
Biological networks are known to be highly modular, and the dysfunction of network modules may cause diseases. Defining the key modules from the omics data and establishing the classification model is helpful in promoting the research of disease diagnosis and prognosis. However, for applying modules in downstream analysis such as disease states discrimination, most methods only utilize the node information, and ignore the node interactions or topological information, which may lead to false positives and limit the model performance. In this study, we propose an omics data analysis method based on feature linear relationship and graph convolutional network (LCNet). In LCNet, we adopt a way of applying the difference of feature linear relationships during disease development to characterize physiological and pathological changes and construct the differential linear relation network, which is simple and interpretable from the perspective of feature linear relationship. A greedy strategy is developed for searching the highly interactive modules with a strong discrimination ability. To fully utilize the information of the detected modules, the personalized sub-graphs for each sample based on the modules are defined, and the graph convolutional network (GCN) classifiers are trained to predict the sample labels. The experimental results on public datasets show the superiority of LCNet in classification performance. For Breast Cancer metabolic data, the identified metabolites by LCNet involve important pathways. Thus, LCNet can identify the module biomarkers by feature linear relationship and a greedy strategy, and label samples by personalized sub-graphs and GCN. It provides a new manner of utilizing node (molecule) information and topological information in the defined modules for better disease classification.
Collapse
Affiliation(s)
- Yanhui Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
| | - Zhenbo Gao
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Tianxiang Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Kunjie Dong
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Jianjun Zhang
- Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Liaoning, China
| |
Collapse
|
9
|
The Importance of the Immune System and Molecular Cell Signaling Pathways in the Pathogenesis and Progression of Lung Cancer. Int J Mol Sci 2023; 24:ijms24021506. [PMID: 36675020 PMCID: PMC9861992 DOI: 10.3390/ijms24021506] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/04/2023] [Accepted: 01/08/2023] [Indexed: 01/13/2023] Open
Abstract
Lung cancer is a disease that in recent years has become one of the greatest threats to modern society. Every year there are more and more new cases and the percentage of deaths caused by this type of cancer increases. Despite many studies, scientists are still looking for answers regarding the mechanisms of lung cancer development and progression, with particular emphasis on the role of the immune system. The aim of this literature review was to present the importance of disorders of the immune system and the accompanying changes at the level of cell signaling in the pathogenesis of lung cancer. The collected results showed that in the process of immunopathogenesis of almost all subtypes of lung cancer, changes in the tumor microenvironment, deregulation of immune checkpoints and abnormalities in cell signaling pathways are involved, which contribute to the multistage and multifaceted carcinogenesis of this type of cancer. We, therefore, suggest that in future studies, researchers should focus on a detailed analysis of tumor microenvironmental immune checkpoints, and to validate their validity, perform genetic polymorphism analyses in a wide range of patients and healthy individuals to determine the genetic susceptibility to lung cancer development. In addition, further research related to the analysis of the tumor microenvironment; immune system disorders, with a particular emphasis on immunological checkpoints and genetic differences may contribute to the development of new personalized therapies that improve the prognosis of patients.
Collapse
|
10
|
Bifarin OO. Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification. PLoS One 2023; 18:e0284315. [PMID: 37141218 PMCID: PMC10159207 DOI: 10.1371/journal.pone.0284315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/28/2023] [Indexed: 05/05/2023] Open
Abstract
Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants are widely used, partly due to the model's interpretability with the Variable Influence in Projection (VIP) scores, a global interpretable method. Herein, Tree-based Shapley Additive explanations (SHAP), an interpretable ML method grounded in game theory, was used to explain ML models with local explanation properties. In this study, ML experiments (binary classification) were conducted for three published metabolomics datasets using PLS-DA, random forests, gradient boosting, and extreme gradient boosting (XGBoost). Using one of the datasets, PLS-DA model was explained using VIP scores, while one of the best-performing models, a random forest model, was interpreted using Tree SHAP. The results show that SHAP has a more explanation depth than PLS-DA's VIP, making it a powerful method for rationalizing machine learning predictions from metabolomics studies.
Collapse
Affiliation(s)
- Olatomiwa O Bifarin
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, Georgia, United States of America
| |
Collapse
|
11
|
Zhu F, Feng F, Toimil-Molares ME, Trautmann C, Wang L, Zhou J, Cheng J, Li H. Triazol-Methanaminium-Pillar[5]arene-Functionalized Single Nanochannel for Quantitative Analysis of Pyrophosphate in Water. Anal Chem 2022; 94:14889-14897. [PMID: 36269622 DOI: 10.1021/acs.analchem.2c02161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Inorganic pyrophosphate (PPi) is an important biological functional anion and plays crucial roles in life science, environmental science, medicine, and chemical process. Quantification of PPi in water has far-reaching significance for life exploration, disease diagnosis, and water pollution control. The label-free quantitative detection of PPi anions with a nanofluidic sensing device based on a conical single nanochannel is demonstrated. The channel surface is functionalized with a synthetic PPi receptor, triazol-methanaminium-functionalized pillar[5]arene (TAMAP5), using carbodiimide coupling chemistry. Due to the specific binding between TAMAP5 and PPi, the functionalized nanochannel can discriminate PPi from other inorganic anions with high selectivity through ionic current recording, even in the presence of various interfering anions. The current response exhibits a linear correlation with PPi concentration in the range from 1 × 10-7 to 1 × 10-4 M with a limit of detection of 6.8 × 10-7 M. A spike-and-recovery analysis of PPi in East Lake water samples indicates that the proposed nanofluidic sensor has the ability to quantitate micromolar concentrations of PPi in environmental water samples.
Collapse
Affiliation(s)
- Fei Zhu
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University (CCNU), Wuhan 430079, P. R. China.,Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Department of Pharmacology, School of Basic Medical Science, Hubei University of Medicine, Shiyan 442000, Hubei, P. R. China
| | - Fudan Feng
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University (CCNU), Wuhan 430079, P. R. China
| | | | - Christina Trautmann
- GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt 64291, Germany.,Technische Universitat Darmstadt, Darmstadt 64287, Germany
| | - Li Wang
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University (CCNU), Wuhan 430079, P. R. China
| | - Juan Zhou
- State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430071, P. R. China
| | - Jing Cheng
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University (CCNU), Wuhan 430079, P. R. China
| | - Haibing Li
- Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University (CCNU), Wuhan 430079, P. R. China
| |
Collapse
|
12
|
Irajizad E, Fahrmann JF, Long JP, Vykoukal J, Kobayashi M, Capello M, Yu CY, Cai Y, Hsiao FC, Patel N, Park S, Peng Q, Dennison JB, Kato T, Tai MC, Taguchi A, Kadara H, Wistuba II, Katayama H, Do KA, Hanash SM, Ostrin EJ. A Comprehensive Search of Non-Canonical Proteins in Non-Small Cell Lung Cancer and Their Impact on the Immune Response. Int J Mol Sci 2022; 23:ijms23168933. [PMID: 36012199 PMCID: PMC9409146 DOI: 10.3390/ijms23168933] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/05/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
There is substantial interest in mining neoantigens for cancer applications. Non-canonical proteins resulting from frameshift mutations have been identified as neoantigens in cancer. We investigated the landscape of non-canonical proteins in non-small cell lung cancer (NSCLC) and their induced immune response in the form of autoantibodies. A database of cryptoproteins was computationally constructed and comprised all alternate open reading frames (altORFs) and ORFs identified in pseudogenes, noncoding RNAs, and untranslated regions of mRNAs that did not align with known canonical proteins. Proteomic profiles of seventeen lung adenocarcinoma (LUAD) cell lines were searched to evaluate the occurrence of cryptoproteins. To assess the immunogenicity, immunoglobulin (Ig)-bound cryptoproteins in plasmas were profiled by mass spectrometry. The specimen set consisted of plasmas from 30 newly diagnosed NSCLC cases, pre-diagnostic plasmas from 51 NSCLC cases, and 102 control plasmas. An analysis of LUAD cell lines identified 420 cryptoproteins. Plasma Ig-bound analyses revealed 90 cryptoproteins uniquely found in cases and 14 cryptoproteins that had a fold-change >2 compared to controls. In pre-diagnostic samples, 17 Ig-bound cryptoproteins yielded an odds ratio ≥2. Eight Ig-bound cryptoproteins were elevated in both pre-diagnostic and newly diagnosed cases compared to controls. Cryptoproteins represent a class of neoantigens that induce an autoantibody response in NSCLC.
Collapse
Affiliation(s)
- Ehsan Irajizad
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Johannes F. Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - James P. Long
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Jody Vykoukal
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Makoto Kobayashi
- Department of Basic Pathology, School of Medicine, Fukushima Medical University, Hikarigaoka, Fukushima 960-1247, Japan
| | - Michela Capello
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Chuan-Yih Yu
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Yining Cai
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Fu Chung Hsiao
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Nikul Patel
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Soyoung Park
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Qian Peng
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Jennifer B. Dennison
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Taketo Kato
- Department of Thoracic Surgery, Nagoya University, Nagoya 464-8601, Japan
| | - Mei Chee Tai
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Ayumu Taguchi
- Division of Molecular Diagnostics, Aichi Cancer Center, Nagoya 464-8601, Japan
- Division of Advanced Cancer Diagnostics, Nagoya University Graduate School of Medicine, Nagoya 464-8601, Japan
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Ignacio I. Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Hiroyuki Katayama
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - Kim-Anh Do
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
- Correspondence: (K.-A.D.); (S.M.H.); (E.J.O.); Tel.: +1-713-745-5242 (S.M.H.)
| | - Samir M. Hanash
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
- Correspondence: (K.-A.D.); (S.M.H.); (E.J.O.); Tel.: +1-713-745-5242 (S.M.H.)
| | - Edwin J. Ostrin
- Departments of General Internal Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
- Correspondence: (K.-A.D.); (S.M.H.); (E.J.O.); Tel.: +1-713-745-5242 (S.M.H.)
| |
Collapse
|
13
|
Imputation of Missing Values for Multi-Biospecimen Metabolomics Studies: Bias and Effects on Statistical Validity. Metabolites 2022; 12:metabo12070671. [PMID: 35888795 PMCID: PMC9317643 DOI: 10.3390/metabo12070671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 02/05/2023] Open
Abstract
The analysis of high-throughput metabolomics mass spectrometry data across multiple biological sample types (biospecimens) poses challenges due to missing data. During differential abundance analysis, dropping samples with missing values can lead to severe loss of data as well as biased results in group comparisons and effect size estimates. However, the imputation of missing data (the process of replacing missing data with estimated values such as a mean) may compromise the inherent intra-subject correlation of a metabolite across multiple biospecimens from the same subject, which in turn may compromise the efficacy of the statistical analysis of differential metabolites in biomarker discovery. We investigated imputation strategies when considering multiple biospecimens from the same subject. We compared a novel, but simple, approach that consists of combining the two biospecimen data matrices (rows and columns of subjects and metabolites) and imputes the two biospecimen data matrices together to an approach that imputes each biospecimen data matrix separately. We then compared the bias in the estimation of the intra-subject multi-specimen correlation and its effects on the validity of statistical significance tests between two approaches. The combined approach to multi-biospecimen studies has not been evaluated previously even though it is intuitive and easy to implement. We examine these two approaches for five imputation methods: random forest, k nearest neighbor, expectation-maximization with bootstrap, quantile regression, and half the minimum observed value. Combining the biospecimen data matrices for imputation did not greatly increase efficacy in conserving the correlation structure or improving accuracy in the statistical conclusions for most of the methods examined. Random forest tended to outperform the other methods in all performance metrics, except specificity.
Collapse
|
14
|
Zhang Y, Lin X, Gao Z, Bai S. A Novel Method for Feature Selection Based on Molecular Interactive Effect Network. J Pharm Biomed Anal 2022; 218:114873. [DOI: 10.1016/j.jpba.2022.114873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 10/18/2022]
|
15
|
Haince JF, Joubert P, Bach H, Ahmed Bux R, Tappia PS, Ramjiawan B. Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome. Int J Mol Sci 2022; 23:ijms23031215. [PMID: 35163138 PMCID: PMC8835988 DOI: 10.3390/ijms23031215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 12/19/2022] Open
Abstract
The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.
Collapse
Affiliation(s)
| | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Department of Pathology, Laval University, Quebec, QC G1V 4G5, Canada;
| | - Horacio Bach
- Department of Medicine, Division of Infectious Diseases, University of British Columbia, Vancouver, BC V6H 3Z6, Canada;
| | - Rashid Ahmed Bux
- BioMark Diagnostics Inc., Richmond, BC V6X 2W8, Canada; (J.-F.H.); (R.A.B.)
| | - Paramjit S. Tappia
- Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada;
- Correspondence: ; Tel.: +1-204-258-1230
| | - Bram Ramjiawan
- Asper Clinical Research Institute, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada;
- Department of Pharmacology & Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
| |
Collapse
|
16
|
Toumazis I, Erdogan SA, Bastani M, Leung A, Plevritis SK. A Cost-Effectiveness Analysis of Lung Cancer Screening With Low-Dose Computed Tomography and a Diagnostic Biomarker. JNCI Cancer Spectr 2021; 5:pkab081. [PMID: 34738073 PMCID: PMC8564700 DOI: 10.1093/jncics/pkab081] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 08/12/2021] [Accepted: 08/20/2021] [Indexed: 12/17/2022] Open
Abstract
Background The Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) reduces the false-positive rate of lung cancer screening but introduces prolonged periods of uncertainty for indeterminate findings. We assess the cost-effectiveness of a screening program that assesses indeterminate findings earlier via a hypothetical diagnostic biomarker introduced in place of Lung-RADS 3 and 4A guidelines. Methods We evaluated the performance of the US Preventive Services Task Force (USPSTF) recommendations on lung cancer screening with and without a hypothetical noninvasive diagnostic biomarker using a validated microsimulation model. The diagnostic biomarker assesses the malignancy of indeterminate nodules, replacing Lung-RADS 3 and 4A guidelines, and is characterized by a varying sensitivity profile that depends on nodules' size, specificity, and cost. We tested the robustness of our findings through univariate sensitivity analyses. Results A lung cancer screening program per the USPSTF guidelines that incorporates a diagnostic biomarker with at least medium sensitivity profile and 90% specificity, that costs $250 or less, is cost-effective with an incremental cost-effectiveness ratio lower than $100 000 per quality-adjusted life year, and improves lung cancer-specific mortality reduction while requiring fewer screening exams than the USPSTF guidelines with Lung-RADS. A screening program with a biomarker costing $750 or more is not cost-effective. The health benefits accrued and costs associated with the screening program are sensitive to the disutility of indeterminate findings and specificity of the biomarker, respectively. Conclusions Lung cancer screening that incorporates a diagnostic biomarker, in place of Lung-RADS 3 and 4A guidelines, could improve the cost-effectiveness of the screening program and warrants further investigation.
Collapse
Affiliation(s)
- Iakovos Toumazis
- Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, CA, USA
| | - S Ayca Erdogan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Mehrad Bastani
- Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, CA, USA
| | - Ann Leung
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Sylvia K Plevritis
- Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, CA, USA
| |
Collapse
|
17
|
Madama D, Martins R, Pires AS, Botelho MF, Alves MG, Abrantes AM, Cordeiro CR. Metabolomic Profiling in Lung Cancer: A Systematic Review. Metabolites 2021; 11:630. [PMID: 34564447 PMCID: PMC8471464 DOI: 10.3390/metabo11090630] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer continues to be a significant burden worldwide and remains the leading cause of cancer-associated mortality. Two considerable challenges posed by this disease are the diagnosis of 61% of patients in advanced stages and the reduced five-year survival rate of around 4%. Noninvasively collected samples are gaining significant interest as new areas of knowledge are being sought and opened up. Metabolomics is one of these growing areas. In recent years, the use of metabolomics as a resource for the study of lung cancer has been growing. We conducted a systematic review of the literature from the past 10 years in order to identify some metabolites associated with lung cancer. More than 150 metabolites have been associated with lung cancer-altered metabolism. These were detected in different biological samples by different metabolomic analytical platforms. Some of the published results have been consistent, showing the presence/alteration of specific metabolites. However, there is a clear variability due to lack of a full clinical characterization of patients or standardized patients selection. In addition, few published studies have focused on the added value of the metabolomic profile as a means of predicting treatment response for lung cancer. This review reinforces the need for consistent and systematized studies, which will help make it possible to identify metabolic biomarkers and metabolic pathways responsible for the mechanisms that promote tumor progression, relapse and eventually resistance to therapy.
Collapse
Affiliation(s)
- Daniela Madama
- Clinical Academic Center of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal;
| | - Rosana Martins
- 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;
| | - Ana S. Pires
- Clinical Academic Center of Coimbra (CACC), Center 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; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Maria F. Botelho
- Clinical Academic Center of Coimbra (CACC), Center 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; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Marco G. Alves
- Department of Anatomy, Unit for Multidisciplinary Research in Biomedicine (UMIB), Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, 4099-002 Porto, Portugal;
| | - Ana M. Abrantes
- Clinical Academic Center of Coimbra (CACC), Center 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; (A.S.P.); (M.F.B.); (A.M.A.)
| | - Carlos R. Cordeiro
- Clinical Academic Center of Coimbra (CACC), Department of Pulmonology, Faculty of Medicine, University Hospitals of Coimbra, University of Coimbra, 3004-504 Coimbra, Portugal;
| |
Collapse
|
18
|
Marchev AS, Vasileva LV, Amirova KM, Savova MS, Balcheva-Sivenova ZP, Georgiev MI. Metabolomics and health: from nutritional crops and plant-based pharmaceuticals to profiling of human biofluids. Cell Mol Life Sci 2021; 78:6487-6503. [PMID: 34410445 PMCID: PMC8558153 DOI: 10.1007/s00018-021-03918-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 12/19/2022]
Abstract
During the past decade metabolomics has emerged as one of the fastest developing branches of “-omics” technologies. Metabolomics involves documentation, identification, and quantification of metabolites through modern analytical platforms in various biological systems. Advanced analytical tools, such as gas chromatography–mass spectrometry (GC/MS), liquid chromatography–mass spectroscopy (LC/MS), and non-destructive nuclear magnetic resonance (NMR) spectroscopy, have facilitated metabolite profiling of complex biological matrices. Metabolomics, along with transcriptomics, has an influential role in discovering connections between genetic regulation, metabolite phenotyping and biomarkers identification. Comprehensive metabolite profiling allows integration of the summarized data towards manipulation of biosynthetic pathways, determination of nutritional quality markers, improvement in crop yield, selection of desired metabolites/genes, and their heritability in modern breeding. Along with that, metabolomics is invaluable in predicting the biological activity of medicinal plants, assisting the bioactivity-guided fractionation process and bioactive leads discovery, as well as serving as a tool for quality control and authentication of commercial plant-derived natural products. Metabolomic analysis of human biofluids is implemented in clinical practice to discriminate between physiological and pathological state in humans, to aid early disease biomarker discovery and predict individual response to drug therapy. Thus, metabolomics could be utilized to preserve human health by improving the nutritional quality of crops and accelerating plant-derived bioactive leads discovery through disease diagnostics, or through increasing the therapeutic efficacy of drugs via more personalized approach. Here, we attempt to explore the potential value of metabolite profiling comprising the above-mentioned applications of metabolomics in crop improvement, medicinal plants utilization, and, in the prognosis, diagnosis and management of complex diseases.
Collapse
Affiliation(s)
- Andrey S Marchev
- Department Plant Cell Biotechnology, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria.,Laboratory of Metabolomics, Department of Biotechnology, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 4000, Plovdiv, Bulgaria
| | - Liliya V Vasileva
- Department Plant Cell Biotechnology, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria.,Laboratory of Metabolomics, Department of Biotechnology, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 4000, Plovdiv, Bulgaria
| | - Kristiana M Amirova
- Department Plant Cell Biotechnology, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria.,Laboratory of Metabolomics, Department of Biotechnology, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 4000, Plovdiv, Bulgaria
| | - Martina S Savova
- Department Plant Cell Biotechnology, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria.,Laboratory of Metabolomics, Department of Biotechnology, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 4000, Plovdiv, Bulgaria
| | - Zhivka P Balcheva-Sivenova
- Department Plant Cell Biotechnology, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria.,Laboratory of Metabolomics, Department of Biotechnology, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 4000, Plovdiv, Bulgaria
| | - Milen I Georgiev
- Department Plant Cell Biotechnology, Center of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria. .,Laboratory of Metabolomics, Department of Biotechnology, The Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 4000, Plovdiv, Bulgaria.
| |
Collapse
|
19
|
Kowalczyk T, Kisluk J, Pietrowska K, Godzien J, Kozlowski M, Reszeć J, Sierko E, Naumnik W, Mróz R, Moniuszko M, Kretowski A, Niklinski J, Ciborowski M. The Ability of Metabolomics to Discriminate Non-Small-Cell Lung Cancer Subtypes Depends on the Stage of the Disease and the Type of Material Studied. Cancers (Basel) 2021; 13:cancers13133314. [PMID: 34282765 PMCID: PMC8268630 DOI: 10.3390/cancers13133314] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 02/04/2023] Open
Abstract
Identification of the NSCLC subtype at an early stage is still quite sophisticated. Metabolomics analysis of tissue and plasma of NSCLC patients may indicate new, and yet unknown, metabolic pathways active in the NSCLC. Our research characterized the metabolomics profile of tissue and plasma of patients with early and advanced NSCLC stage. Samples were subjected to thorough metabolomics analyses using liquid chromatography-mass spectrometry (LC-MS) technique. Tissue and/or plasma samples from 137 NSCLC patients were analyzed. Based on the early stage tissue analysis, more than 200 metabolites differentiating adenocarcinoma (ADC) and squamous cell lung carcinoma (SCC) subtypes as well as normal tissue, were identified. Most of the identified metabolites were amino acids, fatty acids, carnitines, lysoglycerophospholipids, sphingomyelins, plasmalogens and glycerophospholipids. Moreover, metabolites related to N-acyl ethanolamine (NAE) biosynthesis, namely glycerophospho (N-acyl) ethanolamines (GP-NAE), which discriminated early-stage SCC from ADC, have also been identified. On the other hand, the analysis of plasma of chronic obstructive pulmonary disease (COPD) and NSCLC patients allowed exclusion of the metabolites related to the inflammatory state in lungs and the identification of compounds (lysoglycerophospholipids, glycerophospholipids and sphingomyelins) truly characteristic to cancer. Our results, among already known, showed novel, thus far not described, metabolites discriminating NSCLC subtypes, especially in the early stage of cancer. Moreover, the presented results also indicated the activity of new metabolic pathways in NSCLC. Further investigations on the role of NAE biosynthesis pathways in the early stage of NSCLC may reveal new prognostic and diagnostic targets.
Collapse
Affiliation(s)
- Tomasz Kowalczyk
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
| | - Joanna Kisluk
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland; (J.K.); (J.N.)
| | - Karolina Pietrowska
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
| | - Joanna Godzien
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
| | - Miroslaw Kozlowski
- Department of Thoracic Surgery, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland;
| | - Joanna Reszeć
- Department of Medical Patomorphology, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland;
| | - Ewa Sierko
- Department of Oncology, Medical University of Bialystok, Ogrodowa 12, 15-027 Bialystok, Poland;
| | - Wojciech Naumnik
- 1st Department of Lung Diseases and Tuberculosis, Medical University of Bialystok, Żurawia 14, 15-540 Bialystok, Poland;
| | - Robert Mróz
- 2nd Department of Lung Diseases and Tuberculosis, Medical University of Bialystok, Żurawia 14, 15-540 Bialystok, Poland;
| | - Marcin Moniuszko
- Department of Allergology and Internal Medicine, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland;
- Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland
| | - Adam Kretowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland
| | - Jacek Niklinski
- Department of Clinical Molecular Biology, Medical University of Bialystok, Waszyngtona 13, 15-269 Bialystok, Poland; (J.K.); (J.N.)
| | - Michal Ciborowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, M. Skłodowskiej-Curie 24a, 15-276 Bialystok, Poland; (T.K.); (K.P.); (J.G.); (A.K.)
- Correspondence:
| |
Collapse
|
20
|
Zheng Y, He Z, Kong Y, Huang X, Zhu W, Liu Z, Gong L. Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway. J Proteome Res 2021; 20:3444-3454. [PMID: 34056907 DOI: 10.1021/acs.jproteome.0c01019] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Lung cancer (LC) is one of the most malignant cancers in the world, but currently, it lacks effective noninvasive biomarkers to assist its early diagnosis. Our study aims to discover potential serum diagnostic biomarkers for LC. In our study, untargeted serum metabolomics of a discovery cohort and targeted analysis of a test cohort were performed based on gas chromatography-mass spectrometry. Both univariate and multivariate statistical analyses were employed to screen for differential metabolites between LC and healthy control (HC), followed by the selection of candidate biomarkers through multiple algorithms. The results showed that 15 metabolites were significantly dysregulated between LC and HC, and a panel, comprising cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid, was demonstrated to have excellent differentiating capability for LC based on multiple classification modelings. In addition, the molecular interaction analysis combined with transcriptomics revealed a close correlation between the candidate biomarkers and LC proliferation via a Ca2+ signaling pathway. Our study discovered that cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid in combination could be a promising diagnostic biomarker for LC, and most importantly, our results will shed some light on the pathophysiological mechanism underlying LC to understand it deeply. The data that support the findings of this study are openly available in MetaboLights at https://www.ebi.ac.uk/metabolights/, reference number MTBLS1517.
Collapse
Affiliation(s)
- Yuan Zheng
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Zhuoru He
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Yu Kong
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Plant Science Research Centre, Chinese Academy of Sciences, Shanghai Chenshan Botanical Garden, Shanghai 201602, PR China
| | - Xinjie Huang
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Wei Zhu
- Department of Cardiothoracic Surgery, The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Zhongqiu Liu
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| | - Lingzhi Gong
- International Institute for Translational Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, PR China
| |
Collapse
|
21
|
Recent developments in molecular sensor designs for inorganic pyrophosphate detection and biological imaging. Coord Chem Rev 2021. [DOI: 10.1016/j.ccr.2020.213744] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
22
|
Jahagirdar S, Saccenti E. Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine. J Proteome Res 2020; 20:932-949. [PMID: 33267585 PMCID: PMC7786380 DOI: 10.1021/acs.jproteome.0c00696] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Networks
and network analyses are fundamental tools of systems
biology. Networks are built by inferring pair-wise relationships among
biological entities from a large number of samples such that subject-specific
information is lost. The possibility of constructing these sample
(individual)-specific networks from single molecular profiles might
offer new insights in systems and personalized medicine and as a consequence
is attracting more and more research interest. In this study, we evaluated
and compared LIONESS (Linear Interpolation to Obtain Network Estimates
for Single Samples) and ssPCC (single sample network based on Pearson
correlation) in the metabolomics context of metabolite–metabolite
association networks. We illustrated and explored the characteristics
of these two methods on (i) simulated data, (ii) data generated from
a dynamic metabolic model to simulate real-life observed metabolite
concentration profiles, and (iii) 22 metabolomic data sets and (iv)
we applied single sample network inference to a study case pertaining
to the investigation of necrotizing soft tissue infections to show
how these methods can be applied in metabolomics. We also proposed
some adaptations of the methods that can be used for data exploration.
Overall, despite some limitations, we found single sample networks
to be a promising tool for the analysis of metabolomics data.
Collapse
Affiliation(s)
- Sanjeevan Jahagirdar
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| |
Collapse
|
23
|
Volatile organic compounds analysis optimization and biomarker discovery in urine of Non-Hodgkin lymphoma patients before and during chemotherapy. Microchem J 2020. [DOI: 10.1016/j.microc.2020.105479] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
24
|
Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Evans ED, Duvallet C, Chu ND, Oberst MK, Murphy MA, Rockafellow I, Sontag D, Alm EJ. Predicting human health from biofluid-based metabolomics using machine learning. Sci Rep 2020; 10:17635. [PMID: 33077825 PMCID: PMC7572502 DOI: 10.1038/s41598-020-74823-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/05/2020] [Indexed: 12/15/2022] Open
Abstract
Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.
Collapse
Affiliation(s)
- Ethan D Evans
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Claire Duvallet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Biobot Analytics, Somerville, MA, 02143, USA
| | - Nathaniel D Chu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael K Oberst
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael A Murphy
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Isaac Rockafellow
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Superpedestrian, Cambridge, MA, 02139, USA
| | - David Sontag
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| |
Collapse
|
27
|
Lee KB, Ang L, Yau WP, Seow WJ. Association between Metabolites and the Risk of Lung Cancer: A Systematic Literature Review and Meta-Analysis of Observational Studies. Metabolites 2020; 10:E362. [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] [Grants] [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.
Collapse
Affiliation(s)
- Kian Boon Lee
- Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore 117543, Singapore; (K.B.L.); (W.-P.Y.)
| | - Lina Ang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore;
| | - Wai-Ping Yau
- Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore 117543, Singapore; (K.B.L.); (W.-P.Y.)
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore;
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
| |
Collapse
|
28
|
Dong C, Li X, Li K, Zheng C, Ying J. The Expression Pattern of ZIC5 and its Prognostic Value in Lung Cancer. Cancer Biother Radiopharm 2020; 36:407-411. [PMID: 32633542 DOI: 10.1089/cbr.2020.3775] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background: Lung cancer (LC) represents a leading cause of cancer-related deaths. The aim of this study was to analyze the application value of ZIC5 in the prognosis evaluation of LC. Materials and Methods: The mRNA and protein levels of ZIC5 in LC tissues and adjacent normal controls were detected by quantitative real-time polymerase chain reaction and Western blot analysis, respectively. Chi-square test was performed to estimate the association between ZIC5 expression and clinical features. Furthermore, overall survival (OS) curves were plotted using Kaplan-Meier method with log rank test. The prognosis analysis was analyzed using Cox proportional hazards model. Results: Compared with that of noncancerous tissues, the expression of ZIC5 in LC tissues was significantly increased at both protein and mRNA levels (p < 0.001). Moreover, ZIC5 expression showed a positive correlation with differentiation (p = 0.001) and tumor size (p = 0.016). Survival analysis suggested that high expression of ZIC5 predicted shorter OS (log rank test, p = 0.007). ZIC5 might be an independent prognostic biomarker for LC (hazard ratio = 2.892; 95% confidence interval, 1.297-6.449; p = 0.009). Conclusions: ZIC5 expression is upregulated in LC, and it may be employed as a prognostic biomarker for patients with LC.
Collapse
Affiliation(s)
- Caijun Dong
- Department of Thoracic Surgery, Hwa Mei Hospital, Ningbo Instiute of Life and HeaIth Industry, University of Chinese Academy of Sciences, Ningbo Zhenjiang, China
| | - Xiangguo Li
- Department of Respiratory Medicine, Wenling Hospital, Wenzhou Medical University, Zhejiang, China
| | - Kang Li
- Department of Respiratory Medicine, Wenling Hospital, Wenzhou Medical University, Zhejiang, China
| | - Chao Zheng
- Radiotherapy and Chemotherapy Department, Taizhou Cancer Hospital, Zhejiang, China
| | - Jianjian Ying
- Department of Thoracic Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, China
| |
Collapse
|
29
|
Cainap C, Pop LA, Balacescu O, Cainap SS. Early diagnosis and screening in lung cancer. Am J Cancer Res 2020; 10:1993-2009. [PMID: 32774997 PMCID: PMC7407360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 06/25/2020] [Indexed: 06/11/2023] Open
Abstract
Lung cancer is the third most diagnosed cancer, but the first cause of cancer-related deaths worldwide. This rather high death rate is due mainly to the fact that most patients are diagnosed with advanced-stage cancer, for which the conventional treatment does not work. The most used screening method for lung cancer is a low-dose CT scan, but it is recommended for specific age populations and it also started different debates on its advantages for lung cancer diagnosis. Over the year, several new techniques have been developed that are less invasive, have lower side effect, and can be implemented at all types of populations. This article aimed to present the advantages and disadvantages of using several methods for lung cancer diagnosis, including analysis of volatile organic compounds, exhaled breath condensate analysis and specific genomic approaches.
Collapse
Affiliation(s)
- Calin Cainap
- Department of Oncology, “Iuliu Hatieganu” University of Medicine and PharmacyCluj-Napoca, Romania
- Prof. Dr. Ion Chiricuta Institute of OncologyCluj-Napoca, Romania
| | - Laura A Pop
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, University of Medicine and Pharmacy Iuliu HatieganuCluj-Napoca, Romania
| | - Ovidiu Balacescu
- Department of Functional Genomics and Experimental Pathology, Prof. Dr. Ion Chiricuta Institute of OncologyCluj-Napoca, Romania
| | - Simona S Cainap
- Department of Pediatric Cardiology, Emergency County Hospital for Children, Pediatric Clinic no 2Cluj-Napoca, Romania
- Department of Mother and Child, “Iuliu Hatieganu” University of Medicine and PharmacyCluj-Napoca, Romania
| |
Collapse
|
30
|
Chong J, Wishart DS, Xia J. Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis. ACTA ACUST UNITED AC 2020; 68:e86. [PMID: 31756036 DOI: 10.1002/cpbi.86] [Citation(s) in RCA: 1344] [Impact Index Per Article: 336.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.
Collapse
Affiliation(s)
- Jasmine Chong
- Institute of Parasitology, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada
| | - David S Wishart
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.,Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada.,Department of Animal Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada.,Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
31
|
Callejón-Leblic B, Arias-Borrego A, Rodríguez-Moro G, Navarro Roldán F, Pereira-Vega A, Gómez-Ariza JL, García-Barrera T. Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins. Adv Clin Chem 2020; 100:91-137. [PMID: 33453868 DOI: 10.1016/bs.acc.2020.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.
Collapse
Affiliation(s)
- Belén Callejón-Leblic
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Ana Arias-Borrego
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Gema Rodríguez-Moro
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Francisco Navarro Roldán
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Integrated Sciences-Cell Biology, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | | | - José Luis Gómez-Ariza
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain
| | - Tamara García-Barrera
- Research Center for Natural Resources, Health and the Environment (RENSMA), University of Huelva, Huelva, Spain; Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Huelva, Spain.
| |
Collapse
|
32
|
On the Use of Correlation and MI as a Measure of Metabolite-Metabolite Association for Network Differential Connectivity Analysis. Metabolites 2020; 10:metabo10040171. [PMID: 32344593 PMCID: PMC7241243 DOI: 10.3390/metabo10040171] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/15/2020] [Accepted: 04/22/2020] [Indexed: 02/06/2023] Open
Abstract
Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite-metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson's or Spearman's correlation when the application is to quantify and detect differentially connected metabolites.
Collapse
|
33
|
Mendez KM, Reinke SN, Broadhurst DI. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification. Metabolomics 2019; 15:150. [PMID: 31728648 PMCID: PMC6856029 DOI: 10.1007/s11306-019-1612-4] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 11/05/2019] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Metabolomics is increasingly being used in the clinical setting for disease diagnosis, prognosis and risk prediction. Machine learning algorithms are particularly important in the construction of multivariate metabolite prediction. Historically, partial least squares (PLS) regression has been the gold standard for binary classification. Nonlinear machine learning methods such as random forests (RF), kernel support vector machines (SVM) and artificial neural networks (ANN) may be more suited to modelling possible nonlinear metabolite covariance, and thus provide better predictive models. OBJECTIVES We hypothesise that for binary classification using metabolomics data, non-linear machine learning methods will provide superior generalised predictive ability when compared to linear alternatives, in particular when compared with the current gold standard PLS discriminant analysis. METHODS We compared the general predictive performance of eight archetypal machine learning algorithms across ten publicly available clinical metabolomics data sets. The algorithms were implemented in the Python programming language. All code and results have been made publicly available as Jupyter notebooks. RESULTS There was only marginal improvement in predictive ability for SVM and ANN over PLS across all data sets. RF performance was comparatively poor. The use of out-of-bag bootstrap confidence intervals provided a measure of uncertainty of model prediction such that the quality of metabolomics data was observed to be a bigger influence on generalised performance than model choice. CONCLUSION The size of the data set, and choice of performance metric, had a greater influence on generalised predictive performance than the choice of machine learning algorithm.
Collapse
Affiliation(s)
- Kevin M Mendez
- Centre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia
| | - Stacey N Reinke
- Centre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia
| | - David I Broadhurst
- Centre for Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, 6027, Australia.
| |
Collapse
|
34
|
Li JZ, Lai YY, Sun JY, Guan LN, Zhang HF, Yang C, Ma YF, Liu T, Zhao W, Yan XL, Li SM. Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer. Transl Lung Cancer Res 2019; 8:489-499. [PMID: 31555521 DOI: 10.21037/tlcr.2019.07.02] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates. Methods In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls. Results In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls. Conclusions This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.
Collapse
Affiliation(s)
- Jian-Zhong Li
- Department of Thoracic Surgery, Second Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710004, China
| | - Yuan-Yang Lai
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Fourth Medical University, Xi'an 710038, China
| | - Jian-Yong Sun
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Fourth Medical University, Xi'an 710038, China
| | - Li-Na Guan
- Department of Thoracic Surgery, The 211th Hospital of Chinese People's Liberation Army, Harbin 150000, China.,Department of Respiratory, First Affiliated Hospital of Harbin Medical University, Harbin 150000, China
| | - Hong-Fei Zhang
- Department of Thoracic Surgery, The 211th Hospital of Chinese People's Liberation Army, Harbin 150000, China
| | - Chen Yang
- Postdoctoral Research Station of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430000, China.,Department of Neurosurgery, Tangdu Hospital, Air Force Fourth Medical University, Xi'an 710038, China
| | - Yue-Feng Ma
- Department of Thoracic Surgery, Second Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710004, China
| | - Tao Liu
- Department of Orthopaedics, Tangdu Hospital, Air Force Fourth Medical University, Xi'an 710038, China
| | - Wen Zhao
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Fourth Medical University, Xi'an 710038, China
| | - Xiao-Long Yan
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Fourth Medical University, Xi'an 710038, China
| | - Shao-Min Li
- Department of Thoracic Surgery, Second Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710004, China
| |
Collapse
|
35
|
Tang Y, Li Z, Lazar L, Fang Z, Tang C, Zhao J. Metabolomics workflow for lung cancer: Discovery of biomarkers. Clin Chim Acta 2019; 495:436-445. [DOI: 10.1016/j.cca.2019.05.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 05/13/2019] [Accepted: 05/13/2019] [Indexed: 12/20/2022]
|
36
|
Sun Q, Zhao W, Wang L, Guo F, Song D, Zhang Q, Zhang D, Fan Y, Wang J. Integration of metabolomic and transcriptomic profiles to identify biomarkers in serum of lung cancer. J Cell Biochem 2019; 120:11981-11989. [PMID: 30805978 DOI: 10.1002/jcb.28482] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 11/02/2018] [Accepted: 01/02/2019] [Indexed: 01/24/2023]
Abstract
We used blood serum samples collected from 31 lung cancer (LC) patients and 29 healthy volunteers in this study. Levels of serum metabolites were qualitative quantified with gas chromatography-mass spectrometry (GC-MS), and the data were analyzed by partial least-squares discrimination analysis (PLS-DA). Based on the Kyoto Encyclopedia of Genes and Genomes database, we performed pathway-based analysis utilizing metabolites presented at differential abundance between the LC serum samples and the normal healthy serum samples for systematical investigation on the metabolic alterations associated with LC pathogenesis. Finally, we analyzed the significantly enriched pathways as well as their relevant differentially expressed messenger RNAs, and drawn a correlation network plot to identify the serum metabolic biomarkers and the significantly altered metabolic pathways for LC. GC-MS analysis showed that 23 of the 169 metabolites identified were significantly different. PLS-DA model revealed that 13 of these metabolites were with variable importance > 1, and particularly five were with area under curve > 0.9. Pathway-based analysis demonstrated that five of eight enriched metabolic pathways were statistically significant with false discovery rate < 0.05. Lastly, the correlation networks between these pathways and their related genes suggested that 29 genes had correlation degree > 10, which were mainly engaged in the purine metabolism. In conclusion, we identified indole-3-lactate, erythritol, adenosine-5-phosphate, paracetamol and threitol as serum metabolic biomarkers for LC through metabolomics analysis. Besides, we identified the purine metabolism as the significantly altered metabolic pathway in LC with the help of transcriptomics analysis.
Collapse
Affiliation(s)
- Quan Sun
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wei Zhao
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lei Wang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Fei Guo
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Dongjian Song
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qian Zhang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Da Zhang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yingzhong Fan
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jiaxiang Wang
- Department of Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| |
Collapse
|
37
|
Ash JR, Kuenemann MA, Rotroff D, Motsinger-Reif A, Fourches D. Cheminformatics approach to exploring and modeling trait-associated metabolite profiles. J Cheminform 2019; 11:43. [PMID: 31236709 PMCID: PMC6591908 DOI: 10.1186/s13321-019-0366-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 06/17/2019] [Indexed: 12/17/2022] Open
Abstract
Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers. ![]()
Collapse
Affiliation(s)
- Jeremy R Ash
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Melaine A Kuenemann
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Daniel Rotroff
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Alison Motsinger-Reif
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA. .,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
| |
Collapse
|
38
|
Best MG, In 't Veld SGJG, Sol N, Wurdinger T. RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA. Nat Protoc 2019; 14:1206-1234. [PMID: 30894694 DOI: 10.1038/s41596-019-0139-5] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 01/17/2019] [Indexed: 12/12/2022]
Abstract
Blood-based diagnostics tests, using individual or panels of biomarkers, may revolutionize disease diagnostics and enable minimally invasive therapy monitoring. However, selection of the most relevant biomarkers from liquid biosources remains an immense challenge. We recently presented the thromboSeq pipeline, which enables RNA sequencing and cancer classification via self-learning and swarm intelligence-enhanced bioinformatics algorithms using blood platelet RNA. Here, we provide the wet-lab protocol for the generation of platelet RNA-sequencing libraries and the dry-lab protocol for the development of swarm intelligence-enhanced machine-learning-based classification algorithms. The wet-lab protocol includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence-enhanced support vector machine (SVM) algorithm development. This protocol enables platelet RNA profiling from 500 pg of platelet RNA and allows automated and optimized biomarker panel selection. The wet-lab protocol can be performed in 5 d before sequencing, and the algorithm development can be completed in 2 d, depending on computational resources. The protocol requires basic molecular biology skills and a basic understanding of Linux and R. In all, with this protocol, we aim to enable the scientific community to test platelet RNA for diagnostic algorithm development.
Collapse
Affiliation(s)
- Myron G Best
- Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands. .,Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands. .,Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands.
| | - Sjors G J G In 't Veld
- Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
| | - Nik Sol
- Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands.,Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
| | - Thomas Wurdinger
- Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands. .,Brain Tumor Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands.
| |
Collapse
|
39
|
Lowrey CR, Bourke TC, Bagg SD, Dukelow SP, Scott SH. A postural unloading task to assess fast corrective responses in the upper limb following stroke. J Neuroeng Rehabil 2019; 16:16. [PMID: 30691482 PMCID: PMC6350318 DOI: 10.1186/s12984-019-0483-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/15/2019] [Indexed: 01/06/2023] Open
Abstract
Background Robotic technologies to measure human behavior are emerging as a new approach to assess brain function. Recently, we developed a robot-based postural Load Task to assess corrective responses to mechanical disturbances to the arm and found impairments in many participants with stroke compared to a healthy cohort (Bourke et al, J NeuroEngineering Rehabil 12: 7, 2015). However, a striking feature was the large range and skewed distribution of healthy performance. This likely reflects the use of different strategies across the healthy control sample, making it difficult to identify impairments. Here, we developed an intuitive “Unload Task”. We hypothesized this task would reduce healthy performance variability and improve the detection of impairment following stroke. Methods Performance on the Load and Unload Task in the KINARM exoskeleton robot was directly compared for healthy control (n = 107) and stroke (n = 31) participants. The goal was to keep a cursor representing the hand inside a virtual target and return “quickly and accurately” if the robot applied (or removed) an unexpected load to the arm (0.5–1.5 Nm). Several kinematic parameters quantified performance. Impairment was defined as performance outside the 95% of controls (corrected for age, sex and handedness). Task Scores were calculated using standardized parameter scores reflecting overall task performance. Results The distribution of healthy control performance was smaller and less skewed for the Unload Task compared to the Load Task. Fewer task outliers (outside 99.9 percentile for controls) were removed from the Unload Task (3.7%) compared to the Load Task (7.4%) when developing normative models of performance. Critically, more participants with stroke failed the Unload Task based on Task Score with their affected arm (68%) compared to the Load Task (23%). More impairments were found at the parameter level for the Unload (median = 52%) compared to Load Task (median = 29%). Conclusions The Unload Task provides an improved approach to assess fast corrective responses of the arm. We found that corrective responses are impaired in persons living with stroke, often equally in both arms. Impairments in generating rapid motor corrections may place individuals at greater risk of falls when they move and interact in the environment.
Collapse
Affiliation(s)
- Catherine R Lowrey
- Laboratory of Integrative Motor Behaviour, Centre for Neuroscience Studies, Queen's University, 18 Stuart St, Kingston, ON, K7L 3N6, Canada.
| | - Teige C Bourke
- Laboratory of Integrative Motor Behaviour, Centre for Neuroscience Studies, Queen's University, 18 Stuart St, Kingston, ON, K7L 3N6, Canada.,Present Address: Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Stephen D Bagg
- Department of Physical Medicine and Rehabilitation, Queen's University, Kingston, ON, Canada.,School of Medicine, Queen's University, Kingston, ON, Canada
| | - Sean P Dukelow
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Stephen H Scott
- Laboratory of Integrative Motor Behaviour, Centre for Neuroscience Studies, Queen's University, 18 Stuart St, Kingston, ON, K7L 3N6, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| |
Collapse
|
40
|
Callejón-Leblic B, Pereira-Vega A, Vázquez-Gandullo E, Sánchez-Ramos JL, Gómez-Ariza JL, García-Barrera T. Study of the metabolomic relationship between lung cancer and chronic obstructive pulmonary disease based on direct infusion mass spectrometry. Biochimie 2018; 157:111-122. [PMID: 30439409 DOI: 10.1016/j.biochi.2018.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 11/07/2018] [Indexed: 12/19/2022]
Abstract
The high prevalence of lung cancer (LC) has triggered the search of biomarkers for early diagnosis of this disease. For this purpose the study of metabolic changes related to the development of lung cancer could provide interesting information about its early diagnosis. In this sense, chronic obstructive pulmonary disease (COPD), a disease associated with tumor development, is a comorbidity that increases the risk of onset and progression of lung neoplasia and has also to be considered in the study of pathology related to lung cancer. This work develop a metabolomic approach based on direct infusion mass spectrometry using a hybrid triple quadrupole-time of flight mass spectrometer (DI-ESI-QqQ-TOF-MS) in order to identify altered metabolites from serum of LC and COPD patients and evaluate its relationship and implication in the progression of LC. This methodology has been applied to 30 serum samples from LC, 30 healthy patients used as controls (HC) and 30 serum samples from COPD to found altered metabolites from both LC and COPD diseases. In addition, some metabolic differences and similarities were found in Pulmonary Emphysema and Chronic Bronchitis patients. On the other hand, altered metabolites were studied in different stages of LC (II, III and IV) to evaluate the perturbation of them throughout the progression of disease. The sample treatment consisted of the extraction of polar and non-polar metabolites from serum that was later infused into the mass spectrometer using an electrospray ionization source in positive and negative mode. Partial least squares discriminant analysis (PLS-DA) allowed a classification between LC, HC and COPD groups in all acquisition modes. A total of 35 altered and common metabolites between LC and COPD, including amino acids, fatty acids, lysophospholipids, phospholipids and triacylglycerides were identified, being alanine, aspartate and glutamate metabolism the most altered. Finally, ROC curves were applied to the dataset and metabolites with AUC value higher than 0.70 were considered as relevant in the progression of LC.
Collapse
Affiliation(s)
- B Callejón-Leblic
- Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Campus El Carmen, Research Center on Health and Environment (RENSMA), 21007, Huelva, Spain
| | - A Pereira-Vega
- Pneumonology Area of Juan Ramón Jiménez Hospital, Huelva, Spain.
| | | | | | - J L Gómez-Ariza
- Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Campus El Carmen, Research Center on Health and Environment (RENSMA), 21007, Huelva, Spain
| | - T García-Barrera
- Department of Chemistry, Faculty of Experimental Sciences, University of Huelva, Campus El Carmen, Research Center on Health and Environment (RENSMA), 21007, Huelva, Spain.
| |
Collapse
|
41
|
Metabolome-based biomarkers: their potential role in the early detection of lung cancer. Contemp Oncol (Pozn) 2018; 22:135-140. [PMID: 30455584 PMCID: PMC6238086 DOI: 10.5114/wo.2018.78942] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 07/21/2018] [Indexed: 02/06/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, and a major problem affecting its mortality is the late diagnosis of the majority of cases, where treatment options are limited and overall prognosis is very bad. Currently, a low-dose computed tomography (LD-CT) screening in the high-risk group is the only available diagnostic strategy that could reduce mortality due to this malignancy. However, the LD-CT screening test suffers from a high false positive rate. Hence, complementation of LD-CT examination with blood-based biomarkers is a rational approach to increase efficacy and reduce the cost of early lung cancer screening programs. Several molecular signatures that discriminate between patients with early lung cancer and healthy individuals have been proposed in recent years, which are based on components of serum/plasma metabolome. However, none of these signatures has been validated by independent studies based on material collected during real lung cancer screening. Therefore, the validation of the real diagnostic value of these otherwise promising candidates remains a critical step in this challenging field of cancer diagnostics.
Collapse
|
42
|
Hanash SM, Ostrin EJ, Fahrmann JF. Blood based biomarkers beyond genomics for lung cancer screening. Transl Lung Cancer Res 2018; 7:327-335. [PMID: 30050770 DOI: 10.21037/tlcr.2018.05.13] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
While there is considerable interest at the present time in the development of so-called liquid biopsy approaches for cancer detection based notably on circulating tumor DNA, there are other types of potential biomarkers that show promise for lung cancer screening and early detection. Here we review approaches and some of the promising markers based on proteomics, metabolomics and the immune response to tumor antigens in the form of autoantibodies.
Collapse
Affiliation(s)
- Samir M Hanash
- Department of Clinical Cancer Prevention, MD Anderson Cancer Center, Houston, TX, USA
| | - Edwin Justin Ostrin
- Department of Pulmonary Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Johannes F Fahrmann
- Department of Clinical Cancer Prevention, MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
43
|
Chen Y, Ma Z, Zhong J, Li L, Min L, Xu L, Li H, Zhang J, Wu W, Dai L. Simultaneous quantification of serum monounsaturated and polyunsaturated phosphatidylcholines as potential biomarkers for diagnosing non-small cell lung cancer. Sci Rep 2018; 8:7137. [PMID: 29740076 PMCID: PMC5940703 DOI: 10.1038/s41598-018-25552-z] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 04/24/2018] [Indexed: 12/21/2022] Open
Abstract
Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. In this study, we investigated Ultrahigh Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry and Gas Chromatography Time-of-Flight/Mass Spectrometry-based non-targeted metabolomic profiles of serum samples obtained from early-stage NSCLC patients and healthy controls (HC). Metabolic pathways and the biological relevance of potential biomarkers were extensively studied to gain insights into dysregulated metabolism in NSCLC. The identified biomarker candidates were further externally validated via a targeted metabolomics analysis. The global metabolomics profiles could clearly distinguish NSCLC patients from HC. Phosphatidylcholine (PC) levels were found to be dysregulated in glycerophospholipid (GPL) metabolism, which was the top altered pathway in early-stage NSCLC. Compared with those in HC, significant increases in the levels of saturated and monounsaturated PCs such as PC (15:0/18:1), PC (18:0/16:0) and PC (18:0/20:1) were observed in NSCLC. Additionally, relative to those in HC, the levels of 9 polyunsaturated PCs, namely, PC (17:2/2:0), PC (18:4/3:0), and PC (15:0/18:2), and so on were significantly decreased in NSCLC patients. A panel of 12 altered PCs had good diagnostic performance in differentiating early-stage NSCLC patients from HC, and these PCs may thus be used as serum biomarkers for the early diagnosis of NSCLC.
Collapse
Affiliation(s)
- Yingrong Chen
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Zhihong Ma
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Jing Zhong
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Liqin Li
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Lishan Min
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Limin Xu
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Hongwei Li
- Cardiothoracic Surgery, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Jianbin Zhang
- Cardiothoracic Surgery, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Wei Wu
- Internal Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China
| | - Licheng Dai
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou, 313000, P.R. China.
| |
Collapse
|
44
|
Affiliation(s)
- Avrum Spira
- 1 Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts
| | - Balazs Halmos
- 2 Department of Oncology, Albert Einstein College of Medicine, Bronx, New York; and
| | - Charles A Powell
- 3 Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| |
Collapse
|
45
|
Hoda MA, Rozsas A, Lang E, Klikovits T, Lohinai Z, Torok S, Berta J, Bendek M, Berger W, Hegedus B, Klepetko W, Renyi-Vamos F, Grusch M, Dome B, Laszlo V. High circulating activin A level is associated with tumor progression and predicts poor prognosis in lung adenocarcinoma. Oncotarget 2017; 7:13388-99. [PMID: 26950277 PMCID: PMC4924649 DOI: 10.18632/oncotarget.7796] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 02/09/2016] [Indexed: 12/22/2022] Open
Abstract
Activin A (ActA)/follistatin (FST) signaling has been shown to be deregulated in different tumor types including lung adenocarcinoma (LADC). Here, we report that serum ActA protein levels are significantly elevated in LADC patients (n=64) as compared to controls (n=46, p=0.015). ActA levels also correlated with more advanced disease stage (p<0.0001) and T (p=0.0035) and N (p=0.0002) factors. M1 patients had significantly higher ActA levels than M0 patients (p<0.001). High serum ActA level was associated with poor overall survival (p<0.0001) and was confirmed as an independent prognostic factor (p=0.004). Serum FST levels were increased only in female LADC patients (vs. female controls, p=0.031). Two out of five LADC cell lines secreted biologically active ActA, while FST was produced in all of them. Transcripts of both type I and II ActA receptors were detected in all five LADC cell lines. In conclusion, our study does not only suggest that measuring blood ActA levels in LADC patients might improve the prediction of prognosis, but also indicates that this parameter might be a novel non-invasive biomarker for identifying LADC patients with organ metastases.
Collapse
Affiliation(s)
- Mir Alireza Hoda
- Translational Thoracic Oncology Laboratory, Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria.,Institute of Cancer Research, Department of Medicine I, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| | - Anita Rozsas
- Translational Thoracic Oncology Laboratory, Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria.,National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Elisabeth Lang
- Institute of Cancer Research, Department of Medicine I, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| | - Thomas Klikovits
- Translational Thoracic Oncology Laboratory, Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| | - Zoltan Lohinai
- National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Szilvia Torok
- National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Judit Berta
- National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Matyas Bendek
- National Koranyi Institute of Pulmonology, Budapest, Hungary
| | - Walter Berger
- Institute of Cancer Research, Department of Medicine I, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| | - Balazs Hegedus
- Translational Thoracic Oncology Laboratory, Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria.,MTA-SE Molecular Oncology Research Group, Hungarian Academy of Sciences, Budapest, Hungary
| | - Walter Klepetko
- Translational Thoracic Oncology Laboratory, Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| | - Ferenc Renyi-Vamos
- Department of Thoracic Surgery, National Institute of Oncology and Semmelweis University, Budapest, Hungary
| | - Michael Grusch
- Institute of Cancer Research, Department of Medicine I, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| | - Balazs Dome
- Translational Thoracic Oncology Laboratory, Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria.,National Koranyi Institute of Pulmonology, Budapest, Hungary.,Department of Thoracic Surgery, National Institute of Oncology and Semmelweis University, Budapest, Hungary.,Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Viktoria Laszlo
- Translational Thoracic Oncology Laboratory, Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center Vienna, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
46
|
Yu L, Li K, Zhang X. Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review. Oncotarget 2017; 8:115774-115786. [PMID: 29383200 PMCID: PMC5777812 DOI: 10.18632/oncotarget.22404] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 09/21/2017] [Indexed: 02/05/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death. Next-generation metabolomics is becoming a powerful emerging technology for studying the systems biology and chemistry of health and disease. This mini review summarized the main platforms of next-generation metabolomics and its main applications in lung cancer including early diagnosis, pathogenesis, classifications and precision medicine. The period covers between 2009 and August, 2017. The major issues and future directions of metabolomics in lung cancer research and clinical applications were also discussed.
Collapse
Affiliation(s)
- Li Yu
- Department of Oncology, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China
| | - Kefeng Li
- School of Medicine, University of California San Diego, San Diego, CA, USA
| | - Xiaoye Zhang
- Department of Oncology, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China
| |
Collapse
|
47
|
Trainor PJ, DeFilippis AP, Rai SN. Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics. Metabolites 2017. [PMID: 28635678 PMCID: PMC5488001 DOI: 10.3390/metabo7020030] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k-NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k-NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.
Collapse
Affiliation(s)
- Patrick J Trainor
- Division of Cardiovascular Medicine, Department of Medicine, University of Louisville, 580 S. Preston St., Louisville, KY 40202, USA.
| | - Andrew P DeFilippis
- Division of Cardiovascular Medicine, Department of Medicine, University of Louisville, 580 S. Preston St., Louisville, KY 40202, USA.
| | - Shesh N Rai
- Department of Bioinformatics and Biostatistics, University of Louisville, 505 S. Hancock St., Louisville, KY 40202, USA.
| |
Collapse
|
48
|
Fahrmann JF, Grapov DD, Wanichthanarak K, DeFelice BC, Salemi MR, Rom WN, Gandara DR, Phinney BS, Fiehn O, Pass H, Miyamoto S. Integrated Metabolomics and Proteomics Highlight Altered Nicotinamide- and Polyamine Pathways in Lung Adenocarcinoma. Carcinogenesis 2017; 38:271-280. [PMID: 28049629 PMCID: PMC5862279 DOI: 10.1093/carcin/bgw205] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 12/02/2016] [Accepted: 12/20/2016] [Indexed: 01/11/2023] Open
Abstract
Lung cancer is the leading cause of cancer mortality in the United States with non-small cell lung cancer (NSCLC) adenocarcinoma being the most common histological type. Early perturbations in cellular metabolism are a hallmark of cancer, but the extent of these changes in early stage lung adenocarcinoma remains largely unknown. In the current study, an integrated metabolomics and proteomics approach was utilized to characterize the biochemical and molecular alterations between malignant and matched control tissue from 27 subjects diagnosed with early stage lung adenocarcinoma. Differential analysis identified 71 metabolites and 1102 proteins that delineated tumor from control tissue. Integrated results indicated four major metabolic changes in early stage adenocarcinoma: (1) increased glycosylation and glutaminolysis; (2) elevated Nrf2 activation; (3) increase in nicotinic and nicotinamide salvaging pathways; and (4) elevated polyamine biosynthesis linked to differential regulation of the SAM/nicotinamide methyl-donor pathway. Genomic data from publicly available databases were included to strengthen proteomic findings. Our findings provide insight into the biochemical and molecular biological reprogramming that may accompanies early stage lung tumorigenesis and highlight potential therapeutic targets.
Collapse
Affiliation(s)
- Johannes F Fahrmann
- University of California, Davis, West Coast Metabolomics Center, Davis, California
| | | | | | - Brian C DeFelice
- University of California, Davis, West Coast Metabolomics Center, Davis, California
| | | | - William N Rom
- Division of Pulmonary, Critical Care, and Sleep, NYU School of Medicine, New York, NY, USA
| | - David R Gandara
- Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, California
| | | | - Oliver Fiehn
- University of California, Davis, West Coast Metabolomics Center, Davis, California
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia
| | - Harvey Pass
- Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Langone Medical Center, New York University, New York, NY, USA
| | - Suzanne Miyamoto
- Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, California
| |
Collapse
|
49
|
Klupczynska A, Dereziński P, Garrett TJ, Rubio VY, Dyszkiewicz W, Kasprzyk M, Kokot ZJ. Study of early stage non-small-cell lung cancer using Orbitrap-based global serum metabolomics. J Cancer Res Clin Oncol 2017; 143:649-659. [PMID: 28168355 PMCID: PMC5352735 DOI: 10.1007/s00432-017-2347-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Accepted: 01/18/2017] [Indexed: 01/06/2023]
Abstract
Purpose The aim of the project was to apply ultra-high-performance liquid chromatography–quadrupole-Orbitrap-high-resolution mass spectrometry for serum metabolite profiling of non-small-cell lung cancer (NSCLC). This Orbitrap-based methodology has been applied for a study of NSCLC potential markers for the first time. Methods After extraction using protein precipitation, sera were separated on the ACE Excel 2 C18-PFP (100 × 2.1 mm, 2.0 µm) column using gradient elution and analyzed within the range of 70–1000 m/z. Only patients with early stage disease (stages IA–IIB) were included in the study, providing opportunity to find biomarkers for early lung cancer detection. The resulting metabolite profiles were subjected to univariate and multivariate statistical tests. Results 36 features were found significantly changed between NSCLC group and controls after FDR adjustment and 19 were identified using various metabolite databases (in-house library, HMDB, mzCloud). The study revealed a number of NSCLC biomarker candidates which belong to such compound classes as acylcarnitines, organic acids, and amino acids. Multivariate ROC curve built using 12 identified metabolites was characterized by AUC = 0.836 (0.722–0.946). There were no significant differences in the serum metabolite profiles between two most common histological types of lung cancer—adenocarcinoma and squamous cell carcinoma. Conclusions Through identification of novel potential tumor markers, Orbitrap-based global metabolic profiling is a useful strategy in cancer research. Our study can accelerate development of new diagnostic and therapeutic strategies in NSCLC. The metabolites involved in discrimination between NSCLC patients and the control subjects should be further explored using a targeted approach. Electronic supplementary material The online version of this article (doi:10.1007/s00432-017-2347-0) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Agnieszka Klupczynska
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Grunwaldzka 6 Street, 60-780, Poznan, Poland
| | - Paweł Dereziński
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Grunwaldzka 6 Street, 60-780, Poznan, Poland
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, 1395 Center Drive, Gainesville, FL, 32610, USA
| | - Vanessa Y Rubio
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, 1395 Center Drive, Gainesville, FL, 32610, USA
| | - Wojciech Dyszkiewicz
- Department of Thoracic Surgery, Poznan University of Medical Sciences, Szamarzewskiego 62 Street, 60-569, Poznan, Poland
| | - Mariusz Kasprzyk
- Department of Thoracic Surgery, Poznan University of Medical Sciences, Szamarzewskiego 62 Street, 60-569, Poznan, Poland
| | - Zenon J Kokot
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Grunwaldzka 6 Street, 60-780, Poznan, Poland.
| |
Collapse
|
50
|
Fahrmann JF, Grapov D, DeFelice BC, Taylor S, Kim K, Kelly K, Wikoff WR, Pass H, Rom WN, Fiehn O, Miyamoto S. Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer. Cancer Biomark 2017; 16:609-17. [PMID: 27002763 DOI: 10.3233/cbm-160602] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable. OBJECTIVE Identify blood-based metabolite biomarkers for diagnosing lung cancer. MEHTODS Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29). RESULTS Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively. CONCLUSIONS This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.
Collapse
Affiliation(s)
| | | | - Brian C DeFelice
- University of California, Davis Genome Center, Davis, California, CA, USA
| | - Sandra Taylor
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California, CA, USA
| | - Kyoungmi Kim
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California, CA, USA
| | - Karen Kelly
- Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, CA, USA
| | - William R Wikoff
- University of California, Davis Genome Center, Davis, California, CA, USA
| | - Harvey Pass
- Division of Thoracic Surgery, Department of Cardiothoracic Surgery, Langone Medical Center, New York University, New York, NY, USA
| | - William N Rom
- Division of Pulmonary, Critical Care, and Sleep, NYU School of Medicine, New York, NY, USA
| | - Oliver Fiehn
- University of California, Davis Genome Center, Davis, California, CA, USA.,Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia
| | - Suzanne Miyamoto
- Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, CA, USA
| |
Collapse
|