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Gjorgoska M, Taylor AE, Smrkolj Š, Rižner TL. Multi-Steroid Profiling and Machine Learning Reveal Androgens as Candidate Biomarkers for Endometrial Cancer Diagnosis: A Case-Control Study. Cancers (Basel) 2025; 17:1679. [PMID: 40427176 PMCID: PMC12110686 DOI: 10.3390/cancers17101679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2025] [Revised: 05/09/2025] [Accepted: 05/13/2025] [Indexed: 05/29/2025] Open
Abstract
OBJECTIVE To evaluate the diagnostic and prognostic potential of preoperative serum steroid levels in endometrial cancer (EC) alone and in combination with clinical parameters and biomarkers CA-125 and HE4. METHODS This single-center observational study included 62 patients with EC and 70 controls with benign uterine conditions who underwent surgery between June 2012 and February 2020. Preoperative serum levels of classic androgens, 11-oxyandrogens, glucocorticoids and mineralocorticoids were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Machine learning was used to assess their diagnostic and prognostic value alone and combined with clinical parameters and tumor biomarkers. RESULTS Patients with EC had significantly higher serum levels of classic androgens (androstenedione, testosterone), 11-oxyandrogens (11β-hydroxy-androstenedione, 11β-hydroxy-testosterone) and glucocorticoids (17α-hydroxy-progesterone, 11-deoxycortisol) compared to controls. While individual steroids had limited diagnostic value, a multivariate model including classic androgens, CA-125, HE4, BMI and parity achieved an AUC 0.87, 79.1% sensitivity and 74.7% specificity in distinguishing EC from benign uterine condition. This model outperformed our previously published model based on CA-125, HE4 and BMI (AUC: 0.81, p < 0.0001). Prognostically, HE4 was the strongest marker for lymphovascular space invasion (LVSI) (AUC: 0.79) and deep myometrial invasion (MI) (AUC: 0.71). Among steroids, androstenedione was the most predictive of LVSI (AUC: 0.67), while 11β-hydroxy-testosterone was the strongest predictor of deep MI (AUC: 0.64). CONCLUSIONS Patients with EC exhibit distinct steroid hormone profiles. While steroids alone offer modest diagnostic and prognostic value, integrating them into multivariate models improves diagnostic accuracy.
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Affiliation(s)
- Marija Gjorgoska
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Angela E. Taylor
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, UK;
| | - Špela Smrkolj
- Department of Gynecology and Obstetrics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia;
- Division of Gynecology and Obstetrics, University Medical Centre, 1000 Ljubljana, Slovenia
| | - Tea Lanišnik Rižner
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia;
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Luzarraga Aznar A, Canton R, Loren G, Carvajal J, de la Calle I, Masferrer-Ferragutcasas C, Serra F, Bebia V, Bonaldo G, Angeles MA, Cabrera S, Palomar N, Vilarmau C, Martí M, Rigau M, Colas E, Gil-Moreno A. Current challenges and emerging tools in endometrial cancer diagnosis. Int J Gynecol Cancer 2025; 35:100056. [PMID: 40011116 DOI: 10.1016/j.ijgc.2024.100056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Revised: 12/03/2024] [Accepted: 12/07/2024] [Indexed: 02/28/2025] Open
Abstract
The diagnostic process of endometrial cancer includes imaging methods such as trans-vaginal ultrasound, along with procedures to obtain endometrial tissue for histologic evaluation. Common techniques for tissue sampling include Pipelle endometrial biopsy, hysteroscopy, and dilation and curettage, which are used to confirm the diagnosis, determine tumor histology, grade, and molecular profile. However, diagnostic algorithms for endometrial cancer differ significantly across countries, influenced by local resources, protocols, and the availability of diagnostic methods. These variations include differences in the endometrial thickness threshold for recommending a biopsy and the choice of the initial diagnostic test. Moreover, patients often have multiple tests and appointments before a definitive diagnosis, although only 5%-10% of women with post-menopausal bleeding are diagnosed with endometrial cancer. Current diagnostic techniques have limitations. Pipelle endometrial biopsy has a significant false-negative rate (10%-20%) and may fail to provide adequate diagnostic material in up to 30% of cases. Hysteroscopy, while useful, is associated with pain in up to 65% of patients and can delay diagnosis because of limited availability. Dilation and curettage is an invasive procedure requiring general anesthesia and has a higher complication rate. In response to these challenges, there is growing interest in developing new diagnostic tools that are less invasive and provide 1-step diagnoses, including liquid biopsies from urine, blood, cervico-vaginal and endometrial fluid samples by means of genomics and proteomics. This review will examine the current diagnostic algorithms in European and American guidelines, evaluate the sensitivity, specificity, and accuracy of current techniques, and explore new diagnostic tools under development.
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Affiliation(s)
- Ana Luzarraga Aznar
- Vall d'Hebron University Hospital, Department of Gynecologic Oncology, Barcelona, Spain
| | - Roger Canton
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain
| | - Guillem Loren
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain
| | - Javier Carvajal
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain
| | - Irene de la Calle
- Universitat Autònoma de Barcelona, Vall d'Hebron Institute of Research, Biomedical Research Group in Gynecology, CIBERONC, Barcelona, Spain
| | - Carina Masferrer-Ferragutcasas
- Universitat Autònoma de Barcelona, Vall d'Hebron Institute of Research, Biomedical Research Group in Gynecology, CIBERONC, Barcelona, Spain
| | - Francesc Serra
- Universitat Autònoma de Barcelona, Vall d'Hebron Institute of Research, Biomedical Research Group in Gynecology, CIBERONC, Barcelona, Spain
| | - Vicente Bebia
- Vall d'Hebron University Hospital, Department of Gynecologic Oncology, Barcelona, Spain; Universitat Autònoma de Barcelona, Vall d'Hebron Institute of Research, Biomedical Research Group in Gynecology, CIBERONC, Barcelona, Spain
| | - Giulio Bonaldo
- Vall d'Hebron University Hospital, Department of Gynecologic Oncology, Barcelona, Spain
| | - Martina Aida Angeles
- Vall d'Hebron University Hospital, Department of Gynecologic Oncology, Barcelona, Spain; Universitat Autònoma de Barcelona, Vall d'Hebron Institute of Research, Biomedical Research Group in Gynecology, CIBERONC, Barcelona, Spain
| | | | - Núria Palomar
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain
| | - Cristina Vilarmau
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain
| | - Maria Martí
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain
| | - Marina Rigau
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain
| | - Eva Colas
- MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain; Universitat Autònoma de Barcelona, Vall d'Hebron Institute of Research, Biomedical Research Group in Gynecology, CIBERONC, Barcelona, Spain
| | - Antonio Gil-Moreno
- Vall d'Hebron University Hospital, Department of Gynecologic Oncology, Barcelona, Spain; MiMARK Diagnostics SL, Parc Científic de Barcelona, Barcelona, Spain; Universitat Autònoma de Barcelona, Vall d'Hebron Institute of Research, Biomedical Research Group in Gynecology, CIBERONC, Barcelona, Spain.
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Mohd Sahardi NFN, Priya M, Makpol S, Shafiee MN. Clinical translation of metabolomics markers in endometrial carcinoma. J Obstet Gynaecol Res 2025; 51:e16246. [PMID: 40015330 DOI: 10.1111/jog.16246] [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: 09/10/2024] [Accepted: 02/09/2025] [Indexed: 03/01/2025]
Abstract
OBJECTIVE This comprehensive review highlights the current research on metabolomics and the metabolic pathways involved in endometrial cancer (EC), offering potential non-invasive biomarkers for EC. METHODS The data was extracted from published manuscripts between 2015 and 2024 using the reputed search engine "Pubmed." All gathered data were organized into a single table, facilitating a comparison with earlier findings. RESULTS The results of this study revealed most metabolites identified in previous metabolomic research on EC are associated with lipid, glucose, and amino acid metabolism. CONCLUSION Therefore, understanding these metabolic pathway alterations in EC is crucial for improving diagnosis, prognosis, and treatments by specially targeting these metabolic pathways.
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Affiliation(s)
| | - Manishaa Priya
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Suzana Makpol
- Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Mohamad Nasir Shafiee
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Švecová M, Blahová L, Kostolný J, Birková A, Urdzík P, Mareková M, Dubayová K. Enhancing endometrial cancer detection: Blood serum intrinsic fluorescence data processing and machine learning application. Talanta 2025; 283:127083. [PMID: 39471720 DOI: 10.1016/j.talanta.2024.127083] [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: 06/11/2024] [Revised: 10/03/2024] [Accepted: 10/19/2024] [Indexed: 11/01/2024]
Abstract
Endometrial cancer (EC) is the most prevalent cancer within the female reproductive system in developed countries. Despite its high incidence, there is currently no established laboratory screening test for EC, making early detection challenging. This study introduces an innovative, minimally invasive, and cost-effective method utilizing three-dimensional fluorescence analysis combined with machine learning algorithms to enhance early EC detection. Intrinsic fluorescence of blood serum samples was measured using a luminescence spectrophotometer, which captured fluorescence spectra as synchronous excitation spectra and visualized them through wavelength contour matrices. The spectral data were processed using machine learning algorithms, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), along with exploratory techniques such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). Fluorescence ratios R300/330 and R360/490, indicative of altered tryptophan metabolism and redox state changes, were identified as fluorescent spectral markers and represent key metabolic biomarkers. These ratios demonstrated high diagnostic efficacy with AUC values of 0.88 and 0.91, respectively. Among the ML algorithms, LR and RF exhibited high sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), showing significant promise for clinical application. After optimization, LR achieved a sensitivity of 0.94, specificity of 0.89, and an impressive AUC value of 0.94. The application of this novel approach in laboratory diagnostics has the potential to significantly enhance early detection and improve prognosis for EC patients.
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Affiliation(s)
- Monika Švecová
- Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Tr. SNP 1, 040 01, Košice, Slovakia
| | - Linda Blahová
- Department of Informatics, Faculty of Management Sciences and Informatics, University of Žilina, Univerzitná 8215/1, 010 26, Žilina, Slovakia
| | - Jozef Kostolný
- Department of Informatics, Faculty of Management Sciences and Informatics, University of Žilina, Univerzitná 8215/1, 010 26, Žilina, Slovakia
| | - Anna Birková
- Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Tr. SNP 1, 040 01, Košice, Slovakia
| | - Peter Urdzík
- Department of Gynaecology and Obstetrics, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Tr. SNP 1, 040 01, Košice, Slovakia
| | - Mária Mareková
- Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Tr. SNP 1, 040 01, Košice, Slovakia
| | - Katarína Dubayová
- Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University in Košice, Tr. SNP 1, 040 01, Košice, Slovakia.
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5
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Lorentzen GM, Łaniewski P, Cui H, Mahnert ND, Mourad J, Borst MP, Willmott L, Chase DM, Roe DJ, Herbst-Kralovetz MM. Cervicovaginal Metabolome and Tumor Characteristics for Endometrial Cancer Detection and Risk Stratification. Clin Cancer Res 2024; 30:3073-3087. [PMID: 38687603 PMCID: PMC11247321 DOI: 10.1158/1078-0432.ccr-23-2934] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/02/2024] [Accepted: 04/26/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE Endometrial cancer is highly prevalent and lacking noninvasive diagnostic techniques. Diagnosis depends on histological investigation of biopsy samples. Serum biomarkers for endometrial cancer have lacked sensitivity and specificity. The objective of this study was to investigate the cervicovaginal environment to improve the understanding of metabolic reprogramming related to endometrial cancer and identify potential biomarker candidates for noninvasive diagnostic and prognostic tests. EXPERIMENTAL DESIGN Cervicovaginal lavages were collected from 192 participants with endometrial cancer (n = 66) and non-malignant conditions (n = 108), and global untargeted metabolomics was performed. Using the metabolite data (n = 920), we completed a multivariate biomarker discovery analysis. RESULTS We analyzed grade 1/2 endometrioid carcinoma (n = 53) and other endometrial cancer subtypes (n = 13) to identify shared and unique metabolic signatures between the subtypes. When compared to non-malignant conditions, downregulation of proline (P < 0.0001), tryptophan (P < 0.0001), and glutamate (P < 0.0001) was found among both endometrial cancer groups, relating to key hallmarks of cancer including immune suppression and redox balance. Upregulation (q < 0.05) of sphingolipids, fatty acids, and glycerophospholipids was observed in endometrial cancer in a type-specific manner. Furthermore, cervicovaginal metabolites related to tumor characteristics, including tumor size and myometrial invasion. CONCLUSIONS Our findings provide insights into understanding the endometrial cancer metabolic landscape and improvement in diagnosis. The metabolic dysregulation described in this article linked specific metabolites and pathophysiological mechanisms including cellular proliferation, energy supply, and invasion of neighboring tissues. Furthermore, cervicovaginal metabolite levels related to tumor characteristics, which are used for risk stratification. Overall, development of noninvasive diagnostics can improve both the acceptability and accessibility of diagnosis.
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Affiliation(s)
- Georgia M. Lorentzen
- Department of Obstetrics and Gynecology, College of Medicine–Phoenix, University of Arizona, Phoenix, Arizona.
- Department of Biology & Biochemistry, University of Bath, Bath, United Kingdom.
| | - Paweł Łaniewski
- Department of Basic Medical Sciences, College of Medicine–Phoenix, University of Arizona, Phoenix, Arizona.
| | - Haiyan Cui
- UA Cancer Center, University of Arizona, Tucson, Arizona.
| | - Nichole D. Mahnert
- Department of Obstetrics and Gynecology, College of Medicine–Phoenix, University of Arizona, Phoenix, Arizona.
- Banner–University Medical Center, Phoenix, Arizona.
| | - Jamal Mourad
- Department of Obstetrics and Gynecology, College of Medicine–Phoenix, University of Arizona, Phoenix, Arizona.
- Banner–University Medical Center, Phoenix, Arizona.
| | - Matthew P. Borst
- Department of Obstetrics and Gynecology, College of Medicine–Phoenix, University of Arizona, Phoenix, Arizona.
- Banner–University Medical Center, Phoenix, Arizona.
| | | | | | - Denise J. Roe
- UA Cancer Center, University of Arizona, Tucson, Arizona.
- Department of Epidemiology & Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona.
| | - Melissa M. Herbst-Kralovetz
- Department of Obstetrics and Gynecology, College of Medicine–Phoenix, University of Arizona, Phoenix, Arizona.
- Department of Basic Medical Sciences, College of Medicine–Phoenix, University of Arizona, Phoenix, Arizona.
- UA Cancer Center, University of Arizona, Tucson, Arizona.
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Kwinten KJJ, Lemain VA, de Hullu JA, Leenders WPJ, Steenbeek MP, van Altena AM, Pijnenborg JMA. Cervicovaginal specimen biomarkers for early detection of ovarian and endometrial cancer: A review. Cancer Med 2024; 13:e70000. [PMID: 39031958 PMCID: PMC11259558 DOI: 10.1002/cam4.70000] [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: 03/11/2024] [Revised: 06/17/2024] [Accepted: 06/30/2024] [Indexed: 07/22/2024] Open
Abstract
BACKGROUND In the last decade, technical innovations have resulted in the development of several minimally invasive diagnostic cancer tools. Within women at high risk of developing ovarian or endometrial cancer (EC) due to hereditary cancer syndrome, there is an urgent need for minimally invasive and patient-friendly methods to detect ovarian cancer and EC at an early stage. MATERIALS AND METHODS We performed a systematic search of studies using DNA methylation or mutation analysis, microbiome, or proteomics performed on cervicovaginal specimens (smear, swab, or tampon) intended to detect ovarian and EC published until January 2024. RESULTS Included studies (n = 36) showed high heterogeneity in terms of biomarkers used and outcomes, and only a few studies reported on the detection of biomarkers in high-risk subgroups. CONCLUSION Based on the findings in this review, DNA methylation techniques seem to be the most promising for detecting ovarian and EC at early stages in the general population. Future validation of cervicovaginal DNA methylation techniques is needed to determine whether this technique might be beneficial in hereditary high-risk subgroups.
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Affiliation(s)
- Kevin J. J. Kwinten
- Department of Obstetrics and GynecologyRadboud University Medical CenterNijmegenThe Netherlands
- Department of Obstetrics and GynecologyCanisius Wilhelmina HospitalNijmegenThe Netherlands
| | - Victor A. Lemain
- Department of Obstetrics and GynecologyRadboud University Medical CenterNijmegenThe Netherlands
| | - Joanne A. de Hullu
- Department of Obstetrics and GynecologyRadboud University Medical CenterNijmegenThe Netherlands
| | | | - Miranda P. Steenbeek
- Department of Obstetrics and GynecologyRadboud University Medical CenterNijmegenThe Netherlands
- Department of Obstetrics and GynecologyCatharina Hospital EindhovenEindhovenThe Netherlands
| | - Anne M. van Altena
- Department of Obstetrics and GynecologyRadboud University Medical CenterNijmegenThe Netherlands
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López-López Á, López-Gonzálvez Á, Barbas C. Metabolomics for searching validated biomarkers in cancer studies: a decade in review. Expert Rev Mol Diagn 2024; 24:601-626. [PMID: 38904089 DOI: 10.1080/14737159.2024.2368603] [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: 12/27/2023] [Accepted: 06/12/2024] [Indexed: 06/22/2024]
Abstract
INTRODUCTION In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways. AREAS COVERED This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types. EXPERT OPINION Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.
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Affiliation(s)
- Ángeles López-López
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Madrid, Spain
| | - Ángeles López-Gonzálvez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Madrid, Spain
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Albertí-Valls M, Megino-Luque C, Macià A, Gatius S, Matias-Guiu X, Eritja N. Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review. Cancers (Basel) 2023; 16:185. [PMID: 38201612 PMCID: PMC10778161 DOI: 10.3390/cancers16010185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/22/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.
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Affiliation(s)
- Manel Albertí-Valls
- Oncologic Pathology Group, Biomedical Research Institute of Lleida (IRBLleida), University of Lleida, Av. Rovira Roure 80, 25198 Lleida, Spain; (C.M.-L.); (A.M.); (S.G.); (X.M.-G.)
| | - Cristina Megino-Luque
- Oncologic Pathology Group, Biomedical Research Institute of Lleida (IRBLleida), University of Lleida, Av. Rovira Roure 80, 25198 Lleida, Spain; (C.M.-L.); (A.M.); (S.G.); (X.M.-G.)
- Department of Medicine, Division of Hematology and Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Anna Macià
- Oncologic Pathology Group, Biomedical Research Institute of Lleida (IRBLleida), University of Lleida, Av. Rovira Roure 80, 25198 Lleida, Spain; (C.M.-L.); (A.M.); (S.G.); (X.M.-G.)
| | - Sònia Gatius
- Oncologic Pathology Group, Biomedical Research Institute of Lleida (IRBLleida), University of Lleida, Av. Rovira Roure 80, 25198 Lleida, Spain; (C.M.-L.); (A.M.); (S.G.); (X.M.-G.)
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC)
| | - Xavier Matias-Guiu
- Oncologic Pathology Group, Biomedical Research Institute of Lleida (IRBLleida), University of Lleida, Av. Rovira Roure 80, 25198 Lleida, Spain; (C.M.-L.); (A.M.); (S.G.); (X.M.-G.)
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC)
- Laboratory of Precision Medicine, Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), Department of Pathology, Hospital de Bellvitge, Gran via de l’Hospitalet 199, 08908 Barcelona, Spain
| | - Núria Eritja
- Oncologic Pathology Group, Biomedical Research Institute of Lleida (IRBLleida), University of Lleida, Av. Rovira Roure 80, 25198 Lleida, Spain; (C.M.-L.); (A.M.); (S.G.); (X.M.-G.)
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC)
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Chen J, Liu J, Cao D. Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry. BMC Womens Health 2023; 23:583. [PMID: 37940929 PMCID: PMC10634093 DOI: 10.1186/s12905-023-02730-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVE This study aimed to reveal the urine metabolic change of endometrial cancer (EC) patients during fertility-sparing treatment and establish non-invasive predictive models to identify patients with complete remission (CR). METHOD This study enrolled 20 EC patients prior to treatment (PT) and 22 patients with CR, aged 25-40 years. Eligibility criteria consisted of stage IA high-grade EC, lesions confined to endometrium, normal hepatic and renal function, normal urine test, no contraindication for fertility-sparing treatment and no prior therapy. Urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), a technique chosen for its high sensitivity and resolution, allows for rapid, accurate identification and quantification of metabolites, providing a comprehensive metabolic profile and facilitating the discovery of potential biomarkers. Analytical techniques were employed to determine distinct metabolites and altered metabolic pathways. The statistical analyses were performed using univariate and multivariate analyses, logistic regression and receiver operating characteristic (ROC) curves to discover and validate the potential biomarker models. RESULTS A total of 108 different urine metabolomes were identified between CR and PT groups. These metabolites were enriched in ascorbate and aldarate metabolism, one carbon pool by folate, and some amino acid metabolisms pathways. A panel consisting of Baicalin, 5beta-1,3,7 (11)-Eudesmatrien-8-one, Indolylacryloylglycine, Edulitine, and Physapubenolide were selected as biomarkers, which demonstrated the best predictive ability with the AUC values of 0.982/0.851 in training/10-fold-cross-validation group, achieving a sensitivity of 0.975 and specificity of 0.967, respectively. CONCLUSION The urine metabolic analysis revealed the metabolic changes in EC patients during the fertility-sparing treatment. The predictive biomarkers present great potential diagnostic value in fertility-sparing treatments for EC patients, offering a less invasive means of monitoring treatment efficacy. Further research should explore the mechanistic underpinnings of these metabolic changes and validate the biomarker panel in larger, diverse populations due to the small sample size and single-institution nature of our study.
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Affiliation(s)
- Junyu Chen
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, China
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, National Clinical Research Center for Obstetric & Gynecologic Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Jiale Liu
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, China
| | - Dongyan Cao
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, National Clinical Research Center for Obstetric & Gynecologic Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
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10
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Piedimonte S, Rosa G, Gerstl B, Sopocado M, Coronel A, Lleno S, Vicus D. Evaluating the use of machine learning in endometrial cancer: a systematic review. Int J Gynecol Cancer 2023; 33:1383-1393. [PMID: 37666535 DOI: 10.1136/ijgc-2023-004622] [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: 09/06/2023] Open
Abstract
OBJECTIVE To review the literature on machine learning in endometrial cancer, report the most commonly used algorithms, and compare performance with traditional prediction models. METHODS This is a systematic review of the literature from January 1985 to March 2021 on the use of machine learning in endometrial cancer. An extensive search of electronic databases was conducted. Four independent reviewers screened studies initially by title then full text. Quality was assessed using the MINORS (Methodological Index for Non-Randomized Studies) criteria. P values were derived using the Pearson's Χ2 test in JMP 15.0. RESULTS Among 4295 articles screened, 30 studies on machine learning in endometrial cancer were included. The most frequent applications were in patient datasets (33.3%, n=10), pre-operative diagnostics (30%, n=9), genomics (23.3%, n=7), and serum biomarkers (13.3%, n=4). The most commonly used models were neural networks (n=10, 33.3%) and support vector machine (n=6, 20%).The number of publications on machine learning in endometrial cancer increased from 1 in 2010 to 29 in 2021.Eight studies compared machine learning with traditional statistics. Among patient dataset studies, two machine learning models (20%) performed similarly to logistic regression (accuracy: 0.85 vs 0.82, p=0.16). Machine learning algorithms performed similarly to detect endometrial cancer based on MRI (accuracy: 0.87 vs 0.82, p=0.24) while outperforming traditional methods in predicting extra-uterine disease in one serum biomarker study (accuracy: 0.81 vs 0.61). For survival outcomes, one study compared machine learning with Kaplan-Meier and reported no difference in concordance index (83.8% vs 83.1%). CONCLUSION Although machine learning is an innovative and emerging technology, performance is similar to that of traditional regression models in endometrial cancer. More studies are needed to assess its role in endometrial cancer. PROSPERO REGISTRATION NUMBER CRD42021269565.
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Affiliation(s)
- Sabrina Piedimonte
- Department of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada
| | | | - Brigitte Gerstl
- The Rosa Institute, Sydney, New South Wales, Australia
- The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Mars Sopocado
- The Rosa Institute, Sydney, New South Wales, Australia
| | - Ana Coronel
- The Rosa Institute, Sydney, New South Wales, Australia
| | | | - Danielle Vicus
- Department of Gynecologic Oncology, University of Toronto, Toronto, Ontario, Canada
- Department of Gynecologic Oncology, Sunnybrook Health Sciences, Toronto, Ontario, Canada
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11
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Godlewski A, Czajkowski M, Mojsak P, Pienkowski T, Gosk W, Lyson T, Mariak Z, Reszec J, Kondraciuk M, Kaminski K, Kretowski M, Moniuszko M, Kretowski A, Ciborowski M. A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors. Sci Rep 2023; 13:11044. [PMID: 37422554 PMCID: PMC10329700 DOI: 10.1038/s41598-023-38243-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023] Open
Abstract
Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.
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Affiliation(s)
- Adrian Godlewski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Marcin Czajkowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Patrycja Mojsak
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Pienkowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Wioleta Gosk
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Lyson
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Zenon Mariak
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Joanna Reszec
- Department of Medical Pathomorphology, Medical University of Bialystok, Białystok, Poland
| | - Marcin Kondraciuk
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Karol Kaminski
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Marek Kretowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Marcin Moniuszko
- Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Białystok, Poland
- Department of Allergology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland.
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12
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Wang W, Rong Z, Wang G, Hou Y, Yang F, Qiu M. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomark Res 2023; 11:66. [PMID: 37391812 PMCID: PMC10311880 DOI: 10.1186/s40364-023-00507-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/27/2023] [Indexed: 07/02/2023] Open
Abstract
Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.
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Affiliation(s)
- Wenxiang Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China
| | - Zhiwei Rong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Guangxi Wang
- Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Yan Hou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Clinical Research Center, Peking University, Beijing, 100191, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
| | - Mantang Qiu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
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13
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Yagin FH, Alkhateeb A, Colak C, Azzeh M, Yagin B, Rueda L. A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients. Metabolites 2023; 13:metabo13050589. [PMID: 37233630 DOI: 10.3390/metabo13050589] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 04/20/2023] [Accepted: 04/21/2023] [Indexed: 05/27/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700-0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94-1).
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Affiliation(s)
- Fatma Hilal Yagin
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey
| | - Abedalrhman Alkhateeb
- Software Engineering Department, King Hussein School of Computing Science, Princess Sumaya University for Technology, Amman P.O. Box 1438, Jordan
| | - Cemil Colak
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey
| | - Mohammad Azzeh
- Data Science Department, King Hussein School of Computing Science, Princess Sumaya University for Technology, Amman P.O. Box 1438, Jordan
| | - Burak Yagin
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey
| | - Luis Rueda
- School of Computer Science, University of Windsor, Windsor, ON N9B 3P4, Canada
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14
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Romano A, Rižner TL, Werner HMJ, Semczuk A, Lowy C, Schröder C, Griesbeck A, Adamski J, Fishman D, Tokarz J. Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review. Front Oncol 2023; 13:1120178. [PMID: 37091170 PMCID: PMC10118013 DOI: 10.3389/fonc.2023.1120178] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/06/2023] [Indexed: 04/09/2023] Open
Abstract
Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.
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Affiliation(s)
- Andrea Romano
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Tea Lanišnik Rižner
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- *Correspondence: Andrea Romano, ; Tea Lanišnik Rižner,
| | - Henrica Maria Johanna Werner
- Department of Gynaecology, Maastricht University Medical Centre (MUMC), Maastricht, Netherlands
- GROW – School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Andrzej Semczuk
- Department of Gynaecology, Lublin Medical University, Lublin, Poland
| | | | | | | | - Jerzy Adamski
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Dmytro Fishman
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Quretec Ltd., Tartu, Estonia
| | - Janina Tokarz
- Institute for Diabetes and Cancer, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
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15
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DNA Methylation of Window of Implantation Genes in Cervical Secretions Predicts Ongoing Pregnancy in Infertility Treatment. Int J Mol Sci 2023; 24:ijms24065598. [PMID: 36982674 PMCID: PMC10051225 DOI: 10.3390/ijms24065598] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/28/2023] [Accepted: 03/04/2023] [Indexed: 03/17/2023] Open
Abstract
Window of implantation (WOI) genes have been comprehensively identified at the single cell level. DNA methylation changes in cervical secretions are associated with in vitro fertilization embryo transfer (IVF-ET) outcomes. Using a machine learning (ML) approach, we aimed to determine which methylation changes in WOI genes from cervical secretions best predict ongoing pregnancy during embryo transfer. A total of 2708 promoter probes were extracted from mid-secretory phase cervical secretion methylomic profiles for 158 WOI genes, and 152 differentially methylated probes (DMPs) were selected. Fifteen DMPs in 14 genes (BMP2, CTSA, DEFB1, GRN, MTF1, SERPINE1, SERPINE2, SFRP1, STAT3, TAGLN2, TCF4, THBS1, ZBTB20, ZNF292) were identified as the most relevant to ongoing pregnancy status. These 15 DMPs yielded accuracy rates of 83.53%, 85.26%, 85.78%, and 76.44%, and areas under the receiver operating characteristic curves (AUCs) of 0.90, 0.91, 0.89, and 0.86 for prediction by random forest (RF), naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (KNN), respectively. SERPINE1, SERPINE2, and TAGLN2 maintained their methylation difference trends in an independent set of cervical secretion samples, resulting in accuracy rates of 71.46%, 80.06%, 80.72%, and 80.68%, and AUCs of 0.79, 0.84, 0.83, and 0.82 for prediction by RF, NB, SVM, and KNN, respectively. Our findings demonstrate that methylation changes in WOI genes detected noninvasively from cervical secretions are potential markers for predicting IVF-ET outcomes. Further studies of cervical secretion of DNA methylation markers may provide a novel approach for precision embryo transfer.
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16
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Galal A, Talal M, Moustafa A. Applications of machine learning in metabolomics: Disease modeling and classification. Front Genet 2022; 13:1017340. [PMID: 36506316 PMCID: PMC9730048 DOI: 10.3389/fgene.2022.1017340] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.
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Affiliation(s)
- Aya Galal
- Systems Genomics Laboratory, American University in Cairo, New Cairo, Egypt,Institute of Global Health and Human Ecology, American University in Cairo, New Cairo, Egypt
| | - Marwa Talal
- Systems Genomics Laboratory, American University in Cairo, New Cairo, Egypt,Biotechnology Graduate Program, American University in Cairo, New Cairo, Egypt
| | - Ahmed Moustafa
- Systems Genomics Laboratory, American University in Cairo, New Cairo, Egypt,Biotechnology Graduate Program, American University in Cairo, New Cairo, Egypt,Department of Biology, American University in Cairo, New Cairo, Egypt,*Correspondence: Ahmed Moustafa,
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17
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Research on the Guiding Effect of CTCs on Postoperative Adjuvant Therapy for Patients with Early Stage Endometrial Cancer. JOURNAL OF ONCOLOGY 2022; 2022:4327977. [PMID: 35685426 PMCID: PMC9174000 DOI: 10.1155/2022/4327977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/17/2022] [Indexed: 11/17/2022]
Abstract
Endometrial tumor has increased in occurrence and fatality in China during the last 11 years, owing to inconsistent hormone use and modifications in people living surrounding and lifestyles. One of the three main gynaecological tumors is endometrial carcinoma (EC). Longer waiting duration of operation was linked to a lower chance of sustainability in endometrial tumor patients. Despite the great sustainability rate of endometrial tumor, only around 46 percent of patients undergo adjuvant treatment. Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and circulating free DNA (cfDNA) are the most investigated tumor noninvasive indicators. These circulating biomarkers are important in the knowledge of metastasis and tumorigenesis, and they could help researchers comprehend how cancer dynamics evolve throughout the therapy and illness development. In patients with solid tumor, the existence of circulating tumor cells (CTCs) in the peripheral blood is linked to a weak prognosis. However, there is a scarcity of information on how to detect CTCs in endometrial cancer (EC). Hence, in this paper, we analyze the guiding effect of CTCs on postoperative adjuvant treatment for sufferers with initial phase endometrial tumor using multi-cox regression method. The dataset is selected and the blood samples are collected using plasma separation method. The CTC is detected using differential diagnosis. The morphology and biological features, Immunocytochemistry, Genomic analysis, Transcriptomic analysis, Proteomic analysis, and molecular analysis are performed and the outcomes are evaluated.
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18
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Yi R, Xie L, Wang X, Shen C, Chen X, Qiao L. Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer. Front Oncol 2022; 12:861142. [PMID: 35574395 PMCID: PMC9099206 DOI: 10.3389/fonc.2022.861142] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/01/2022] [Indexed: 11/15/2022] Open
Abstract
Background Endometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics. Methods Forty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples. Results By integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples. Conclusions With multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.
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Affiliation(s)
- Runqiu Yi
- Department of Chemistry, Shanghai Stomatological Hospital, and Obstetrics and Gynecology Hospital of Fudan University, Fudan University, Shanghai, China
| | - Liying Xie
- Department of Chemistry, Shanghai Stomatological Hospital, and Obstetrics and Gynecology Hospital of Fudan University, Fudan University, Shanghai, China
| | | | | | - Xiaojun Chen
- Department of Chemistry, Shanghai Stomatological Hospital, and Obstetrics and Gynecology Hospital of Fudan University, Fudan University, Shanghai, China
- *Correspondence: Liang Qiao, ; Xiaojun Chen,
| | - Liang Qiao
- Department of Chemistry, Shanghai Stomatological Hospital, and Obstetrics and Gynecology Hospital of Fudan University, Fudan University, Shanghai, China
- *Correspondence: Liang Qiao, ; Xiaojun Chen,
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19
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Li J, Zhang Q, Zhao J, Zhang H, Chen W. Streptococcus mutans and Candida albicans Biofilm Inhibitors Produced by Lactiplantibacillus plantarum CCFM8724. Curr Microbiol 2022; 79:143. [PMID: 35325333 DOI: 10.1007/s00284-022-02833-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/10/2022] [Indexed: 12/24/2022]
Abstract
Lactiplantibacillus plantarum CCFM8724 inhibits the growth of Streptococcus mutans and Candida albicans in mixed-species biofilm formation. In this study, bioactive compound including cyclo (leu-pro), cyclo (phe-pro), and some organic acids, such as 3-phenyllactic acid, hydrocinnamic acid, and palmitic acid, were identified through GC-MS analysis. At 50 μg·mL-1, cyclo (leu-pro) reduced biofilm mass (OD600) from 3.00 to 2.00, and hydrocinnamic acid at 25 μg·mL-1 reduced biofilm mass (OD600) from 3.00 to 1.00. The expression of ALS3 and HWP1 was downregulated by cyclo (leu-pro). Furthermore, a mixture of cyclo (leu-pro), cyclo (phe-pro), 3-phenyllactic acid, hydrocinnamic acid, and palmitic acid, had anti-biofilm activity. Overall, the results provide promising baseline information for the potential use of this probiotic and its components in preventing biofilm formation.
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Affiliation(s)
- Jiaxun Li
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Qiuxiang Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China. .,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China. .,(Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, 225004, China.
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Wei Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, 214122, Jiangsu, China
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20
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Holcakova J, Bartosik M, Anton M, Minar L, Hausnerova J, Bednarikova M, Weinberger V, Hrstka R. New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers. Cancers (Basel) 2021; 13:6339. [PMID: 34944963 PMCID: PMC8699592 DOI: 10.3390/cancers13246339] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/07/2021] [Accepted: 12/14/2021] [Indexed: 12/24/2022] Open
Abstract
The prevention and early diagnostics of precancerous stages are key aspects of contemporary oncology. In cervical cancer, well-organized screening and vaccination programs, especially in developed countries, are responsible for the dramatic decline of invasive cancer incidence and mortality. Cytological screening has a long and successful history, and the ongoing implementation of HPV triage with increased sensitivity can further decrease mortality. On the other hand, endometrial and ovarian cancers are characterized by a poor accessibility to specimen collection, which represents a major complication for early diagnostics. Therefore, despite relatively promising data from evaluating the combined effects of genetic variants, population screening does not exist, and the implementation of new biomarkers is, thus, necessary. The introduction of various circulating biomarkers is of potential interest due to the considerable heterogeneity of cancer, as highlighted in this review, which focuses exclusively on the most common tumors of the genital tract, namely, cervical, endometrial, and ovarian cancers. However, it is clearly shown that these malignancies represent different entities that evolve in different ways, and it is therefore necessary to use different methods for their diagnosis and treatment.
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Affiliation(s)
- Jitka Holcakova
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, 656 53 Brno, Czech Republic; (J.H.); (M.B.)
| | - Martin Bartosik
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, 656 53 Brno, Czech Republic; (J.H.); (M.B.)
| | - Milan Anton
- Department of Obstetrics and Gynecology, Masaryk University and University Hospital, 625 00 Brno, Czech Republic; (M.A.); (L.M.)
| | - Lubos Minar
- Department of Obstetrics and Gynecology, Masaryk University and University Hospital, 625 00 Brno, Czech Republic; (M.A.); (L.M.)
| | - Jitka Hausnerova
- Department of Pathology, Masaryk University and University Hospital, 625 00 Brno, Czech Republic;
| | - Marketa Bednarikova
- Department of Internal Medicine, Hematology and Oncology, Masaryk University and University Hospital, 625 00 Brno, Czech Republic;
| | - Vit Weinberger
- Department of Obstetrics and Gynecology, Masaryk University and University Hospital, 625 00 Brno, Czech Republic; (M.A.); (L.M.)
| | - Roman Hrstka
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, 656 53 Brno, Czech Republic; (J.H.); (M.B.)
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21
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Grading of endometrial cancer using 1H HR-MAS NMR-based metabolomics. Sci Rep 2021; 11:18160. [PMID: 34518615 PMCID: PMC8438077 DOI: 10.1038/s41598-021-97505-y] [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: 04/07/2021] [Accepted: 08/26/2021] [Indexed: 11/09/2022] Open
Abstract
The tissue metabolomic characteristics associated with endometrial cancer (EC) at different grades were studied using high resolution (400 MHz) magic angle spinning (HR-MAS) proton spectroscopy. The metabolic profiles were obtained from 64 patients (14 with grade 1 (G1), 33 with grade 2 (G2) and 17 with grade 3 (G3) tumors) and compared with the profile acquired from 10 patients with the benign disorders. OPLS-DA revealed increased valine, isoleucine, leucine, hypotaurine, serine, lysine, ethanolamine, choline and decreased creatine, creatinine, glutathione, ascorbate, glutamate, phosphoethanolamine and scyllo-inositol in all EC grades in reference to the non-transformed tissue. The increased levels of taurine was additionally detected in the G1 and G2 tumors in comparison to the control tissue, while the elevated glycine, N-acetyl compound and lactate—in the G1 and G3 tumors. The metabolic features typical for the G1 tumors are the increased dimethyl sulfone, phosphocholine, and decreased glycerophosphocholine and glutamine levels, while the decreased myo-inositol level is characteristic for the G2 and G3 tumors. The elevated 3-hydroxybutyrate, alanine and betaine levels were observed in the G3 tumors. The differences between the grade G1 and G3 malignances were mainly related to the perturbations of phosphoethanolamine and phosphocholine biosynthesis, inositol, betaine, serine and glycine metabolism. The statistical significance of the OPLS-DA modeling was also verified by an univariate analysis. HR-MAS NMR based metabolomics provides an useful insight into the metabolic reprogramming in endometrial cancer.
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22
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Pietkiewicz D, Klupczynska-Gabryszak A, Plewa S, Misiura M, Horala A, Miltyk W, Nowak-Markwitz E, Kokot ZJ, Matysiak J. Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review. Pharmaceuticals (Basel) 2021; 14:ph14080731. [PMID: 34451829 PMCID: PMC8400482 DOI: 10.3390/ph14080731] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 02/06/2023] Open
Abstract
Gynecological and breast cancers still remain a significant health problem worldwide. Diagnostic methods are not sensitive and specific enough to detect the disease at an early stage. During carcinogenesis and tumor progression, the cellular need for DNA and protein synthesis increases leading to changes in the levels of amino acids. An important role of amino acids in many biological pathways, including biosynthesis of proteins, nucleic acids, enzymes, etc., which serve as an energy source and maintain redox balance, has been highlighted in many research articles. The aim of this review is a detailed analysis of the literature on metabolomic studies of gynecology and breast cancers with particular emphasis on alterations in free amino acid profiles. The work includes a brief overview of the metabolomic methodology and types of biological samples used in the studies. Special attention was paid to the possible role of selected amino acids in the carcinogenesis, especially proline and amino acids related to its metabolism. There is a clear need for further research and multiple external validation studies to establish the role of amino acid profiling in diagnosing gynecological and breast cancers.
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Affiliation(s)
- Dagmara Pietkiewicz
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, 60-780 Poznan, Poland; (D.P.); (A.K.-G.); (S.P.)
| | - Agnieszka Klupczynska-Gabryszak
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, 60-780 Poznan, Poland; (D.P.); (A.K.-G.); (S.P.)
| | - Szymon Plewa
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, 60-780 Poznan, Poland; (D.P.); (A.K.-G.); (S.P.)
| | - Magdalena Misiura
- Department of Analysis and Bioanalysis of Medicines, Medical University of Bialystok, 15-089 Bialystok, Poland; (M.M.); (W.M.)
| | - Agnieszka Horala
- Gynecologic Oncology Department, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (A.H.); (E.N.-M.)
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Medical University of Bialystok, 15-089 Bialystok, Poland; (M.M.); (W.M.)
| | - Ewa Nowak-Markwitz
- Gynecologic Oncology Department, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (A.H.); (E.N.-M.)
| | - Zenon J. Kokot
- Faculty of Health Sciences, Calisia University, 62-800 Kalisz, Poland;
| | - Jan Matysiak
- Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, 60-780 Poznan, Poland; (D.P.); (A.K.-G.); (S.P.)
- Correspondence:
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23
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A Crosstalk- and Interferent-Free Dual Electrode Amperometric Biosensor for the Simultaneous Determination of Choline and Phosphocholine. SENSORS 2021; 21:s21103545. [PMID: 34069690 PMCID: PMC8160789 DOI: 10.3390/s21103545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 05/14/2021] [Indexed: 11/22/2022]
Abstract
Choline (Ch) and phosphocholine (PCh) levels in tissues are associated to tissue growth and so to carcinogenesis. Till now, only highly sophisticated and expensive techniques like those based on NMR spectroscopy or GC/LC- high resolution mass spectrometry permitted Ch and PCh analysis but very few of them were capable of a simultaneous determination of these analytes. Thus, a never reported before amperometric biosensor for PCh analysis based on choline oxidase and alkaline phosphatase co-immobilized onto a Pt electrode by co-crosslinking has been developed. Coupling the developed biosensor with a parallel sensor but specific to Ch, a crosstalk-free dual electrode biosensor was also developed, permitting the simultaneous determination of Ch and PCh in flow injection analysis. This novel sensing device performed remarkably in terms of sensitivity, linear range, and limit of detection so to exceed in most cases the more complex analytical instrumentations. Further, electrode modification by overoxidized polypyrrole permitted the development of a fouling- and interferent-free dual electrode biosensor which appeared promising for the simultaneous determination of Ch and PCh in a real sample.
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24
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Njoku K, Campbell AE, Geary B, MacKintosh ML, Derbyshire AE, Kitson SJ, Sivalingam VN, Pierce A, Whetton AD, Crosbie EJ. Metabolomic Biomarkers for the Detection of Obesity-Driven Endometrial Cancer. Cancers (Basel) 2021; 13:cancers13040718. [PMID: 33578729 PMCID: PMC7916512 DOI: 10.3390/cancers13040718] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/23/2021] [Accepted: 02/06/2021] [Indexed: 12/15/2022] Open
Abstract
Simple Summary Endometrial cancer is the commonest cancer of the female genital tract and obesity is its main modifiable risk factor. Over 80% of endometrial cancers develop in the context of obesity-induced metabolic changes. This study focuses on the potential of plasma-based metabolites to enable the early detection of endometrial cancer in a cohort of women with body mass index (BMI) ≥ 30 kg/m2. Specific lipid metabolites including phospholipids and sphingolipids (sphingomyelins) demonstrated good accuracy for the detection of endometrial cancer, especially when combined in a diagnostic model. This study advances our knowledge of the role of metabolomics in endometrial cancer and provides a basis for the minimally invasive screening of women with elevated BMI. Abstract Endometrial cancer is the most common malignancy of the female genital tract and a major cause of morbidity and mortality in women. Early detection is key to ensuring good outcomes but a lack of minimally invasive screening tools is a significant barrier. Most endometrial cancers are obesity-driven and develop in the context of severe metabolomic dysfunction. Blood-derived metabolites may therefore provide clinically relevant biomarkers for endometrial cancer detection. In this study, we analysed plasma samples of women with body mass index (BMI) ≥ 30 kg/m2 and endometrioid endometrial cancer (cases, n = 67) or histologically normal endometrium (controls, n = 69), using a mass spectrometry-based metabolomics approach. Eighty percent of the samples were randomly selected to serve as a training set and the remaining 20% were used to qualify test performance. Robust predictive models (AUC > 0.9) for endometrial cancer detection based on artificial intelligence algorithms were developed and validated. Phospholipids were of significance as biomarkers of endometrial cancer, with sphingolipids (sphingomyelins) discriminatory in post-menopausal women. An algorithm combining the top ten performing metabolites showed 92.6% prediction accuracy (AUC of 0.95) for endometrial cancer detection. These results suggest that a simple blood test could enable the early detection of endometrial cancer and provide the basis for a minimally invasive screening tool for women with a BMI ≥ 30 kg/m2.
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Affiliation(s)
- Kelechi Njoku
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK; (K.N.); (M.L.M.); (A.E.D.); (S.J.K.); (V.N.S.)
- Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (A.E.C.); (B.G.)
| | - Amy E. Campbell
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (A.E.C.); (B.G.)
| | - Bethany Geary
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (A.E.C.); (B.G.)
| | - Michelle L. MacKintosh
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK; (K.N.); (M.L.M.); (A.E.D.); (S.J.K.); (V.N.S.)
- Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
| | - Abigail E. Derbyshire
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK; (K.N.); (M.L.M.); (A.E.D.); (S.J.K.); (V.N.S.)
- Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
| | - Sarah J. Kitson
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK; (K.N.); (M.L.M.); (A.E.D.); (S.J.K.); (V.N.S.)
- Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
| | - Vanitha N. Sivalingam
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK; (K.N.); (M.L.M.); (A.E.D.); (S.J.K.); (V.N.S.)
- Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
| | - Andrew Pierce
- Wolfson Molecular Imaging Centre, Division of Cancer Sciences, University of Manchester, Palatine Road, Manchester M20 3LJ, UK;
| | - Anthony D. Whetton
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (A.E.C.); (B.G.)
- Wolfson Molecular Imaging Centre, Division of Cancer Sciences, University of Manchester, Palatine Road, Manchester M20 3LJ, UK;
- Correspondence: (A.D.W.); (E.J.C.); Tel.: +44-161-275-0038 (A.D.W.); +44-161-701-6942 (E.J.C.)
| | - Emma J. Crosbie
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK; (K.N.); (M.L.M.); (A.E.D.); (S.J.K.); (V.N.S.)
- Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
- Correspondence: (A.D.W.); (E.J.C.); Tel.: +44-161-275-0038 (A.D.W.); +44-161-701-6942 (E.J.C.)
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Tokarz J, Adamski J, Lanišnik Rižner T. Metabolomics for Diagnosis and Prognosis of Uterine Diseases? A Systematic Review. J Pers Med 2020; 10:294. [PMID: 33371433 PMCID: PMC7767462 DOI: 10.3390/jpm10040294] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/08/2020] [Accepted: 12/18/2020] [Indexed: 12/24/2022] Open
Abstract
This systematic review analyses the contribution of metabolomics to the identification of diagnostic and prognostic biomarkers for uterine diseases. These diseases are diagnosed invasively, which entails delayed treatment and a worse clinical outcome. New options for diagnosis and prognosis are needed. PubMed, OVID, and Scopus were searched for research papers on metabolomics in physiological fluids and tissues from patients with uterine diseases. The search identified 484 records. Based on inclusion and exclusion criteria, 44 studies were included into the review. Relevant data were extracted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist and quality was assessed using the QUADOMICS tool. The selected metabolomics studies analysed plasma, serum, urine, peritoneal, endometrial, and cervico-vaginal fluid, ectopic/eutopic endometrium, and cervical tissue. In endometriosis, diagnostic models discriminated patients from healthy and infertile controls. In cervical cancer, diagnostic algorithms discriminated patients from controls, patients with good/bad prognosis, and with/without response to chemotherapy. In endometrial cancer, several models stratified patients from controls and recurrent from non-recurrent patients. Metabolomics is valuable for constructing diagnostic models. However, the majority of studies were in the discovery phase and require additional research to select reliable biomarkers for validation and translation into clinical practice. This review identifies bottlenecks that currently prevent the translation of these findings into clinical practice.
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Affiliation(s)
- Janina Tokarz
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, German Research Centre for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (J.T.); (J.A.)
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, German Research Centre for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; (J.T.); (J.A.)
- German Centre for Diabetes Research, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, 85764 Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
| | - Tea Lanišnik Rižner
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
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Králíčková M, Vetvicka V, Laganà AS. Endometrial cancer-is our knowledge changing? Transl Cancer Res 2020; 9:7734-7745. [PMID: 35117376 PMCID: PMC8798081 DOI: 10.21037/tcr-20-1720] [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: 03/29/2020] [Accepted: 04/29/2020] [Indexed: 11/27/2022]
Abstract
In developed countries, endometrial cancer (EC) is the most frequent gynecologic malignancy in postmenopausal women. At the same time, EC has become one of the most common cancers in numerous developing countries, probably influenced by global epidemic of obesity. The majority of patients have low-grade endometrioid cancer with a high 5-year survival rate, but with high-risk EC, the survival rates are still rather low. However, despite intensive research in last decades, our knowledge of the mechanisms, risk factors, diagnosis and treatment have not significantly improved. The standard treatment of all types of EC is still a traditional combination of surgery, irradiation and/or chemotherapy, despite the fact that each of these options is not without having some negative side effects. Despite the fact that on the molecular level, EC is relatively well-studied, but the efforts to transform these findings into either diagnosis or therapies of EC remain elusive. In addition, some research into risk factors involved in the development or progression of EC seems to be more a fishing expedition than a well thought-out approach. The purpose of this review is to summarize the most recent developments in the search for biomarkers and prognostic markers and to discuss the progress in EC treatment.
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Affiliation(s)
- Milena Králíčková
- Department of Histology and Embryology, Faculty of Medicine, Charles University, Karlovarska 48, Plzen, Czech Republic.,Department of Obstetrics and Gynecology, University Hospital, Faculty of Medicine, Charles University, Alej Svobody 80, Plzen, Czech Republic.,Biomedical Centre, Faculty of Medicine in Plzen, Charles University, Plzen, Czech Republic
| | - Vaclav Vetvicka
- Department of Pathology, University of Louisville, Louisville, KY, USA
| | - Antonio Simone Laganà
- Department of Obstetrics and Gynecology, "Filippo Del Ponte" Hospital, University of Insubria, Piazza Biroldi 1, Varese, Italy
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27
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Njoku K, Sutton CJ, Whetton AD, Crosbie EJ. Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer. Metabolites 2020; 10:E314. [PMID: 32751940 PMCID: PMC7463916 DOI: 10.3390/metabo10080314] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 12/24/2022] Open
Abstract
Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.
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Affiliation(s)
- Kelechi Njoku
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK;
- Stoller Biomarker Discovery Centre, Institute of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK;
| | - Caroline J.J Sutton
- School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester M13 9WL, UK;
| | - Anthony D. Whetton
- Stoller Biomarker Discovery Centre, Institute of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK;
| | - Emma J. Crosbie
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester M13 9WL, UK;
- Department of Obstetrics and Gynaecology, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
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28
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Pei C, Liu C, Wang Y, Cheng D, Li R, Shu W, Zhang C, Hu W, Jin A, Yang Y, Wan J. FeOOH@Metal-Organic Framework Core-Satellite Nanocomposites for the Serum Metabolic Fingerprinting of Gynecological Cancers. Angew Chem Int Ed Engl 2020; 59:10831-10835. [PMID: 32237260 DOI: 10.1002/anie.202001135] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/05/2020] [Indexed: 12/11/2022]
Abstract
High-throughput metabolic analysis is of significance in diagnostics, while tedious sample pretreatment has largely hindered its clinic application. Herein, we designed FeOOH@ZIF-8 composites with enhanced ionization efficiency and size-exclusion effect for laser desorption/ionization mass spectrometry (LDI-MS)-based metabolic diagnosis of gynecological cancers. The FeOOH@ZIF-8-assisted LDI-MS achieved rapid, sensitive, and selective metabolic fingerprints of the native serum without any enrichment or purification. Further analysis of extracted serum metabolic fingerprints successfully discriminated patients with gynecological cancers (GCs) from healthy controls and also differentiated three major subtypes of GCs. Given the low cost, high-throughput, and easy operation, our approach brings a new dimension to disease analysis and classification.
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Affiliation(s)
- Congcong Pei
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Chao Liu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - You Wang
- Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, Shanghai, 200001, P. R. China.,Department of Obstetrics and Gynecology, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200001, P. R. China
| | - Dan Cheng
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Rongxin Li
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Weikang Shu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Chaoqi Zhang
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Wenli Hu
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
| | - Aihua Jin
- Institute of Molecular Bioscience, The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Yannan Yang
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, P. R. China
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29
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Pei C, Liu C, Wang Y, Cheng D, Li R, Shu W, Zhang C, Hu W, Jin A, Yang Y, Wan J. FeOOH@Metal–Organic Framework Core–Satellite Nanocomposites for the Serum Metabolic Fingerprinting of Gynecological Cancers. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.202001135] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Congcong Pei
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - Chao Liu
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - You Wang
- Shanghai Key Laboratory of Gynecologic OncologyRenji Hospital Shanghai 200001 P. R. China
- Department of Obstetrics and GynecologySchool of MedicineShanghai Jiao Tong University Shanghai 200001 P. R. China
| | - Dan Cheng
- Australian Institute for Bioengineering and NanotechnologyThe University of Queensland Brisbane QLD 4072 Australia
| | - Rongxin Li
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - Weikang Shu
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - Chaoqi Zhang
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - Wenli Hu
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
| | - Aihua Jin
- Institute of Molecular BioscienceThe University of Queensland St Lucia Queensland 4072 Australia
| | - Yannan Yang
- Australian Institute for Bioengineering and NanotechnologyThe University of Queensland Brisbane QLD 4072 Australia
| | - Jingjing Wan
- School of Chemistry and Molecular EngineeringEast China Normal University Shanghai 200241 P. R. China
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30
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Bliziotis NG, Engelke UFH, Aspers RLEG, Engel J, Deinum J, Timmers HJLM, Wevers RA, Kluijtmans LAJ. A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics. Metabolomics 2020; 16:64. [PMID: 32358672 PMCID: PMC7196944 DOI: 10.1007/s11306-020-01686-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/23/2020] [Indexed: 12/15/2022]
Abstract
INTRODUCTION When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr-Purcell-Meiboom-Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares-Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation (< 30%) in replicate samples. CONCLUSION Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.
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Affiliation(s)
- Nikolaos G. Bliziotis
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Udo F. H. Engelke
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Ruud L. E. G. Aspers
- Institute for Molecules and Materials, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
| | - Jasper Engel
- Institute for Molecules and Materials, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
- Present Address: Biometris, Wageningen UR, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Jaap Deinum
- Department of Internal Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Henri J. L. M. Timmers
- Department of Internal Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Ron A. Wevers
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Leo A. J. Kluijtmans
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
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