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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: 4.3] [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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Liang D, Li M, Wei R, Wang J, Li Y, Jia W, Chen T. Strategy for Intercorrelation Identification between Metabolome and Microbiome. Anal Chem 2019; 91:14424-14432. [PMID: 31638380 DOI: 10.1021/acs.analchem.9b02948] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accumulating evidence points to the strong and complicated associations between the metabolome and the microbiome, which play diverse roles in physiology and pathology. Various correlation analysis approaches were applied to identify microbe-metabolite associations. Given the strengths and weaknesses of the existing methods and considering the characteristics of different types of omics data, we designed a special strategy, called Generalized coRrelation analysis for Metabolome and Microbiome (GRaMM), for the intercorrelation discovery between the metabolome and microbiome. GRaMM can properly deal with two types of omics data, the effect of confounders, and both linear and nonlinear correlations by integrating several complementary methods such as the classical linear regression, the emerging maximum information coefficient (MIC), the metabolic confounding effect elimination (MCEE), and the centered log-ratio transformation (CLR). GRaMM contains four sequential computational steps: (1) metabolic and microbial data preprocessing, (2) linear/nonlinear type identification, (3) data correction and correlation detection, and (4) p value correction. The performances of GRaMM, including the accuracy, sensitivity, specificity, false positive rate, applicability, and effects of preprocessing and confounder adjustment steps, were evaluated and compared with three other methods in multiple simulated and real-world datasets. To our knowledge, GRaMM is the first strategy designed for the intercorrelation analysis between metabolites and microbes. The Matlab function and an R package were developed and are freely available for academic use (comply with GNU GPL.V3 license).
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Affiliation(s)
- Dandan Liang
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine , Shanghai Jiao Tong University Affiliated Sixth People's Hospital , Shanghai 200233 , China
| | - Mengci Li
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine , Shanghai Jiao Tong University Affiliated Sixth People's Hospital , Shanghai 200233 , China.,School of Biomedical Engineering and Med-X Research Institute , Shanghai Jiao Tong University , Shanghai 200030 , China
| | - Runmin Wei
- University of Hawaii Cancer Center , 701 Ilalo Street , Honolulu , Hawaii 96813 , United States
| | - Jingye Wang
- University of Hawaii Cancer Center , 701 Ilalo Street , Honolulu , Hawaii 96813 , United States
| | - Yitao Li
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine , Shanghai Jiao Tong University Affiliated Sixth People's Hospital , Shanghai 200233 , China.,Hong Kong Traditional Chinese Medicine Phenome Research Centre, School of Chinese Medicine , Hong Kong Baptist University , Kowloon Tong , Hong Kong 999077 , China
| | - Wei Jia
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine , Shanghai Jiao Tong University Affiliated Sixth People's Hospital , Shanghai 200233 , China.,University of Hawaii Cancer Center , 701 Ilalo Street , Honolulu , Hawaii 96813 , United States.,Hong Kong Traditional Chinese Medicine Phenome Research Centre, School of Chinese Medicine , Hong Kong Baptist University , Kowloon Tong , Hong Kong 999077 , China
| | - Tianlu Chen
- Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine , Shanghai Jiao Tong University Affiliated Sixth People's Hospital , Shanghai 200233 , China
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