Metabolomics for Diagnosis and Prognosis of Uterine Diseases? A Systematic Review

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.


Supplementary
Were handling and pre-analytical procedures reported in sufficient detail and similar for the whole group? If differences in procedures were reported, was their effect on the results assessed? Detailed description of pre-analytical procedures: temperature of storage, procedure of metabolite extraction.

6
Is the time between the reference standard and the index test short enough to guarantee that the target condition did not change between the two tests? Samples are usually obtained before or during surgery, which is considered a reference standard.
7 Did the whole sample or a random selection of the sample receive verification using a reference standard of diagnosis? 4 Supplementary Uterine fibroids (1) Heinonen 2017 tissue no yes no NC $ no* no yes yes NC yes *no clinical data, $ no information on daytime of sample acquisition, timing of sample processing, and freeze-thaw cycles Endometriosis (17) Vouk 2012 plasma yes no* yes yes yes no** yes yes yes NC # *HW, **Metabolite extraction, # no info on transformation, scaling, cross validation Dutta 2012 serum yes no* no** NC $ no*** no**** NC yes yes yes *HW. **time to centrifugation, ***fasting **rpm, $ no information on daytime of sample acquisition, timing of sample processing Jana 2013 serum yes yes no* NC $ no** yes NC NC yes yes *time to centrifugation,**type and stage of disease, $ no information on timing of sample processing Lee 2014 serum, PF, tissue yes yes NC NC $ no** yes NC yes NC # yes *tissue samples **BMI, $ no information on daytime of sample collection, # no info on sample randomization and QC samples Vicente-Munoz 2015 urine yes no* yes yes No** yes yes yes yes NC # *HW, **BMI, # no info on data transformation Vouk 2016 PF yes no* yes NC $ yes yes yes yes NC # NC ## *HW, $ no information on daytime of sample acquisition, and freeze-thaw cycles, # no info on sample randomization, ## no info on data transformation and scaling Ghazi 2016 serum yes no* no** NC $ no*** yes NC yes yes NC # *HW, **rpm, ***no BMI, $ no information on timing of sample processing, # no info on sample-to-sample normalization, data transformation and scaling Vicente-Munoz 2016 plasma yes no* yes NC $ yes yes yes yes yes NC # *HW, $ no information on daytime of sample acquisition and freeze-thaw cycles, # no info on sample-to-sample normalization, data transformation Letsiou 2017 plasma yes NC yes NC $ NC* no yes yes no NC # Control patients with myoma, *fasting, $ no information on daytime of sample acquisition, timing of sample processing, and freeze-thaw cycles, # no info on sample-to-sample randomization Dominguez 2017 endometrial fluid yes no* yes NC $ yes yes yes no** yes yes *infertile patients excluded as controls, **not for controls, $ no information on daytime of sample acquisition and freeze-thaw cycles Chagovets 2017 tissue yes yes yes NC $ no* yes yes yes NC # NC ## *wrong info about ethnicity, $ no information on daytime of sample acquisition, # no info on sample randomization and QC samples, ## no info on data transformation and scaling Dutta 2018 tissue, serum yes no* no** NC $ yes yes yes NC yes NC # *HW, **tissue and serum not described (reference), $ no information on daytime of sample acquisition, timing of sample processing, and freezethaw cycles, # no info on data transformation and scaling Li 2018 (FP) tissue yes yes yes NC $ yes yes no* yes yes no or*3 months after surgery, $ no information on daytime of sample acqusition Li 2018 (RBE) tissue yes yes yes NC $ yes yes yes yes NC # no $ no information on daytime of sample acquisition, # no info on sample randomization    Figure S1: QUADOMICS scoring of the included studies for endometriosis, cervical cancer, and endometrial cancer separately. Proportion of studies with answers "yes", "no", or "not clear" to each of the selected signaling questions. Each signaling question is numbered on the left, and a short description of each question is given on the right. The detailed scoring is given in Supplementary Tables S2, S3, and S4.

6,7
Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

6
Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. 6 Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. Table 1 Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis). Table 2 Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. 7 Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

6
Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. 7; Fig. 3 Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies). 6, Figure  3 Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

RESULTS
Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

8-9
Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

9-12
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12).

12-13
Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

DISCUSSION
Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

13-15
Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).

15-16
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research.  For more information, visit: www.prisma-statement.org.