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

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.

Keywords: adenomyosis; algorithms; biomarker; biomarker discovery; cervical cancer; endometrial cancer; endometriosis; leiomyoma; omics; uterine fibroids.

Figure 1

Flow diagram of metabolomics approaches. Targeted and nontargeted metabolomics are both divided into pre-analytical, analytical, and post-analytical phases.

Figure 2

Study flow diagram.

Figure 3

QUADOMICS scoring of all studies included. 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–S4.

Figure 4

Speculative model of alterations in metabolic pathways in endometriosis, based on the identified biomarkers for endometriosis. Interactions between pathways and interactions between the endometrium tissue and the circulation were not experimentally validated in the studies included into this review. Arrows do not necessarily imply causal relationships.

Figure 5

Speculative model of alterations in metabolic pathways in endometrial cancer based on identified biomarkers for endometrial cancer. Interactions between pathways and interactions between the cancer tissue and the circulation were not experimentally validated in the studies included into this review. Arrows do not necessarily imply causal relationships.