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.

https://www.mdpi.com/2075-4426/10/4/294/pdf

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

Abstract As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics. 

/pdf/exploration-of-biomarkers-for-the-diagnosis-treatment-and-1zsyok7z.pdf

Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review

High-risk human papillomavirus (HR-HPV) is the main etiological factor for cervical cancer. Accumulating evidence has suggested the active role of metabolites in the initiation and progression of cancers. This study explored the plasma metabolic profiles of HPV-16 positive (HPV16 (+)), HPV-18 positive (HPV18 (+)), and HPV negative (CTL) individuals using a nontargeted metabolomics approach. C8 ceramide-1-Phosphate (d18 : 1/8 : 0) was found to inhibit cervical cancer cell proliferation and migration in vitro, evidenced by CCK8 experiments, a cell migration test, RT-qPCR, and western blotting. The underlying mechanism demonstrated that C8 inhibited proliferation and migration in cervical cancer cells via the MAPK/JNK1 signaling pathway. These findings may contribute to the clinical treatment of HR-HPV-induced cervical cancer by intervening in its initiation and progression.

Plasma-Based Metabolomics Profiling of High-Risk Human Papillomavirus and their Emerging Roles in the Progression of Cervical Cancer

Background Cervical cancer is the second most common female malignancy worldwide, and the prognosis of patients with the locally advanced stage is poor, with a high risk of recurrent. This study aimed to explore the effect factors that influence the overall survival (OS), progression-free survival (PFS), and quality of life in patients with stage III cervical cancer. Methods A two-way follow-up method was utilized to collect information from patients with stage III cervical cancer. Survival analyses were performed by the Kaplan-Meier method and Cox regression model. Multiple linear regression was used to analyze the factors related to quality of life. Results Four hundred and sixty subjects were enrolled in this study. The median follow-up time was 28 months (range, 1–51 months). The 3-year OS and PFS rates were 69.0% and 55.0%, respectively. Primiparous age above 30 years, age at diagnosis below 40 years, lymph node metastasis, non-squamous cell carcinoma, and larger tumors were risk factors of OS and PFS, while the protective factors were weight loss, good mental status, and standardized treatment. The quality of life was better in patients with the following characteristics: younger age at diagnosis, active review according to medical orders, and earlier return to society. Conclusions Primiparous age above 30 years was a poor prognostic factor for OS, PFS, and quality of life. The common factors relevant to prognosis were confirmed to be significant.

/pdf/prognosis-of-stage-iii-cervical-cancer-a-two-way-outcome-18scb64zjz.pdf

Prognosis of stage III cervical cancer: a two-way outcome study

Simple Summary Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response. Abstract Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

/pdf/exploring-metabolic-signatures-of-ex-vivo-tumor-tissue-3rropdw0.pdf

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Prognostic biomarkers of cervical squamous cell carcinoma identified via plasma metabolomics.

Background Cervical cancer is the second most common female malignancy worldwide. The main method to evaluate the effect of concurrent chemoradiotherapy (CCRT) in the locally advanced stage is imaging which cannot meet the clinical needs. This study aimed to explore potential cervical cancer biomarkers via plasma metabolomics and evaluate the effectiveness of CCRT and disease progression. Methods Twenty-four primary and thirty recurrent patients were enrolled between November 2016 and November 2017. Plasma samples were obtained by centrifugation of whole blood collected from enrolled patients at admission and from primary patients after CCRT. Plasma metabolic profiles were determined via ultra-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Multivariate analyses and public databases were used to screen and identify differential metabolites. Pathway analysis was conducted using MetaboAnalyst. Results Metabolic profiles obtained were significantly different among primary, post-CCRT-treated, and recurrent patients. Multivariate analyses showed that 37 metabolites differed significantly among the three groups, of which the levels of 22 metabolites changed significantly after CCRT and recovered or even exceeded the levels in primary patients when the tumor reappeared. These 22 metabolites were mainly lipids involved in sphingolipid and glycerophospholipid metabolism. Among them, 8 metabolites with area under curve values above 0.75 between each pair of groups exhibited great potential for evaluating CCRT effectiveness and disease progression. Conclusions Our results show significantly different plasma metabolic profiles among the three cervical cancer groups; 8 metabolites were identified as potential biomarkers to evaluate the effectiveness of CCRT and disease progression, which can help evaluate the prognosis and treatment of cervical cancer in a timely manner.

https://tcr.amegroups.com/article/viewFile/37900/pdf

Exploring metabolomics biomarkers for evaluating the effectiveness of concurrent radiochemotherapy for cervical cancers

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Prognostic biomarkers of cervical squamous cell carcinoma identified via plasma metabolomics.

Prognosis of stage III cervical cancer: a two-way outcome study

Exploring metabolomics biomarkers for evaluating the effectiveness of concurrent radiochemotherapy for cervical cancers