Targeted metabolomic profiling of low and high grade serous epithelial ovarian cancer tissues: a pilot study

Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy

In this work, the HR MAS NMR (high-resolution magic-angle spinning nuclear magnetic resonance) spectroscopy technique was combined with standard histological examinations to investigate the metabolic features of high-grade serous ovarian cancer (HGSOC) with a special focus on the relation between a metabolic profile and a cancer cell fraction. The studied group consisted of 44 patients with HGSOC and 18 patients with benign ovarian tumors. Normal ovarian tissue was also excised from 13 control patients. The metabolic profiles of 138 tissue specimens were acquired on a Bruker Avance III 400 MHz spectrometer. The NMR spectra of the HGSOC samples could be discriminated from those acquired from the non-transformed tissue and were shown to depend on tumor purity. The most important features that differentiate the samples with a high fraction of cancer cells from the samples containing mainly fibrotic stroma are the increased intensities in the spectral regions corresponding to phosphocholine/glycerophosphocholine, phosphoethanolamine/serine, threonine, uridine nucleotides and/or uridine diphosphate (UDP) nucleotide sugars. Higher levels of glutamine, glutamate, acetate, lysine, alanine, leucine and isoleucine were detected in the desmoplastic stroma within the HGSOC lesions compared to the stroma of benign tumors. The HR MAS NMR analysis of the metabolic composition of the epithelial and stromal compartments within HGSOC contributes to a better understanding of the disease's biology.

Triterpenoid ursolic acid regulates the environmental carcinogen benzo[a]pyrene-driven epigenetic and metabolic alterations in SKH-1 hairless mice for skin cancer interception

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental carcinogens accountable to developing skin cancers. Recently, we reported that exposure to benzo[a]pyrene (B[a]P), a common PAH, causes epigenetic and metabolic alterations in the initiation, promotion and progression of non-melanoma skin cancer (NMSC). As a follow-up investigation, this study examines how dietary triterpenoid ursolic acid (UA) regulates B[a]P-driven epigenetic and metabolic pathways in SKH-1 hairless mice. Our results show UA intercepts against B[a]P-induced tumorigenesis at different stages of NMSC. Epigenomic cytosines followed by guanine residues (CpG) methyl-seq data showed UA diminished B[a]P-mediated differentially methylated regions (DMRs) profiles. Transcriptomic RNA-seq revealed UA revoked B[a]P-induced differentially expressed genes (DEGs) of skin cancer-related genes, such as leucine-rich repeat LGI family member 2 (Lgi2) and kallikrein-related peptidase 13 (Klk13), indicating UA plays a vital role in B[a]P-mediated gene regulation and its potential consequences in NMSC interception. Association analysis of DEGs and DMRs found that the mRNA expression of KLK13 gene was correlated with the promoter CpG methylation status in the early-stage comparison group, indicating UA could regulate the KLK13 by modulating its promoter methylation at an early stage of NMSC. The metabolomic study showed UA alters B[a]P-regulated cancer-associated metabolisms like thiamin metabolism, ascorbate and aldarate metabolism during the initiation phase; pyruvate, citrate and thiamin metabolism during the promotion phase; and beta-alanine and pathothenate coenzyme A (CoA) biosynthesis during the late progression phase. Taken together, UA reverses B[a]P-driven epigenetic, transcriptomic and metabolic reprogramming, potentially contributing to the overall cancer interception against B[a]P-mediated NMSC.

The longitudinal biochemical profiling of TBI in a drop weight model of TBI

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic pathways and unique biochemical profiles associated with TBI. Herein, we employed a longitudinal metabolomics approach to study TBI in a weight drop mouse model to reveal metabolic changes associated with TBI pathogenesis, severity, and secondary injury. Using proton nuclear magnetic resonance (1H NMR) spectroscopy, we biochemically profiled post-mortem brain from mice that suffered mild TBI (N = 25; 13 male and 12 female), severe TBI (N = 24; 11 male and 13 female) and sham controls (N = 16; 11 male and 5 female) at baseline, day 1 and day 7 following the injury. 1H NMR-based metabolomics, in combination with bioinformatic analyses, highlights a few significant metabolites associated with TBI severity and perturbed metabolism related to the injury. We report that the concentrations of taurine, creatinine, adenine, dimethylamine, histidine, N-Acetyl aspartate, and glucose 1-phosphate are all associated with TBI severity. Longitudinal metabolic observation of brain tissue revealed that mild TBI and severe TBI lead distinct metabolic profile changes. A multi-class model was able to classify the severity of injury as well as time after TBI with estimated 86% accuracy. Further, we identified a high degree of correlation between respective hemisphere metabolic profiles (r > 0.84, p < 0.05, Pearson correlation). This study highlights the metabolic changes associated with underlying TBI severity and secondary injury. While comprehensive, future studies should investigate whether: (a) the biochemical pathways highlighted here are recapitulated in the brain of TBI sufferers and (b) if the panel of biomarkers are also as effective in less invasively harvested biomatrices, for objective and rapid identification of TBI severity and prognosis.

Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry

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.

Peripheral Blood Serum NMR Metabolomics Is a Powerful Tool to Discriminate Benign and Malignant Ovarian Tumors

Ovarian cancer is the major cause of death from gynecological cancer and the third most common gynecological malignancy worldwide. Despite a slight improvement in the overall survival of ovarian carcinoma patients in recent decades, the cure rate has not improved. This is mainly due to late diagnosis and resistance to therapy. It is therefore urgent to develop effective methods for early detection and prognosis. We hypothesized that, besides being able to distinguish serum samples of patients with ovarian cancer from those of patients with benign ovarian tumors, 1H-NMR metabolomics analysis might be able to predict the malignant potential of tumors. For this, serum 1H-NMR metabolomics analyses were performed, including patients with malignant, benign and borderline ovarian tumors. The serum metabolic profiles were analyzed by multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. A metabolic profile associated with ovarian malignant tumors was defined, in which lactate, 3-hydroxybutyrate and acetone were increased and acetate, histidine, valine and methanol were decreased. Our data support the use of 1H-NMR metabolomics analysis as a screening method for ovarian cancer detection and might be useful for predicting the malignant potential of borderline tumors.

Exposure to copper nanoparticles or copper sulfate dysregulated the hypothalamic-pituitary-gonadalaxis, gonadal histology, and metabolites in Pelteobagrus fulvidraco

This study evaluated the effects of chronic exposure to copper nanoparticles (Cu-NPs) and waterborne copper (CuSO₄) on the reproductive system of yellow catfish (Pelteobagrus fulvidraco). Juvenile yellow catfish were exposed to 100 and 200 μg Cu/L Cu-NPs and 100 μg Cu/L CuSO₄ for 42 days. The results showed clear reproductive defects in both female and male yellow catfish in the 200 μg Cu/L Cu-NPs and 100 μg Cu/L CuSO₄ groups. Exposure to Cu-NPs or CuSO₄ inhibited folliculogenesis and vitellogenesis in the ovaries, and spermatogenesis in the testes, accompanied by elevation of the apoptotic signal. Ultrastructural observations also revealed damaged organelles of gonadal cells in both testes and ovaries. Most of the hypothalamic-pituitary-gonadal (HPG) axis genes examined and serum sex steroid hormones tended to be downregulated after Cu exposure. Metabolomic analysis suggested that gonadal estradiol level is sensitive to Cu-NPs or CuSO₄. The heat map of gonadal metabolomics suggested a similar effect of 200 μg Cu/L Cu-NPs and 100 μg Cu/L CuSO4 in both the ovaries and testes. Additionally, metabolomics data showed that the reproductive toxicity due to Cu-NPs and CuSO₄ may occur via different metabolic pathways. Cu-NPs tend to dysregulate the metabolic pathways of sphingolipid and linoleic acid metabolism in the ovary and the biosynthesis of amino acids and pantothenate and CoA in the testis. Overall, these findings revealed the toxicological effects of Cu-NPs and CuSO₄ on the HPG axis and gonadal metabolism in yellow catfish.

Evaluation of BAYESIL for automated annotation of 1H NMR data using limited sample volumes: application to African elephant serum

INTRODUCTION

Technological advancements enabled the analyses of limited sample volumes on ¹H NMR. Manual spectral profiling of the data is, however, complex, and timely.

OBJECTIVE

To evaluate the performance of BAYESIL for automated identification and quantification of ¹H NMR spectra of limited volume samples.

METHOD

Aliquots of a pooled African elephant serum sample were analyzed using standard and reduced volumes. Performance was evaluated on confidence scores, non-detects and laboratory CV.

RESULTS

Of the 47 compounds detected, 28 had favorable performances. The approach could differentiate samples based on biological variation.

CONCLUSIONS

BAYESIL is valuable for limited sample ¹H NMR data analyses.

Metabolic rewiring in MYC-driven medulloblastoma by BET-bromodomain inhibition

Medulloblastoma (MB) is the most common malignant brain tumour in children. High-risk MB patients harbouring MYC amplification or overexpression exhibit a very poor prognosis. Aberrant activation of MYC markedly reprograms cell metabolism to sustain tumorigenesis, yet how metabolism is dysregulated in MYC-driven MB is not well understood. Growing evidence unveiled the potential of BET-bromodomain inhibitors (BETis) as next generation agents for treating MYC-driven MB, but whether and how BETis may affect tumour cell metabolism to exert their anticancer activities remains unknown. In this study, we explore the metabolic features characterising MYC-driven MB and examine how these are altered by BET-bromodomain inhibition. To this end, we employed an NMR-based metabolomics approach applied to the MYC-driven MB D283 and D458 cell lines before and after the treatment with the BETi OTX-015. We found that OTX-015 triggers a metabolic shift in both cell lines resulting in increased levels of myo-inositol, glycerophosphocholine, UDP-N-acetylglucosamine, glycine, serine, pantothenate and phosphocholine. Moreover, we show that OTX-015 alters ascorbate and aldarate metabolism, inositol phosphate metabolism, phosphatidylinositol signalling system, glycerophospholipid metabolism, ether lipid metabolism, aminoacyl-tRNA biosynthesis, and glycine, serine and threonine metabolism pathways in both cell lines. These insights provide a metabolic characterisation of MYC-driven childhood MB cell lines, which could pave the way for the discovery of novel druggable pathways. Importantly, these findings will also contribute to understand the downstream effects of BETis on MYC-driven MB, potentially aiding the development of new therapeutic strategies to combat medulloblastoma.

Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer

Ovarian cancer (OC) is the third most common malignant tumor of women accompanied by alteration of systemic metabolism, yet the underlying interactions between the local OC tissue and other system biofluids remain unclear. In this study, we recruited 17 OC patients, 16 benign ovarian tumor (BOT) patients, and 14 control patients to collect biological samples including ovary plasma, urine, and hair from the same patient. The metabolic features of samples were characterized using a global and targeted metabolic profiling strategy based on Gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) revealed that the metabolites display obvious differences in ovary tissue, plasma, and urine between OC and non-malignant groups but not in hair samples. The metabolic alterations in OC tissue included elevated glycolysis (lactic acid) and TCA cycle intermediates (malic acid, fumaric acid) were related to energy metabolism. Furthermore, the increased levels of glutathione and polyunsaturated fatty acids (linoleic acid) together with decreased levels of saturated fatty acid (palmitic acid) were observed, which might be associated with the anti-oxidative stress capability of cancer. Furthermore, how metabolite profile changes across differential biospecimens were compared in OC patients. Plasma and urine showed a lower concentration of amino acids (alanine, aspartic acid, glutamic acid, proline, leucine, and cysteine) than the malignant ovary. Plasma exhibited the highest concentrations of fatty acids (stearic acid, EPA, and arachidonic acid), while TCA cycle intermediates (succinic acid, citric acid, and malic acid) were most concentrated in the urine. In addition, five plasma metabolites and three urine metabolites showed the best specificity and sensitivity in differentiating the OC group from the control or BOT groups (AUC > 0.90) using machine learning modeling. Overall, this study provided further insight into different specimen metabolic characteristics between OC and non-malignant disease and identified the metabolic fluctuation across ovary and biofluids.

Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression

The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70–80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R² of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)–MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.

Tumour fatty acid metabolism in the context of therapy resistance and obesity

Fatty acid metabolism is known to support tumorigenesis and disease progression as well as treatment resistance through enhanced lipid synthesis, storage and catabolism. More recently, the role of membrane fatty acid composition, for example, ratios of saturated, monounsaturated and polyunsaturated fatty acids, in promoting cell survival while limiting lipotoxicity and ferroptosis has been increasingly appreciated. Alongside these insights, it has become clear that tumour cells exhibit plasticity with respect to fatty acid metabolism, responding to extratumoural and systemic metabolic signals, such as obesity and cancer therapeutics, to promote the development of aggressive, treatment-resistant disease. Here, we describe cellular fatty acid metabolic changes that are connected to therapy resistance and contextualize obesity-associated changes in host fatty acid metabolism that likely influence the local tumour microenvironment to further modify cancer cell behaviour while simultaneously creating potential new vulnerabilities.

The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives

Rare ovarian cancers are ovarian cancers with an annual incidence of less than 6 cases per 100,000 women. They generally have a poor prognosis due to being delayed diagnosis and treatment. Exploration of molecular mechanisms in these cancers has been challenging due to their rarity and research efforts being fragmented across the world. Omics approaches can provide detailed molecular snapshots of the underlying mechanisms of these cancers. Omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, can identify potential candidate biomarkers for diagnosis, prognosis, and screening of rare gynecological cancers and can aid in identifying therapeutic targets. The integration of multiple omics techniques using approaches such as proteogenomics can provide a detailed understanding of the molecular mechanisms of carcinogenesis and cancer progression. Further, omics approaches can provide clues towards developing immunotherapies, cancer recurrence, and drug resistance in tumors; and form a platform for personalized medicine. The current review focuses on the application of omics approaches and integrative biology to gain a better understanding of rare ovarian cancers.

Evaluating Targeted Therapies in Ovarian Cancer Metabolism: Novel Role for PCSK9 and Second Generation mTOR Inhibitors

BACKGROUND

Dysregulated lipid metabolism is emerging as a hallmark in several malignancies, including ovarian cancer (OC). Specifically, metastatic OC is highly dependent on lipid-rich omentum. We aimed to investigate the therapeutic value of targeting lipid metabolism in OC. For this purpose, we studied the role of PCSK9, a cholesterol-regulating enzyme, in OC cell survival and its downstream signaling. We also investigated the cytotoxic efficacy of a small library of metabolic (n = 11) and mTOR (n = 10) inhibitors using OC cell lines (n = 8) and ex vivo patient-derived cell cultures (PDCs, n = 5) to identify clinically suitable drug vulnerabilities. Targeting PCSK9 expression with siRNA or PCSK9 specific inhibitor (PF-06446846) impaired OC cell survival. In addition, overexpression of PCSK9 induced robust AKT phosphorylation along with increased expression of ERK1/2 and MEK1/2, suggesting a pro-survival role of PCSK9 in OC cells. Moreover, our drug testing revealed marked differences in cytotoxic responses to drugs targeting metabolic pathways of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) PDCs. Our results show that targeting PCSK9 expression could impair OC cell survival, which warrants further investigation to address the dependency of this cancer on lipogenesis and omental metastasis. Moreover, the differences in metabolic gene expression and drug responses of OC PDCs indicate the existence of a metabolic heterogeneity within OC subtypes, which should be further explored for therapeutic improvements.

Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics

BACKGROUND

Epithelial ovarian cancer (EOC) is a common gynecological cancer with high mortality rates. The main objective of this study was to investigate the serum amino acid and organic acid profiles to distinguish key metabolites for screening EOC patients.

METHODS

In total, 39 patients with EOC and 31 healthy controls were selected as the training set. Serum amino acid and organic acid profiles were determined using the targeted metabolomics approach. Metabolite profiles were processed via multivariate analysis to identify potential metabolites and construct a metabolic network. Finally, a test dataset derived from 29 patients and 28 healthy controls was constructed to validate the potential metabolites.

RESULTS

Distinct amino acid and organic acid profiles were obtained between EOC and healthy control groups. Methionine, glutamine, asparagine, glutamic acid and glycolic acid were identified as potential metabolites to distinguish EOC from control samples. The areas under the curve for methionine, glutamine, asparagine, glutamic acid and glycolic acid were 0.775, 0 778, 0.955, 0.874 and 0.897, respectively, in the validation study. Metabolic network analysis of the training set indicated key roles of alanine, aspartate and glutamate metabolism as well as D-glutamine and D-glutamate metabolism in the pathogenesis of EOC.

CONCLUSIONS

Amino acid and organic acid profiles may serve as potential screening tools for EOC. Data from this study provide useful information to bridge gaps in the understanding of the amino acid and organic acid alterations associated with epithelial ovarian cancer.

The diversity and breadth of cancer cell fatty acid metabolism

Tumor cellular metabolism exhibits distinguishing features that collectively enhance biomass synthesis while maintaining redox balance and cellular homeostasis. These attributes reflect the complex interactions between cell-intrinsic factors such as genomic-transcriptomic regulation and cell-extrinsic influences, including growth factor and nutrient availability. Alongside glucose and amino acid metabolism, fatty acid metabolism supports tumorigenesis and disease progression through a range of processes including membrane biosynthesis, energy storage and production, and generation of signaling intermediates. Here, we highlight the complexity of cellular fatty acid metabolism in cancer, the various inputs and outputs of the intracellular free fatty acid pool, and the numerous ways that these pathways influence disease behavior.

Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer

Simple Summary

In-time diagnosing ovarian cancer, intractable cancer that has no symptoms can increase the survival of women. The aim of this study was to discover biomarkers from liquid biopsy samples using multi-omics approach, metabolomics and proteomics for the diagnosis of ovarian cancer. To verify our biomarker candidates, we conducted comparative analysis with other previous published studies. Despite the limitations of non-invasive samples, our findings are able to discover emerging properties through the interplay between metabolites and proteins and mechanism-based biomarkers through integrated protein and metabolite analysis.

Abstract

The 5-year survival rate in the early and late stages of ovarian cancer differs by 63%. In addition, a liquid biopsy is necessary because there are no symptoms in the early stage and tissue collection is difficult without using invasive methods. Therefore, there is a need for biomarkers to achieve this goal. In this study, we found blood-based metabolite or protein biomarker candidates for the diagnosis of ovarian cancer in the 20 clinical samples (10 ovarian cancer patients and 10 healthy control subjects). Plasma metabolites and proteins were measured and quantified using mass spectrometry in ovarian cancer patients and control groups. We identified the differential abundant biomolecules (34 metabolites and 197 proteins) and statistically integrated molecules of different dimensions to better understand ovarian cancer signal transduction and to identify novel biological mechanisms. In addition, the biomarker reliability was verified through comparison with existing research results. Integrated analysis of metabolome and proteome identified emerging properties difficult to grasp with the single omics approach, more reliably interpreted the cancer signaling pathway, and explored new drug targets. Especially, through this analysis, proteins (PPCS, PMP2, and TUBB) and metabolites (L-carnitine and PC-O (30:0)) related to the carnitine system involved in cancer plasticity were identified.

The application of metabolomics in ovarian cancer management: a systematic review

Metabolomics, the global analysis of metabolites in a biological specimen, could potentially provide a fast method of biomarker identification for ovarian cancer. This systematic review aims to examine findings from studies that apply metabolomics to the diagnosis, prognosis, treatment, and recurrence of ovarian cancer. A systematic search of English language publications was conducted on PubMed, Science Direct, and SciFinder. It was augmented by a snowball strategy, whereby further relevant studies are identified from reference lists of included studies. Studies in humans with ovarian cancer which focus on metabolomics of biofluids and tumor tissue were included. No restriction was placed on the time of publication. A separate review of targeted metabolomic studies was conducted for completion. Qualitative data were summarized in a comprehensive table. The studies were assessed for quality and risk of bias using the ROBINS-I tool. 32 global studies were included in the main systematic review. Most studies applied metabolomics to diagnosing ovarian cancer, within which the most frequently reported metabolite changes were a down-regulation of phospholipids and amino acids: histidine, citrulline, alanine, and methionine. Dysregulated phospholipid metabolism was also reported in the separately reviewed 18 targeted studies. Generally, combinations of more than one significant metabolite as a panel, in different studies, achieved a higher sensitivity and specificity for diagnosis than a single metabolite; for example, combinations of different phospholipids. Widespread metabolite differences were observed in studies examining prognosis, treatment, and recurrence, and limited conclusions could be drawn. Cellular processes of proliferation and invasion may be reflected in metabolic changes present in poor prognosis and recurrence. For example, lower levels of lysine, with increased cell invasion as an underlying mechanism, or glutamine dependency of rapidly proliferating cancer cells. In conclusion, this review highlights potential metabolites and biochemical pathways which may aid the clinical care of ovarian cancer if further validated.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Novel Approaches in Ovarian Cancer Research against Heterogeneity, Late Diagnosis, Drug Resistance, and Transcoelomic Metastases

The development of modern technologies has revolutionised science and has had a huge impact on biomedical studies. This review focuses on possible tools that scientists can use to face the challenges of fighting ovarian cancer. Ovarian cancer is the deadliest gynaecologic malignancy and, even after years of study, the mortality has not decreased significantly. In the era of sequencing and personalised and precision medicine, we are now closer than ever to helping patients and physicians in regard to treatment and diagnosis of this disease. This work summarises the newest findings in the development of ovarian cancer research.