Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

Targeted Nanocarrier-Based Drug Delivery Strategies for Improving the Therapeutic Efficacy of PARP Inhibitors against Ovarian Cancer

The current focus of ovarian cancer (OC) research is the improvement of treatment options through maximising drug effectiveness. OC remains the fifth leading cause of cancer-induced mortality in women worldwide. In recent years, nanotechnology has revolutionised drug delivery systems. Nanoparticles may be utilised as carriers in gene therapy or to overcome the problem of drug resistance in tumours by limiting the number of free drugs in circulation and thereby minimising undesired adverse effects. Cell surface receptors, such as human epidermal growth factor 2 (HER2), folic acid (FA) receptors, CD44 (also referred to as homing cell adhesion molecule, HCAM), and vascular endothelial growth factor (VEGF) are highly expressed in ovarian cancer cells. Generation of active targeting nanoparticles involves modification with ligands that recognise cell surface receptors and thereby promote internalisation by cancer cells. Several poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) are currently used for the treatment of high-grade serous ovarian carcinomas (HGSOC) or platinum-sensitive relapsed OC. However, PARP resistance and poor drug bioavailability are common challenges, highlighting the urgent need to develop novel, effective strategies for ovarian cancer treatment. This review evaluates the utility of nanoparticles in ovarian cancer therapy, with a specific focus on targeted approaches and the use of PARPi nanocarriers to optimise treatment outcomes.

Racial Differences in Vaginal Fluid Metabolites and Association with Systemic Inflammation Markers among Ovarian Cancer Patients: A Pilot Study

The vaginal microbiome differs by race and contributes to inflammation by directly producing or consuming metabolites or by indirectly inducing host immune response, but its potential contributions to ovarian cancer (OC) disparities remain unclear. In this exploratory cross-sectional study, we examine whether vaginal fluid metabolites differ by race among patients with OC, if they are associated with systemic inflammation, and if such associations differ by race. Study participants were recruited from the Ovarian Cancer Epidemiology, Healthcare Access, and Disparities Study between March 2021 and September 2022. Our study included 36 study participants with ovarian cancer who provided biospecimens; 20 randomly selected White patients and all 16 eligible Black patients, aged 50-70 years. Acylcarnitines (n = 45 species), sphingomyelins (n = 34), and ceramides (n = 21) were assayed on cervicovaginal fluid, while four cytokines (IL-1β, IL-10, TNF-α, and IL-6) were assayed on saliva. Seven metabolites showed >2-fold differences, two showed significant differences using the Wilcoxon rank-sum test (p < 0.05; False Discovery Rate > 0.05), and 30 metabolites had coefficients > ±0.1 in a Penalized Discriminant Analysis that achieved two distinct clusters by race. Arachidonoylcarnitine, the carnitine adduct of arachidonic acid, appeared to be consistently different by race. Thirty-eight vaginal fluid metabolites were significantly correlated with systemic inflammation biomarkers, irrespective of race. These findings suggest that vaginal fluid metabolites may differ by race, are linked with systemic inflammation, and hint at a potential role for mitochondrial dysfunction and sphingolipid metabolism in OC disparities. Larger studies are needed to verify these findings and further establish specific biological mechanisms that may link the vaginal microbiome with OC racial disparities.

Microfluidic production of amiodarone loaded nanoparticles and application in drug repositioning in ovarian cancer

Amiodarone repositioning in cancer treatment is promising, however toxicity limits seem to arise, constraining its exploitability. Notably, amiodarone has been investigated for the treatment of ovarian cancer, a tumour known for metastasizing within the peritoneal cavity. This is associated with an increase of fatty acid oxidation, which strongly depends on CPT1A, a transport protein which has been found overexpressed in ovarian cancer. Amiodarone is an inhibitor of CPT1A but its role still has to be explored. Therefore, in the present study, amiodarone was tested on ovarian cancer cell lines with a focus on lipid alteration, confirming its activity. Moreover, considering that drug delivery systems could lower drug side effects, microfluidics was employed for the development of drug delivery systems of amiodarone obtaining simultaneously liposomes with a high payload and amiodarone particles. Prior to amiodarone loading, microfluidics production was optimized in term of temperature and flow rate ratio. Moreover, stability over time of particles was evaluated. In vitro tests confirmed the efficacy of the drug delivery systems.

Insights into the Microbial Composition of Intratumoral, Reproductive Tract, and Gut Microbiota in Ovarian Cancer Patients

According to the latest global cancer data, ovarian cancer is the deadliest among all gynecological malignant tumors and ranks fifth in terms of mortality. Its etiology and pathogenesis are unknown, and the 5-year survival rate of patients with advanced ovarian cancer is only 40% (Sung et al. CA Cancer J Clin 71:209-49, 2021). Recent research has shown that the human microbiota plays a crucial role in the development and progression of tumors, including ovarian cancer. Numerous studies have highlighted the complex connections between the reproductive tract microbiota, intestinal microbiota, and ovarian cancer (Jacobson et al. PeerJ 9:e11574, 2021). Therefore, this chapter will delve into composition, function, and the correlation between microbiota and immunity in the field of ovarian cancer microbiota, as well as the potential of bacteria in therapeutics and diagnostics of ovarian cancer.

Lactate plasma level as a potential biomarker in early diagnosis of ectopic pregnancy: A case-control survey

Introduction

A novel metabolomics survey proposed lactic acid as a diagnostic biomarker to detect ectopic pregnancy (EP). Here we investigate the plasma level of lactate for early diagnosis of EP as a potential biomarker.

Methods

In a case-control study, the reproductive aged women with definite tubal EP (6-10 weeks' gestation), referred to our department during 2021-2022, considered as case group, and women with normal singleton pregnancy in the same gestational age as control group. After informed concept, demographic data (maternal and gestational age and parity) recorded and 5 mL venous blood samples were taken to detect the lactate plasma level. The data analyzed using SPSS software ver22.

Results

Finally, 95 participations (50 in case and 45 in control group) enrolled. The clinical results showed that the most of case group were aged more than 35 years old with had higher parity and body mass index, but, no statistically significant difference showed up. On the other hand, although the lactate level was slightly higher in women with EP, but, the plasma lactate level did not statistically differ between the two study groups. Also, the logistic regression showed no relationship between the demographic variables and the lactate plasma level.

Conclusion

It seems that the plasma level of lactate cannot be a diagnostic biomarker for EP.

The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review

Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.

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.

Comparative metabolomic profiling of women undergoing in vitro fertilization procedures reveals potential infertility-related biomarkers in follicular fluid

Infertility is a worldwide concern, affecting one in six couples throughout their reproductive period. Therefore, enhancing the clinical tools available to identify the causes of infertility may save time, money, and emotional distress for the involved parties. This study aims to annotate potential biomarkers in follicular fluid that are negatively affecting pregnancy outcomes in women suffering infertility-related diseases such as endometriosis, tuboperitoneal factor, uterine factor, and unexplained infertility, using a metabolomics approach through high-resolution mass spectrometry. Follicular fluid samples collected from women who have the abovementioned diseases and managed to become pregnant after in vitro fertilization procedures [control group (CT)] were metabolically compared with those from women who suffer from the same diseases and could not get pregnant after the same treatment [infertile group (IF)]. Mass spectrometry analysis indicated 10 statistically relevant differential metabolites in the IF group, including phosphatidic acids, phosphatidylethanolamines, phosphatidylcholines, phosphatidylinositol, glucosylceramides, and 1-hydroxyvitamin D3 3-D-glucopyranoside. These metabolites are associated with cell signaling, cell proliferation, inflammation, oncogenesis, and apoptosis, and linked to infertility problems. Our results indicate that understanding the IF's metabolic profile may result in a faster and more assertive female infertility diagnosis, lowering the costs, and increasing the probability of a positive pregnancy outcome.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Dysregulated Metabolism in EGFR-TKI Drug Resistant Non-Small-Cell Lung Cancer: A Systematic Review

Drug resistance is a common barrier to continued effective treatment in cancer. In non-small-cell lung cancer (NSCLC), tyrosine kinase inhibitors that target the epidermal growth factor receptor (EGFR-TKIs) exhibit good efficacy in cancer treatment until acquired resistance occurs. It has been observed that drug resistance is accompanied by numerous molecular-level changes, including significant shifts in cellular metabolism. The purpose of this study was to critically and systematically review the published literature with respect to how metabolism differs in drug-resistant compared to drug-sensitive NSCLC. Understanding the differences between resistant and sensitive cells is vital and has the potential to allow interventions that enable the re-sensitisation of resistant cells to treatment, and consequently reinitiate the therapeutic effect of EGFR-TKIs. The main literature search was performed using relevant keywords in PubMed and Ovid (Medline) and reviewed using the Covidence platform. Of the 1331 potentially relevant literature records retrieved, 27 studies were subsequently selected for comprehensive analysis. Collectively, the literature revealed that NSCLC cell lines resistant to EGFR-TKI treatment possess characteristic metabolic and lipidomic phenotypic signatures that differentiate them from sensitive lines. Further exploration of these reported differences suggests that drug-resistant cell lines are differentially reliant on cellular energy sources and that modulation of relative energy production pathways may lead to the reversal of drug resistance.

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

The Burden of Hepatitis B, Hepatitis C, and Human Immunodeficiency Viruses in Ovarian Cancer Patients in Nairobi, Kenya

Ovarian cancer (OC) is a gynecological malignancy characterized by high morbidity and mortalities due to late-stage diagnosis because accurate early diagnostic biomarkers are lacking. Testing of Hepatitis B virus (HBV), Hepatitis C virus (HCV), and Human immunodeficiency virus (HIV) infections in OC patients is pertinent in light of the emerging evidence of their contribution to poor prognosis. We, for the first time, investigated the prevalence of HBV, HCV, and HIV infections in a Kenyan cohort of OC to inform optimal management. We recruited a cohort of women above 18 years of age, comprising 86 OC patients and 50 healthy controls. Participants’ blood samples were serologically screened for HBV, HCV, and HIV. We found seroprevalence rates of 29.1%, 26.7%, and 1.2% for HBV, HIV, and HCV, respectively, in OC patients. The healthy control group had HBV and HIV seroprevalence rates of 3.9% for each with no positive HCV case. HBV/HIV coinfection was noted only in the OC group with a positivity rate of 17.4%. In summary, we found higher HBV and HIV seroprevalence in Kenyan OC patients compared to the healthy control group, whereas HCV prevalence was reflective of the general population. Hence, we recommend screening for HBV and HIV among OC patients destined for anticancer treatment.

Metabolomic analysis of endometrial cancer by high-resolution magic angle spinning NMR spectroscopy

PURPOSE

To analyze endometrial metabolite profiles between patients with endometrial cancer and controls.

METHODS

Seventeen (17) women with endometrium cancer and 18 controls were enrolled in this study. ¹H HR-MAS (High Resolution-Magic Angle Spinning) NMR (Nuclear Magnetic Resonance) spectroscopy data obtained from endometrial tissue samples of patients with endometrial cancer and control group were analyzed with bioinformatics methods.

RESULTS

Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of pre-processed spectral data shows a separation between the samples from patients with endometrial cancer and controls. Analysis results suggest that the levels of lactate, glucose, o-phosphoethanolamine, choline, glycerophosphocholine, phosphocholine, leucine, isoleucine, valine, glutamate, glutamine, n-acetyltyrosine, methionine, taurine, alanine, aspartate and phenylalanine are increased in patients with endometrial cancer compared to the controls.

CONCLUSION

The metabolomics signature of patients with endometrial cancer is different from that of benign endometrial tissue.

BCKDK Promotes Ovarian Cancer Proliferation and Migration by Activating the MEK/ERK Signaling Pathway

Background

Ovarian cancer (OC) is the most fatal gynecologic cancer. The branched-chain α-keto acid dehydrogenase kinase (BCKDK) plays an important role in many serious human diseases, including cancers. Its function in promoting cell proliferation and migration has been reported in various cancers. However, the biological role of BCKDK and its molecular mechanisms underlying OC initiation and progression are unclear.

Methods

First, the expression level of BCKDK in OC cell lines or tissues was determined using tissue microarray- (TMA-) based immunohistochemistry or western blotting. Then, growth curve analysis, anchorage-independent cell transformation assays, wound healing assays, cell migration assays, and tumor xenografts were used to test whether BCKDK could promote cell transformation or metastasis. Finally, the signaling pathways involved in this process were investigated by western blotting or immunoprecipitation.

Results

We found that the expression of BCKDK was upregulated in OC tissues and the high expression of BCKDK was correlated with an advanced pathological grade in patients. The ectopic overexpression of BCKDK promoted the proliferation and migration of OC cells, and the knockdown of BCKDK with shRNAs inhibited the proliferation and migration of OC ex vivo and in vivo. Moreover, BCKDK promoted OC proliferation and migration by activating MEK.

Conclusions

Our results demonstrate that BCKDK promotes OC proliferation and migration by activating the MEK/ERK signaling pathway. Targeting the BCKDK-MEK axis may provide a new therapeutic strategy for treating patients with OC.

GC-MS based metabolite fingerprinting of serous ovarian carcinoma and benign ovarian tumor

The aim of this study is to identify urinary metabolomic profile of benign and malign ovarian tumors patients. Samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and metabolomic tools to define biomarkers that cause differentiation between groups. 7 metabolites were found to be different in patients with ovarian cancer (OC) and benign tumors (BT). R2Y and Q2 values were found to be 0.670 and 0.459, respectively. L-tyrosine, glycine, stearic acid, turanose and L-threonine metabolites were defined as prominent biomarkers. The sensitivity of the model was calculated as 90.72% and the specificity as 82.09%. In the pathway analysis, glutathione metabolism, aminoacyl-tRNA biosynthesis, glycine serine and threonine metabolic pathway, primary bile acid biosynthesis pathways were found to be important. According to the t-test, 29 metabolites were found to be significant in urine samples of OC patients and healthy controls (HC). R2Y and Q2 values were found to be 0.8170 and 0.749, respectively. These results showed that the model has high compatibility and predictive power. Benzoic acid, L-threonine, L-pyroglutamic acid, creatinine and 3,4-dihydroxyphenylacetic acid metabolites were determined as prominent biomarkers. The sensitivity of the model was calculated as 93.81% and the specificity as 98.59%. Glycine serine and threonine metabolic pathway, glutathione metabolism and aminoacyl-tRNA biosynthesis pathways were determined important in OC patients and HC. The R2Y, Q2, sensitivity and specificity values in the urine samples of BT patients and HC were found to be 0.869, 0.794, 91.75, 97.01% and 97.18%, respectively. L-threonine, L-pyroglutamic acid, benzoic acid, creatinine and pentadecanol metabolites were determined as prominent biomarkers. Valine, leucine and isoleucine biosynthesis and aminoacyl-tRNA biosynthesis were significant. In this study, thanks to the untargeted metabolomic approach and chemometric methods, every group was differentiated from the others and prominent biomarkers were determined.

OUP accepted manuscript

Currently, the only definitive method for diagnosing ovarian cancer involves histological examination of tissue obtained at time of surgery or by invasive biopsy. Blood has traditionally been the biofluid of choice in ovarian cancer biomarker discovery; however, there has been a growing interest in exploring urinary biomarkers, particularly as it is non-invasive. In this systematic review, we present the diagnostic accuracy of urinary biomarker candidates for the detection of ovarian cancer. A comprehensive literature search was performed using the MEDLINE/PubMed and EMBASE, up to 1 April 2021. All included studies reported the diagnostic accuracy using sensitivity and/or specificity and/or receiver operating characteristics (ROC) curve. Risk of bias and applicability of included studies were assessed using the QUADAS-2 tool. Twenty-seven studies were included in the narrative synthesis. Protein/peptide biomarkers were most commonly described (n = 18), with seven studies reporting composite scores of multiple protein-based targets. The most frequently described urinary protein biomarker was HE4 (n = 5), with three studies reporting a sensitivity and specificity > 80%. Epigenetic (n = 1) and metabolomic/organic compound biomarkers (n = 8) were less commonly described. Overall, six studies achieved a sensitivity and specificity of >90% and/or an AUC > 0.9. Evaluation of urinary biomarkers for the detection of ovarian cancer is a dynamic and growing field. Currently, the most promising biomarkers are those that interrogate metabolomic pathways and organic compounds, or quantify multiple proteins. Such biomarkers require external validation in large, prospective observational studies before they can be implemented into clinical practice.

Models for measuring metabolic chemical changes in the metastasis of high grade serous ovarian cancer: fallopian tube, ovary, and omentum

Ovarian cancer (OC) is the most lethal gynecologic malignancy and high grade serous ovarian cancer (HGSOC) is the most common and deadly subtype, accounting for 70-80% of OC deaths. HGSOC has a distinct pattern of metastasis as many believe it originates in the fallopian tube and then it metastasizes first to the ovary, and later to the adipose-rich omentum. Metabolomics has been heavily utilized to investigate metabolite changes in HGSOC tumors and metastasis. Generally, metabolomics studies have traditionally been applied to biospecimens from patients or animal models; a number of recent studies have combined metabolomics with innovative cell-culture techniques to model the HGSOC metastatic microenvironment for the investigation of cell-to-cell communication. The purpose of this review is to serve as a tool for researchers aiming to model the metastasis of HGSOC for metabolomics analyses. It will provide a comprehensive overview of current knowledge on the origin and pattern of metastasis of HGSOC and discuss the advantages and limitations of different model systems to help investigators choose the best model for their research goals, with a special emphasis on compatibility with different metabolomics modalities. It will also examine what is presently known about the role of small molecules in the origin and metastasis of HGSOC.

Discovery of metabolomic biomarkers for discriminating platinum-sensitive and platinum-resistant ovarian cancer by using GC-MS

This study aims to determine ovarian cancer (OC) patients with platinum resistance for alternative treatment protocols by using metabolomic methodologies. Urine and serum samples of platinum-resistant and platinum-sensitive OC were analyzed using GC-MS. After data processing of GC-MS raw data, multivariate analyses were performed to interpret complex data for biologically meaningful information and to identify the biomarkers that cause differences between two groups. The biomarkers were verified after univariate, multivariate, and ROC analysis. Finally, metabolomic pathways related to group separations were specified. The results of biomarker analysis showed that 3,4-dihydroxyphenylacetic acid, 4-hydroxybutyric acid, L-threonine, D- mannose, and sorbitol metabolites were potential biomarkers in urine samples. In serum samples, L-arginine, linoleic acid, L-glutamine, and hypoxanthine were identified as important biomarkers. R2Y, Q2, AUC, sensitivity and specificity values of platinum-resistant and sensitive OC patients' urine and serum samples were 0.85, 0.545, 0.844, 91.30%, 81.08 and 0.570, 0.206, 0.743, 77.78%, 74.28%, respectively. In metabolic pathway analysis of urine samples, tyrosine metabolism and fructose and mannose metabolism were found to be statistically significant (p < 0.05) for the discrimination of the two groups. While 3,4-dihydroxyphenylacetic acid, L-tyrosine, and fumaric acid metabolites were effective in tyrosine metabolism. D-sorbitol and D-mannose metabolites were significantly important in fructose and mannose metabolism. However, seven metabolomic pathways were significant (p < 0.05) in serum samples. In terms of p-value, L-glutamine in the nitrogen metabolic pathway from the first three pathways; L-glutamine and pyroglutamic acid metabolites in D-glutamine and D-glutamate metabolism. In the arginine and proline metabolic pathway, L-arginine, L-proline, and L-ornithine metabolites differed significantly between the two groups.

Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer

Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.

Quantitative global lipidomics analysis of patients with ovarian cancer versus benign adnexal mass

Altered lipid metabolism has emerged as an important feature of ovarian cancer (OC), yet the translational potential of lipid metabolites to aid in diagnosis and triage remains unproven. We conducted a multi-level interrogation of lipid metabolic phenotypes in patients with adnexal masses, integrating quantitative lipidomics profiling of plasma and ascites with publicly-available tumor transcriptome data. Using Sciex Lipidyzer, we assessed concentrations of > 500 plasma lipids in two patient cohorts-(i) a pilot set of 100 women with OC (50) or benign tumor (50), and (ii) an independent set of 118 women with malignant (60) or benign (58) adnexal mass. 249 lipid species and several lipid classes were significantly reduced in cases versus controls in both cohorts (FDR < 0.05). 23 metabolites-triacylglycerols, phosphatidylcholines, cholesterol esters-were validated at Bonferroni significance (P < 9.16 × 10-5). Certain lipids exhibited greater alterations in early- (diacylglycerols) or late-stage (lysophospholipids) cases, and multiple lipids in plasma and ascites were positively correlated. Lipoprotein receptor gene expression differed markedly in OC versus benign tumors. Importantly, several plasma lipid species, such as DAG(16:1/18:1), improved the accuracy of CA125 in differentiating early-stage OC cases from benign controls, and conferred a 15-20% increase in specificity at 90% sensitivity in multivariate models adjusted for age and BMI. This study provides novel insight into systemic and local lipid metabolic differences between OC and benign disease, further implicating altered lipid uptake in OC biology, and advancing plasma lipid metabolites as a complementary class of circulating biomarkers for OC diagnosis and triage.

The role of the microbiome in ovarian cancer: mechanistic insights into oncobiosis and to bacterial metabolite signaling

Ovarian cancer is characterized by dysbiosis, referred to as oncobiosis in neoplastic diseases. In ovarian cancer, oncobiosis was identified in numerous compartments, including the tumor tissue itself, the upper and lower female genital tract, serum, peritoneum, and the intestines. Colonization was linked to Gram-negative bacteria with high inflammatory potential. Local inflammation probably participates in the initiation and continuation of carcinogenesis. Furthermore, local bacterial colonies in the peritoneum may facilitate metastasis formation in ovarian cancer. Vaginal infections (e.g. Neisseria gonorrhoeae or Chlamydia trachomatis) increase the risk of developing ovarian cancer. Bacterial metabolites, produced by the healthy eubiome or the oncobiome, may exert autocrine, paracrine, and hormone-like effects, as was evidenced in breast cancer or pancreas adenocarcinoma. We discuss the possible involvement of lipopolysaccharides, lysophosphatides and tryptophan metabolites, as well as, short-chain fatty acids, secondary bile acids and polyamines in the carcinogenesis of ovarian cancer. We discuss the applicability of nutrients, antibiotics, and probiotics to harness the microbiome and support ovarian cancer therapy. The oncobiome and the most likely bacterial metabolites play vital roles in mediating the effectiveness of chemotherapy. Finally, we discuss the potential of oncobiotic changes as biomarkers for the diagnosis of ovarian cancer and microbial metabolites as possible adjuvant agents in therapy.

Serum Metabolic Profiling Analysis of Chronic Gastritis and Gastric Cancer by Untargeted Metabolomics

Purpose

Gastric cancer is a common tumor of the digestive system. Identification of potential molecules associated with gastric cancer progression and validation of potential biomarkers for gastric cancer diagnosis are very important. Thus, the aim of our study was to determine the serum metabolic characteristics of the serum of patients with chronic gastritis (CG) or gastric cancer (GC) and validate candidate biomarkers for disease diagnosis.

Experimental Design

A total of 123 human serum samples from patients with CG or GC were collected for untargeted metabolomic analysis via UHPLC-Q-TOF/MS to determine characteristics of the serum. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and heat map were used for multivariate analysis. In addition, commercial databases were used to identify the pathways of metabolites. Differential metabolites were identified based on a heat map with a t-test threshold (p < 0.05), fold-change threshold (FC > 1.5 or FC < 2/3) and variable importance in the projection (VIP >1). Then, differential metabolites were analyzed by receiver operating characteristic (ROC) curve to determine candidate biomarkers. All samples were analyzed for fasting lipid profiles.

Results

Analysis of serum metabolomic profiles indicated that most of the altered metabolic pathways in the three groups were associated with lipid metabolism (p < 0.05) and lipids and lipid-like molecules were the predominating metabolites within the top 100 differential metabolites (p < 0.05, FC > 1.5 or FC < 2/3, and VIP >1). Moreover, differential metabolites, including hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine had high diagnostic performance according to PLS-DA. In addition, fasting lipid profile analysis showed the serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (Apo-A1) were decreased concomitant to the progression of the progression of the disease compared with those in the control group (p < 0.05).

Conclusions

Thus, this study demonstrated that lipid metabolism may influence the development of CG to GC. Hexadecasphinganine, linoleamide, and N-Hydroxy arachidonoyl amine were selected as candidate diagnostic markers for CG and GC.

The Interaction Between Microorganisms, Metabolites, and Immune System in the Female Genital Tract Microenvironment

The female reproductive tract microenvironment includes microorganisms, metabolites, and immune components, and the balance of the interactions among them plays an important role in maintaining female reproductive tract homeostasis and health. When any one of the reproductive tract microorganisms, metabolites, or immunity is out of balance, it will affect the other two, leading to the occurrence and development of diseases and the appearance of corresponding symptoms and signs, such as infertility, miscarriage, premature delivery, and gynecological tumors caused by infectious diseases of the reproductive tract. Nutrients in the female reproductive tract provide symbiotic and pathogenic microorganisms with a source of nutrients for their own reproduction and utilization. At the same time, this interaction with the host forms a variety of metabolites. Changes in metabolites in the host reproductive tract are related not only to the interaction between the host and microbiota under dysbiosis but also to changes in host immunity or the environment, all of which will participate in the pathogenesis of diseases and lead to disease-related phenotypes. Microorganisms and their metabolites can also interact with host immunity, activate host immunity, and change the host immune status and are closely related to persistent genital pathogen infections, aggravation of infectious diseases, severe pregnancy outcomes, and even gynecological cancers. Therefore, studying the interaction between microorganisms, metabolites, and immunity in the reproductive tract cannot only reveal the pathogenic mechanisms that lead to inflammation of the reproductive tract, adverse pregnancy outcomes and tumorigenesis but also provide a basis for further research on the diagnosis and treatment of targets.

Metabolomics: The Stethoscope for the Twenty-First Century

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

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.

Metabolomic Biomarkers in Gynecology: A Treasure Path or a False Path?

Omic-technologies (genomics, transcriptomics, proteomics and metabolomics) have become more important in current medical science. Among them, it is metabolomics that most accurately reflects the minor changes in body functioning, as it focuses on metabolome - the group of the metabolism products, both intermediate and end. Therefore, metabolomics is actively engaged in fundamental and clinical studies and search for potential biomarkers. The biomarker could be used in diagnostics, management and stratification of the patients, as well as in prognosing the outcomes. The good example is gynecology, since many gynecological diseases lack effective biomarkers. In the current review, we aimed to summarize the results of the studies, devoted to the search of potential metabolomic biomarkers for the most common gynecological diseases.

Metabolomics in endometrial cancer diagnosis: A systematic review

INTRODUCTION

Endometrial cancer (EC) is the most common gynecological malignancy in the developed world. The prognosis of EC strongly depends on tumor stage, hence the importance of improving diagnosis. Metabolomics has recently appeared as a promising test for a non-invasive diagnosis of several diseases. Nevertheless, no metabolic marker has been approved for use in the routine practice. We aimed to provide an overview of metabolomics findings in the diagnosis of EC.

MATERIAL AND METHODS

A systematic review was performed by searching eight electronic databases from their inception to October 2019 for studies assessing metabolomics in EC diagnosis. Extracted data included characteristics of patients and EC, serum concentration of metabolites in women with and without EC and its association with EC diagnosis, tumor behavior and pathological characteristics.

RESULTS

Six studies with 732 women (356 cases and 376 controls) were included. Several metabolites were found able to predict the presence of EC, tumor behavior (progression and recurrence) and pathological characteristics (histotype, myometrial invasion and lymph vascular space invasion).

CONCLUSIONS

Metabolomics might be suitable for a non-invasive diagnosis and screening of EC, offering the possibility to predict tumor behavior and pathological characteristics. Further studies are necessary to validate these results.

Update on urine as a biomarker in cancer: a necessary review of an old story

Introduction: Cancer causes thousands of deaths worldwide each year. Therefore, monitoring of health status and the early diagnosis of cancer using noninvasive assays, such as the analysis of molecular biomarkers in urine, is essential. However, effective biomarkers for early diagnosis of cancer have not been established in many types of cancer.Areas covered: In this review, we discuss recent findings with regard to the use of urine composition as a biomarker in eleven types of cancer. We also highlight the use of urine biomarkers for improving early diagnosis.Expert opinion: Urinary biomarkers have been applied for clinical application of early diagnosis. The main limitation is a lack of integrated approaches for identification of new biomarkers in most cancer. The utilization of urinary biomarker detection will be promoted by improved detection methods and new data from different types of cancers. With the development of precision medicine, urinary biomarkers will play an increasingly important clinical role. Future early diagnosis would benefit from changes in the utilization of urinary biomarkers.

Relative Ratios Enhance the Diagnostic Power of Phospholipids in Distinguishing Benign and Cancerous Ovarian Masses

Ovarian cancer remains a highly lethal disease due to its late clinical presentation and lack of reliable early biomarkers. Protein-based diagnostic markers have presented limitations in identifying ovarian cancer. We tested the potential of phospholipids as markers of ovarian cancer by utilizing inter-related regulation of phospholipids, a unique property that allows the use of ratios between phospholipid species for quantitation. High-performance liquid chromatography mass spectrometry was used to measure phospholipid, lysophospholipid, and sphingophospholipid content in plasma from patients with benign ovarian masses, patients with ovarian cancer, and controls. We applied both absolute and relative phospholipid ratios for quantitation. Receiver operating characteristic analysis was performed to test the sensitivity and specificity. We found that utilization of ratios between phospholipid species greatly outperformed absolute quantitation in the identification of ovarian cancer. Of the phospholipids analyzed, species in phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and sphingomyelin (SM) were found to have great biomarker potential. LPC(20:4)/LPC(18:0) carried the greatest capacity to differentiate cancer from control, SM(d18:1/24:1)/SM(d18:1/22:0) to differentiate benign from cancer, and PC(18:0/20:4)/PC(18:0/18:1) to differentiate benign from control. These results demonstrate the potential of plasma phospholipids as a novel marker of ovarian cancer by utilizing the unique characteristics of phospholipids to further enhance the diagnostic power.

Prospective serum metabolomic profiling of lethal prostate cancer

Impaired metabolism may play an important role in the pathogenesis of lethal prostate cancer, yet there is a paucity of evidence regarding the association. We conducted a large prospective serum metabolomic analysis of lethal prostate cancer in 523 cases and 523 matched controls nested within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from baseline fasting serum collection to prostate cancer death was 18 years (maximum 30 years). We identified 860 known biochemicals through an ultrahigh-performance LC-MS/MS platform. Conditional logistic regression models estimated odds ratios (OR) and 95% confidence intervals of risk associated with 1-standard deviation (s.d.) increases in log-metabolite signals. We identified 34 metabolites associated with lethal prostate cancer with a false discovery rate (FDR) < 0.15. Notably, higher serum thioproline, and thioproline combined with two other cysteine-related amino acids and redox metabolites, cystine and cysteine, were associated with reduced risk (1-s.d. OR = 0.75 and 0.71, respectively; p ≤ 8.2 × 10-5 ). By contrast, the dipeptide leucylglycine (OR = 1.36, p = 8.2 × 10-5 ), and three gamma-glutamyl amino acids (OR = 1.28-1.30, p ≤ 4.6 × 10-4 ) were associated with increased risk of lethal prostate cancer. Cases with metastatic disease at diagnosis (n = 179) showed elevated risk for several lipids, including especially the ketone body 3-hydroxybutyrate (BHBA), acyl carnitines, and dicarboxylic fatty acids (1.37 ≤ OR ≤ 1.49, FDR < 0.15). These findings provide a prospective metabolomic profile of lethal prostate cancer characterized by altered biochemicals in the redox, dipeptide, pyrimidine, and gamma-glutamyl amino acid pathways, whereas ketone bodies and fatty acids were associated specifically with metastatic disease.

Metabolomic identification of novel diagnostic biomarkers in ectopic pregnancy

INTRODUCTION

Ectopic pregnancy (EP) is a potentially life-threatening condition and early diagnosis still remains a challenge, causing a delay in management leading to tubal rupture.

OBJECTIVES

To identify putative plasma biomarkers for the detection of tubal EP and elucidate altered biochemical pathways in EP compared to intrauterine pregnancies.

METHODS

This case-control study included prospective recruitment of 39 tubal EP cases and 89 early intrauterine pregnancy controls. Plasma samples were biochemically profiled using proton nuclear magnetic resonance spectroscopy (¹H NMR). To avoid over-fitting, datasets were randomly divided into a discovery group (26 cases vs 60 controls) and a test group (13 cases and 29 controls). Logistic regression models were developed in the discovery group and validated in the independent test group. Area under the receiver operating characteristics curve (AUC), 95% confidence interval (CI), sensitivity, and specificity values were calculated.

RESULTS

In total 13 of 43 (30.3%) metabolite concentrations were significantly altered in EP plasma (p < 0.05). Metabolomic profiling yielded significant separation between EP and controls (p < 0.05). Independent validation of a two-metabolite model consisting of lactate and acetate, achieved an AUC (95% CI) = 0.935 (0.843-1.000) with a sensitivity of 92.3% and specificity of 96.6%. The second metabolite model (D-glucose, pyruvate, acetoacetate) performed well with an AUC (95% CI) = 0.822 (0.657-0.988) and a sensitivity of 84.6% and specificity of 86.2%.

CONCLUSION

We report novel metabolomic biomarkers with a high accuracy for the detection of EP. Accurate biomarkers could potentially result in improved early diagnosis of tubal EP cases.

SERS-based differential diagnosis between multiple solid malignancies: breast, colorectal, lung, ovarian and oral cancer

PURPOSE

Surface-enhanced Raman scattering (SERS) spectroscopy on serum and other biofluids for cancer diagnosis represents an emerging field, which has shown promising preliminary results in several types of malignancies. The purpose of this study was to demonstrate that SERS spectroscopy on serum can be employed for the differential diagnosis between five of the leading malignancies, ie, breast, colorectal, lung, ovarian and oral cancer.

PATIENTS AND METHODS

Serum samples were acquired from healthy volunteers (n=39) and from patients diagnosed with breast (n=42), colorectal (n=109), lung (n=33), oral (n=17), and ovarian cancer (n=13), comprising n=253 samples in total. SERS spectra were acquired using a 532 nm laser line as excitation source, while the SERS substrates were represented by Ag nanoparticles synthesized by reduction with hydroxylamine. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA).

RESULTS

The sensitivity and specificity in discriminating between cancer patients and controls was 98% and 91%, respectively. Cancer samples were correctly assigned to their corresponding cancer types with an accuracy of 88% for oral cancer, 86% for colorectal cancer, 80% for ovarian cancer, 76% for breast cancer and 59% for lung cancer.

CONCLUSION

SERS on serum represents a promising strategy of diagnosing cancer which can discriminate between cancer patients and controls, as well as between cancer types such as breast, colorectal, lung ovarian and oral cancer.

1H HR-MAS NMR-Based Metabolomics of Cancer Cells in Response to Treatment with the Diruthenium Trithiolato Complex [(p-MeC6H4iPr)2Ru2(SC6H4-p-But)3]+ (DiRu-1)

The trithiolato bridged diruthenium complex DiRu-1 [(p-MeC6H4iPr)2Ru2(SC6H4-p-But)3]+ is highly cytotoxic against various cancer cell lines, but its exact mode of action remains unknown. The present 1H HR-MAS NMR-based metabolomic study was performed on ovarian cancer cell line A2780, on its cis-Pt resistant variant A2780cisR, and on the cell line HEK-293 treated with 0.03 µM and 0.015 µM of DiRu-1 corresponding to full and half IC50 doses, respectively, to investigate the mode of action of this ruthenium complex. The resulting changes in the metabolic profile of the cell lines were studied using HR-MAS NMR of cell lysates and a subsequent statistical analysis. We show that DiRu-1 in a 0.03 µM dose has significant impact on the levels of a number of metabolites, such as glutamine, glutamate, glutathione, cysteine, lipid, creatine, lactate, and acetate, especially pronounced in the A2780cisR cell line. The IC50/2 dose shows some significant changes, but full IC50 appears to be necessary to observe the full effect. Overall, the metabolic changes observed suggest that redox homeostasis, the Warburg effect, and the lipid metabolism are affected by DiRu-1.

Deciphering the complex interplay between microbiota, HPV, inflammation and cancer through cervicovaginal metabolic profiling

BACKGROUND

Dysbiotic vaginal microbiota have been implicated as contributors to persistent HPV-mediated cervical carcinogenesis and genital inflammation with mechanisms unknown. Given that cancer is a metabolic disease, metabolic profiling of the cervicovaginal microenvironment has the potential to reveal the functional interplay between the host and microbes in HPV persistence and progression to cancer.

METHODS

Our study design included HPV-negative/positive controls, women with low-grade and high-grade cervical dysplasia, or cervical cancer (n = 78). Metabolic fingerprints were profiled using liquid chromatography-mass spectrometry. Vaginal microbiota and genital inflammation were analysed using 16S rRNA gene sequencing and immunoassays, respectively. We used an integrative bioinformatic pipeline to reveal host and microbe contributions to the metabolome and to comprehensively assess the link between HPV, microbiota, inflammation and cervical disease.

FINDINGS

Metabolic analysis yielded 475 metabolites with known identities. Unique metabolic fingerprints discriminated patient groups from healthy controls. Three-hydroxybutyrate, eicosenoate, and oleate/vaccenate discriminated (with excellent capacity) between cancer patients versus the healthy participants. Sphingolipids, plasmalogens, and linoleate positively correlated with genital inflammation. Non-Lactobacillus dominant communities, particularly in high-grade dysplasia, perturbed amino acid and nucleotide metabolisms. Adenosine and cytosine correlated positively with Lactobacillus abundance and negatively with genital inflammation. Glycochenodeoxycholate and carnitine metabolisms connected non-Lactobacillus dominance to genital inflammation.

INTERPRETATION

Cervicovaginal metabolic profiles were driven by cancer followed by genital inflammation, HPV infection, and vaginal microbiota. This study provides evidence for metabolite-driven complex host-microbe interactions as hallmarks of cervical cancer with future translational potential. FUND: Flinn Foundation (#1974), Banner Foundation Obstetrics/Gynecology, and NIH NCI (P30-CA023074).

Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges

BACKGROUND

The development of effective screening methods for early cancer detection is one of the foremost challenges facing modern cancer research. Urinary metabolomics has recently emerged as a potentially transformative approach to cancer biomarker discovery owing to its noninvasive sampling characteristics and robust analytical feasibility.

OBJECTIVE

To provide an overview of new developments in urinary metabolomics, cover the most promising aspects of hyphenated techniques in untargeted and targeted metabolomics, and to discuss technical and clinical limitations in addition to the emerging challenges in the field of urinary metabolomics and its application to cancer biomarker discovery.

METHODS

A systematic review of research conducted in the past five years on the application of urinary metabolomics to cancer biomarker discovery was performed. Given the breadth of this topic, our review focused on the five most widely studied cancers employing urinary metabolomics approaches, including lung, breast, bladder, prostate, and ovarian cancers.

RESULTS

As an extension of conventional metabolomics, urinary metabolomics has benefitted from recent technological developments in nuclear magnetic resonance, mass spectrometry, gas and liquid chromatography, and capillary electrophoresis that have improved urine metabolome coverage and analytical reproducibility. Extensive metabolic profiling in urine has revealed a significant number of altered metabolic pathways and putative biomarkers, including pteridines, modified nucleosides, and acylcarnitines, that have been associated with cancer development and progression.

CONCLUSION

Urinary metabolomics presents a transformative new approach toward cancer biomarker discovery with high translational capacity to early cancer screening.

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study

Background

Polycystic ovarian syndrome (PCOS) is a highly variable syndrome and one of the most common female endocrine disorders. Although the association inositols-glucomannan may represent a good therapeutic strategy in the treatment of PCOS women with insulin resistance, the effect of inositols on the metabolomic profile of these women has not been described yet.

Results

Fifteen PCOS-patients and 15 controls were enrolled. Patients were treated with myo-inositol (1.75 g/day), D-chiro-inositol (0.25 g/day) and glucomannan (4 g/day) for 3 months. Blood concentrations of glucose, insulin, triglycerides and cholesterol, and ovary volumes and antral follicles count, as well as metabolomic profiles, were evaluated for control subjects and for cases before and after treatment.

PCOS-patients had higher BMI compared with Controls, BMI decreased significantly after 3 months of treatment although it remained significantly higher compared to controls. 3-methyl-1-hydroxybutyl-thiamine-diphosphate, valine, phenylalanine, ketoisocapric, linoleic, lactic, glyceric, citric and palmitic acid, glucose, glutamine, creatinine, arginine, choline and tocopherol emerged as the most relevant metabolites for distinguishing cases from controls.

Conclusion

Our pilot study has identified a complex network of serum molecules that appear to be correlated with PCOS, and with a combined treatment with inositols and glucomannan.

Trial registration

ClinicalTial.gov, NCT03608813. Registered 1st August 2018 - Retrospectively registered, .

Electronic supplementary material

The online version of this article (10.1186/s13048-019-0500-x) contains supplementary material, which is available to authorized users.

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

INTRODUCTION

Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy.

OBJECTIVES

The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer.

METHODS

A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls.

RESULTS

Using partial least squares-discriminant analysis, we observed significant separation between all groups (p < 0.05) following cross validation. We identified which metabolites were significantly perturbed in each EOC grade as compared with controls and report the biochemical pathways which were perturbed due to the disease. Among these metabolic pathways, ascorbate and aldarate metabolism was identified, for the first time, as being significantly altered in both LG and HG serous cancers. Further, we have identified potential biomarkers of EOC and generated predictive algorithms with AUC (CI) = 0.940 and 0.929 for HG and LG, respectively.

CONCLUSION

These previously unreported biochemical changes provide a framework for future metabolomic studies for the development of EOC biomarkers. Finally, pharmacologic targeting of the key metabolic pathways identified herein could lead to novel and effective treatments of EOC.

Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

INTRODUCTION

Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population.

OBJECTIVE

This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC-MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response.

METHODS

Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC-MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools.

RESULTS

Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC-MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc. CONCLUSIONS: Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.

Lysophospholipid Signaling in the Epithelial Ovarian Cancer Tumor Microenvironment

As one of the important cancer hallmarks, metabolism reprogramming, including lipid metabolism alterations, occurs in tumor cells and the tumor microenvironment (TME). It plays an important role in tumorigenesis, progression, and metastasis. Lipids, and several lysophospholipids in particular, are elevated in the blood, ascites, and/or epithelial ovarian cancer (EOC) tissues, making them not only useful biomarkers, but also potential therapeutic targets. While the roles and signaling of these lipids in tumor cells are extensively studied, there is a significant gap in our understanding of their regulations and functions in the context of the microenvironment. This review focuses on the recent study development in several oncolipids, including lysophosphatidic acid and sphingosine-1-phosphate, with emphasis on TME in ovarian cancer.

Amino acid kinetics and the response to nutrition in patients with cancer

PURPOSE

Amino acids are involved in many physiological processes in the body and serve as building blocks of proteins which are the main component of muscle mass. Often patients with cancer experience muscle wasting, which is associated with poor outcomes. The purpose of this paper is to discuss amino acid kinetics in cancer, review the evidence on the response to nutrition in patients with cancer, and to give recommendations on the appropriate level of amino acid or protein intake in cancer. Current evidence shows that amino acid kinetics in patients with cancer are disturbed, as reflected by increased and decreased levels of plasma amino acids, an increased whole body turnover of protein and muscle protein breakdown. A few studies show beneficial effects of acute and short-term supplementation of high protein meals or essential amino acid mixtures on muscle protein synthesis.

CONCLUSIONS

Cancer is associated with disturbances in amino acid kinetics. A high protein intake or supplementation of amino acids may improve muscle protein synthesis. Future research needs to identify the optimal level and amino acid mixtures for patients with cancer, in particular for those who are malnourished.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Male infertility has become a global health problem. Currently, the diagnosis of male infertility depends on the results of semen quality or requires invasive surgical intervention. The process is complex and time-consuming. Metabolomics is an emerging platform with unique advantages in disease diagnosis and pathological mechanism research. In this study, ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry (UPLC-ESI-IT-TOFMS) combined with chemometrics methods was used to discover potential biomarkers of male infertility based on non-targeted plasma metabolomics. Plasma samples from healthy controls (HC, n = 43) and various types of infertile patients, i.e., patients having oligozoospermia (OS, n = 36), asthenospermia (AS, n = 56) and erectile dysfunction (ED, n = 45) were analyzed by UPLC-ESI-IT-TOFMS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results of OPLS-DA showed that HCs could be discriminated from infertile patients including OS (R² = 0.903, Q² = 0.617, AUC = 0.992), AS (R² = 0.985, Q² = 0.658, AUC = 0.999) or ED (R² = 0.942, Q² = 0.500, AUC = 0.998). Some potential biomarkers were successfully discovered by variable selection methods and variable important in the projection (VIP) in combination with the T-test. Statistical significance was set at p < 0.05; the Benjamini–Hochberg false discovery rate was used to reduce type 1 errors resulting from multiple comparisons. The identified biomarkers were associated with energy consumption, hormone regulation and antioxidant defenses in spermatogenesis. To elucidate the pathophysiology of male infertility, relative metabolic pathways were studied. It was found that male infertility is closely related to disturbed phospholipid metabolism, amino acid metabolism, steroid hormone biosynthesis metabolism, metabolism of fatty acids and products of carnitine acylation, and purine and pyrimidine metabolisms. Plasma metabolomics provides a novel strategy for the diagnosis of male infertility and offers a new insight to study pathogenesis mechanism.

Ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry combined with chemometrics methods was used to discover potential biomarkers of male infertility based on untargeted plasma metabolomics.

Identification of Metabolomic Biomarkers for Endometrial Cancer and Its Recurrence after Surgery in Postmenopausal Women

Endometrial cancer (EC) is the most frequent gynecological cancer in developed countries. Most EC occurs after menopause and is diagnosed as endometrioid (type I) carcinomas, which exhibit a favorable prognosis. In contrast, non-endometrioid (type II) carcinomas such as serous tumors have a poor prognosis. Our goal was to identify novel blood-based markers associated with EC subtypes and recurrence after surgery in postmenopausal women. Using mass spectrometry-based untargeted metabolomics, we examined preoperative serum metabolites among control women (n = 18) and those with non-recurrent (NR) and recurrent (R) cases of type I endometrioid (n = 24) and type II serous (n = 12) carcinomas. R and NR cases were similar with respect to pathological characteristics, body mass index, and age. A total of 1,592 compounds were analyzed including 14 different lipid classes. When we compared EC cases with controls, 137 metabolites were significantly different. A combination of spermine and isovalerate resulted in an age-adjusted area under the receiver-operating characteristic curve (AUC_adj) of 0.914 (P < 0.001) for EC detection. The combination of 2-oleoylglycerol and TAG42:2-FA12:0 allowed the distinction of R cases from NR cases with an AUC_adj of 0.901 (P < 0.001). Type I R cases were also characterized by much lower levels of bile acids and elevated concentrations of phosphorylated fibrinogen cleavage peptide, whereas type II R cases displayed higher levels of ceramides. The findings from our pilot study provide a detailed metabolomics study of EC and identify putative serum biomarkers for defining clinically relevant risk groups.

Usefulness of Amino Acid Profiling in Ovarian Cancer Screening with Special Emphasis on Their Role in Cancerogenesis

The aim of this study was to quantitate 42 serum-free amino acids, propose the biochemical explanation of their role in tumor development, and identify new ovarian cancer (OC) biomarkers for potential use in OC screening. The additional value of this work is the schematic presentation of the interrelationship between metabolites which were identified as significant for OC development and progression. The liquid chromatography-tandem mass spectrometry technique using highly-selective multiple reaction monitoring mode and labeled internal standards for each analyzed compound was applied. Performed statistical analyses showed that amino acids are potentially useful as OC biomarkers, especially as variables in multi-marker models. For the distinguishing metabolites the following metabolic pathways involved in cancer growth and development were proposed: histidine metabolism; tryptophan metabolism; arginine biosynthesis; arginine and proline metabolism; and alanine, aspartate and glutamine metabolism. The presented research identifies histidine and citrulline as potential new OC biomarkers. Furthermore, it provides evidence that amino acids are involved in metabolic pathways related to tumor growth and play an important role in cancerogenesis.

Metabolomic prediction of endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality.

OBJECTIVE

To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.

METHODS

We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I-II) and 10 late-stage (FIGO stages III-IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n = 69) and an independent validation group (n = 47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis.

RESULTS

A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI) = 0.826 (0.706-0.946) and a sensitivity = 82.6%, and specificity = 70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI) = 0.819 (0.689-0.95) and a sensitivity = 72.2% and specificity = 79.2% in the validation group.

CONCLUSIONS

EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

Anti-melanoma activity of Forsythiae Fructus aqueous extract in mice involves regulation of glycerophospholipid metabolisms by UPLC/Q-TOF MS-based metabolomics study

Metabolomics is a comprehensive assessment of endogenous metabolites of a biological system in a holistic context. In this study, we evaluated the in vivo anti-melanoma activity of aqueous extract of Forsythiae Fructus (FAE) and globally explored the serum metabolome characteristics of B16-F10 melanoma-bearing mice. UPLC/Q-TOF MS combined with pattern recognition approaches were employed to examine the comprehensive metabolic signatures and differentiating metabolites. The results demonstrated that FAE exhibited remarkable antitumor activity against B16-F10 melanoma in C57BL/6 mice and restored the disturbed metabolic profile by tumor insult. We identified 17 metabolites which were correlated with the antitumor effect of FAE. Most of these metabolites are involved in glycerophospholipid metabolisms. Notably, several lysophosphatidylcholines (LysoPCs) significantly decreased in tumor model group, while FAE treatment restored the changes of these phospholipids to about normal condition. Moreover, we found that lysophosphatidylcholine acyltransferase 1 (LPCAT1) and autotaxin (ATX) were highly expressed in melanoma, and FAE markedly down-regulated their expression. These findings indicated that modulation of glycerophospholipid metabolisms may play a pivotal role in the growth of melanoma and the antitumor activity of FAE. Besides, our results suggested that serum LysoPCs could be potential biomarkers for the diagnosis and prognosis of melanoma and other malignant tumors.

Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging

Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.