Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer

Role of dietary and nutritional interventions in ceramide-associated diseases

Ceramides are important intermediates in sphingolipid metabolism and serve as signaling molecules with independent biological significance. Elevated cellular and circulating ceramide levels are consistently associated with pathological conditions including cardiometabolic diseases, neurological diseases, autoimmune diseases, and cancers. Although pharmacological inhibition of ceramide formation often protects against these diseases in animal models, pharmacological modulation of ceramides in humans remains impractical. Dietary interventions including the Mediterranean diet, lacto-ovo-vegetarian diet, calorie-restricted diet, restriction of dairy product consumption, and dietary supplementation with polyunsaturated fatty acids, dietary fibers, and polyphenols, all have beneficial effects on modulating ceramide levels. Mechanistic insights into these interventions are discussed. This article reviews the relationships between ceramides and disease pathogenesis, with a focus on dietary intervention as a viable strategy for lowering the concentration of circulating ceramides.

Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer

Ovarian cancer (OC), the second leading cause of death among gynecological cancers, is often diagnosed at an advanced stage due to its asymptomatic nature at early stages. This study aimed to explore the diagnostic potential of plasma-based lipidomics combined with machine learning (ML) in OC. Non-targeted lipidomics analysis was conducted on plasma samples from participants with epithelial ovarian cancer (EOC), benign ovarian tumor (BOT), and healthy control (HC). The samples were randomly divided into a train set and a test set. Differential lipids between groups were selected using two-tailed Student's t-test and partial least squares discriminant analysis (PLS-DA). Both single lipid-based receiver operating characteristic (ROC) model, and multiple lipid-based ML model, were constructed to investigate the diagnostic value of the differential lipids. The results showed several lipids with significant diagnostic potential. ST 27:2;O achieved the highest prediction accuracy of 0.92 in distinguishing EOC from HC. DG 42:2 had the highest prediction accuracy of 0.96 in diagnosing BOT from HC. Cer d18:1/18:0 had the highest prediction accuracy of 0.65 in differentiating EOC from BOT. Furthermore, multiple lipid-based ML models illustrated better diagnostic performance. K-nearest neighbors (k-NN), partial least squares (PLS), and random forest (RF) models achieved the highest prediction accuracy of 0.96 in discriminating EOC from HC. The support vector machine (SVM) model reached the highest prediction accuracy both in distinguishing BOT from HC, and in differentiating EOC from BOT, with accuracies of 1.00 and 0.74, respectively. In conclusion, this study revealed that the combination of plasma-based lipidomics and ML algorithms is an effective method for diagnosing OC.

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

BACKGROUND

Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention.

METHODS

Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches.

RESULTS

We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set.

CONCLUSIONS

This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women.

IMPACT

Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.

Comprehensive profiling of ceramides in human serum by liquid chromatography coupled to tandem mass spectrometry combining data independent/dependent acquisition modes

Ceramides are sphingolipids with a structural function in the cell membrane and are involved in cell differentiation, proliferation and apoptosis. Recently, these chemical species have been pointed out as potential biomarkers in different diseases, due to their abnormal levels in blood. In this research, we present an overall strategy combining data-independent and dependent acquisitions (DIA and DDA, respectively) for identification, confirmation, and quantitative determination of ceramides in human serum. By application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) method in DIA mode we identified 49 ceramides including d18:1, d18:0, d18:2, d16:1, d17:1 and t18:0 species. Complementary, quantitative determination of ceramides was based on a high-throughput and fully automated method consisting of solid-phase extraction on-line coupled to LC-MS/MS in DDA to improve analytical features avoiding the errors associated to sample processing. Quantitation limits were at pg mL-1 level, the intra-day and between-days variability were below 20 and 25 %, respectively; and the accuracy, expressed as bias, was always within ±25 %. The proposed method was tested with the CORDIOPREV cohort in order to obtain a qualitative and quantitative profiling of ceramides in human serum. This characterization allowed identifying d18:1 ceramides as the most concentrated with 70.8% of total concentration followed by d18:2 and d18:0 with 13.0 % and 8.8 %, respectively. Less concentrated ceramides, d16:1, d17:1 and t18:0, reported a 7.1 % of the total content. Combination of DIA and DDA LC-MS/MS analysis enabled to profile qualitative and quantitatively ceramides in human serum.

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.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipid alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Teaser

Time-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis.

METHODS

This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning.

RESULTS

Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), L-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879).

CONCLUSIONS

This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer.

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.

Ceramide Composition in Exosomes for Characterization of Glioblastoma Stem-Like Cell Phenotypes

Glioblastoma (GBM) is one of the most malignant central nervous system tumor types. Comparative analysis of GBM tissues has rendered four major molecular subtypes. From them, two molecular subtypes are mainly found in their glioblastoma cancer stem-like cells (GSCs) derived in vitro: proneural (PN) and mesenchymal (MES) with nodular (MES-N) and semi-nodular (MES-SN) disseminations, which exhibit different metabolic, growth, and malignancy properties. Many studies suggest that cancer cells communicate between them, and the surrounding microenvironment, via exosomes. Identifying molecular markers that allow the specific isolation of GSC-derived exosomes is key in the development of new therapies. However, the differential exosome composition produced by main GSCs remains unknown. The aim of this study was to determine ceramide (Cer) composition, one of the critical lipids in both cells and their cell-derived exosomes, from the main three GSC phenotypes using mass spectrometry-based lipidomics. GSCs from human tissue samples and their cell-derived exosomes were measured using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) in an untargeted analysis. Complete characterization of the ceramide profile, in both cells and cell-derived exosomes from GSC phenotypes, showed differential distributions among them. Results indicate that such differences of ceramide are chain-length dependent. Significant changes for the C16 Cer and C24:1 Cer and their ratio were observed among GSC phenotypes, being different for cells and their cell-derived exosomes.

Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.

The Lipid Composition of Serum-Derived Small Extracellular Vesicles in Participants of a Lung Cancer Screening Study

Simple Summary

Molecular components of extracellular vesicles present in serum are potential biomarkers of lung cancer, however, none of them have been validated in the context of an actual early detection of lung cancer. Here, we compared the lipid profiles of vesicles obtained from participants in a lung cancer screening study, including patients with screening-detected cancer and individuals with benign pulmonary nodules or without pathological changes. A few lipids whose levels were different between compared groups were detected, including ceramide Cer(42:1) upregulated in vesicles from cancer patients. Furthermore, a high heterogeneity of lipid profiles of extracellular vesicles was observed, which impaired the performance of classification models based on specific compounds.

Abstract

Molecular components of exosomes and other classes of small extracellular vesicles (sEV) present in human biofluids are potential biomarkers with possible applicability in the early detection of lung cancer. Here, we compared the lipid profiles of serum-derived sEV from three groups of lung cancer screening participants: individuals without pulmonary alterations, individuals with benign lung nodules, and patients with screening-detected lung cancer (81 individuals in each group). Extracellular vesicles and particles were purified from serum by size-exclusion chromatography, and a fraction enriched in sEV and depleted of low-density lipoproteins (LDLs) was selected (similar sized vesicles was observed in all groups: 70–100 nm). The targeted mass-spectrometry-based approach enabled the detection of 352 lipids, including 201 compounds used in quantitative analyses. A few compounds, exemplified by Cer(42:1), i.e., a ceramide whose increased plasma/serum level was reported in different pathological conditions, were upregulated in vesicles from cancer patients. On the other hand, the contribution of phosphatidylcholines with poly-unsaturated acyl chains was reduced in vesicles from lung cancer patients. Cancer-related features detected in serum-derived sEV were different than those of the corresponding whole serum. A high heterogeneity of lipid profiles of sEV was observed, which markedly impaired the performance of classification models based on specific compounds (the three-state classifiers showed an average AUC = 0.65 and 0.58 in the training and test subsets, respectively).

Aberrant sphingomyelin 31P-NMR signatures in giant cell tumour of bone

An understanding of the biochemistry of the giant cell tumour of bone (GCTB) provides an opportunity for the development of prognostic markers and identification of therapeutic targets. Based on metabolomic analysis, we proposed glycerophospholipid metabolism as the altered pathway in GCTB and the objective of this study was to identify these altered metabolites. Using phosphorus-31 nuclear magnetic resonance spectroscopy (³¹P-NMR), sphingomyelin was determined as the most dysregulated phospholipid in tissue samples from six patients with GCTB; subsequently, enzymes related to its biosynthesis and hydrolysis were examined using immunodetection techniques. High expression of sphingomyelin synthases 1 and 2, but low expression of neutral sphingomyelinase 2 (nSMase2), was found in GCTB tissues compared to non-neoplastic bone tissues. Sphingomyelin/ ceramide biosynthesis is dysregulated in GCTB due to alterations in the expression of SMS1, SMS2, and nSMase2.

Identification of novel diagnostic biomarkers in endometrial cancer using targeted metabolomic profiling

PURPOSE

Endometrial cancer (EC) is the most common gynecological malignancy with high disease burden especially in advanced stages of the disease. Our study investigated the metabolomic profile of EC patient's serum with the aim of identifying novel diagnostic biomarkers that could be used especially in early disease detection.

MATERIAL AND METHODS

Using targeted metabolomic serum profiling based on HPLC-TQ/MS, women with EC (n ​= ​15) and controls (n ​= ​21) were examined for 232 endogenous metabolites.

RESULTS

Top performing biomarkers included ceramides, acylcarnitines and 1-methyl adenosine. Top 4 biomarkers combined achieved 94% sensitivity with 75% specificity with AUC 92.5% (CI 90.5-94.5%). Individual markers also provided significant predictive values: C16-ceramide achieved sensitivity 73%, specificity 81%, AUC 0.83, C22-ceramide sensitivity 67%, specificity 81%, AUC 0.77, hydroxyhexadecenoylcarnitine sensitivity 60%, specificity 96%, AUC 0.76 and 1-methyladenosine sensitivity 67%, specificity 81%, AUC 0.75. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.

CONCLUSIONS

Using mass spectrometry targeted metabolomic profiling, ceramides, acylcarnitines and 1-methyladenosine were identified as potential diagnostic biomarkers for EC. Additionally, these identified metabolites may provide additional insight into cancer cell metabolism.

Sphingolipids as multifaceted mediators in ovarian cancer

Ovarian cancer is the most lethal gynaecological malignancy. It is commonly diagnosed at advanced stage when it has metastasised to the abdominal cavity and treatment becomes very challenging. While current standard therapy involving debulking surgery and platinum + taxane-based chemotherapy is associated with high response rates initially, the large majority of patients relapse and ultimately succumb to chemotherapy-resistant disease. In order to improve survival novel strategies for early detection and therapeutics against treatment-refractory disease are urgently needed. A promising new target against ovarian cancer is the sphingolipid pathway which is commonly hijacked in cancer to support cell proliferation and survival and has been shown to promote chemoresistance and metastasis in a wide range of malignant neoplasms. In particular, the sphingosine kinase 1-sphingosine 1-phosphate receptor 1 axis has been shown to be altered in ovarian cancer in multiple ways and therefore represents an attractive therapeutic target. Here we review the roles of sphingolipids in ovarian cancer progression, metastasis and chemoresistance, highlighting novel strategies to target this pathway that represent potential avenues to improve patient survival.

Circulating Lysophosphatidylcholines, Phosphatidylcholines, Ceramides, and Sphingomyelins and Ovarian Cancer Risk: A 23-Year Prospective Study

BACKGROUND

Experimental evidence supports a role of lipid dysregulation in ovarian cancer progression. We estimated associations with ovarian cancer risk for circulating levels of four lipid groups, previously hypothesized to be associated with ovarian cancer, measured 3-23 years before diagnosis.

METHODS

Analyses were conducted among cases (N = 252) and matched controls (N = 252) from the Nurses' Health Studies. We used logistic regression adjusting for risk factors to investigate associations of lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), ceramides (CERs), and sphingomyelins (SMs) with ovarian cancer risk overall and by histotype. A modified Bonferroni approach (0.05/4 = 0.0125, four lipid groups) and the permutation-based Westfall and Young approach were used to account for testing multiple correlated hypotheses. Odds ratios (ORs; 10th-90th percentile), and 95% confidence intervals of ovarian cancer risk were estimated. All statistical tests were two-sided.

RESULTS

SM sum was statistically significantly associated with ovarian cancer risk (OR = 1.97, 95% CI = 1.16 to 3.32; P = .01/permutation-adjusted P = .20). C16:0 SM, C18:0 SM, and C16:0 CERs were suggestively associated with risk (OR = 1.95-2.10; P = .004-.01; permutation-adjusted P = .08-.21). SM sum, C16:0 SM, and C16:0 CER had stronger odds ratios among postmenopausal women (OR = 2.16-3.22). Odds ratios were similar for serous/poorly differentiated and endometrioid/clear cell tumors, although C18:1 LPC and LPC to PC ratio were suggestively inversely associated, whereas C18:0 SM was suggestively positively associated with risk of endometrioid/clear cell tumors. No individual metabolites were associated with risk when using the permutation-based approach.

CONCLUSIONS

Elevated levels of circulating SMs 3-23 years before diagnosis were associated with increased risk of ovarian cancer, regardless of histotype, with stronger associations among postmenopausal women. Further studies are required to validate and understand the role of lipid dysregulation in ovarian carcinogenesis.

Identification of Novel Diagnostic Biomarkers in Breast Cancer Using Targeted Metabolomic Profiling

INTRODUCTION

Breast cancer (BC) is the most common cancer in women, with a high disease burden, especially in the advanced disease stages. Our study investigated the metabolomic profile of breast cancer patients' serum with the aim of identifying novel diagnostic biomarkers that could be used, especially for early disease detection.

MATERIALS AND METHODS

Using targeted metabolomic serum profiling based on high-performance liquid chromatography mass spectrometry, women with BC (n = 39) and a control group (n = 21) were examined for 232 endogenous metabolites.

RESULTS

The top performing biomarkers included acylcarnitines (ACs) and 9,12-linoleic acid. A combined panel of the top 4 biomarkers achieved 83% sensitivity and 81% specificity, with an area under the curve (AUC) of 0.839 (95% confidence interval, 0.811-0.867). Individual markers also provided significant predictive values: AC 12:0, sensitivity of 72%, specificity of 67%, and AUC of 0.71; AC 14:2, sensitivity of 74%, specificity of 71%, and AUC of 0.73; AC 14:0: sensitivity of 67%, specificity of 81%, and AUC of 0.73; and 9,12-linoleic acid, sensitivity of 69%, specificity of 67%, and AUC of 0.71. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination.

CONCLUSION

Using mass spectrometry-targeted metabolomic profiling, ACs and 9,12-linoleic acid were identified as potential diagnostic biomarkers for breast cancer. Additionally, these identified metabolites could provide additional insight into cancer cell metabolism.

Untargeted Metabolomic Characterization of Ovarian Tumors

Simple Summary

This study utilized untargeted metabolomic techniques to detect urine and plasma metabolites. Using support vector machine algorithm, three models for ovarian tumors diagnosis, benign-malignant distinguishing, early diagnosis and borderline-malignant distinguishing were developed. These models have good classification performance and provided a novel insight for non-invasive diagnosis of ovarian cancer.

Abstract

Diagnosis of ovarian cancer is difficult due to the lack of clinical symptoms and effective screening algorithms. In this study, we aim to develop models for ovarian cancer diagnosis by detecting metabolites in urine and plasma samples. Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive ion mode was used for metabolome quantification in 235 urine samples and 331 plasma samples. Then, Urine and plasma metabolomic profiles were analyzed by univariate and multivariate statistics. Four groups of samples: normal control, benign, borderline and malignant ovarian tumors were enrolled in this study. A total of 1330 features and 1302 features were detected from urine and plasma samples respectively. Based on two urine putative metabolites, five plasma putative metabolites and five urine putative metabolites, three models for distinguishing normal-ovarian tumors, benign-malignant (borderline + malignant) and borderline-malignant ovarian tumors were developed respectively. The AUC (Area Under Curve) values were 0.987, 0876 and 0.943 in discovery set and 0.984, 0.896 and 0.836 in validation set for three models. Specially, the diagnostic model based on 5 plasma putative metabolites had better early-stage diagnosis performance than CA125 alone. The AUC values of the model were 0.847 and 0.988 in discovery and validation set respectively. Our results showed that normal and ovarian tumors have unique metabolic signature in urine and plasma samples, which shed light on the ovarian cancer diagnosis and classification.

Dietary Patterns, Ceramide Ratios, and Risk of All-Cause and Cause-Specific Mortality: The Framingham Offspring Study

BACKGROUND

Prior evidence suggests that diet modifies the association of blood ceramides with the risk of incident cardiovascular disease (CVD). It remains unknown if diet quality modifies the association of very long-chain-to-long-chain ceramide ratios with mortality in the community.

OBJECTIVES

Our objectives were to determine how healthy dietary patterns associate with blood ceramide concentrations and to examine if healthy dietary patterns modify associations of ceramide ratios (C22:0/C16:0 and C24:0/C16:0) with all-cause and cause-specific mortality.

METHODS

We examined 2157 participants of the Framingham Offspring Study (mean age = 66 y, 55% women). Blood ceramides were quantified using a validated assay. We evaluated prospective associations of the Dietary Guidelines Adherence Index (DGAI) and Mediterranean-style Diet Score (MDS) with incidence of all-cause and cause-specific mortality using Cox proportional hazards models. Cross-sectional associations of the DGAI and MDS with ceramides were evaluated using multivariable linear regression models.

RESULTS

The C22:0/C16:0 and C24:0/C16:0 ceramide ratios were inversely associated with all-cause, CVD, and cancer mortality; multivariable-adjusted HRs (95% CIs) were 0.73 (0.67, 0.80) and 0.70 (0.63, 0.77) for all-cause mortality, 0.74 (0.60, 0.90) and 0.69 (0.55, 0.86) for CVD mortality, and 0.75 (0.65, 0.87) and 0.75 (0.64, 0.88) for cancer mortality, respectively. Inverse associations of the C22:0/C16:0 and C24:0/C16:0 ceramide ratios with cancer mortality were attenuated among individuals with a higher diet quality (DGAI or MDS above the median, all P-interaction ≤0.1). The DGAI and MDS had distinct associations with ceramide ratios (DGAI: lower C22:0/C16:0 across quartiles; MDS: higher C24:0/C16:0 across quartiles; all P-trend ≤0.01).

CONCLUSION

In our community-based sample, ceramide ratios (C22:0/C16:0 and C24:0/C16:0) were associated with a lower risk of all-cause and cause-specific mortality. Further, we observed that a higher overall diet quality attenuates the association between blood ceramide ratios and cancer mortality and that dietary patterns have distinct relations with ceramide ratios.

New insights into molecules and pathways of cancer metabolism and therapeutic implications

Cancer cells are abnormal cells that can reproduce and regenerate rapidly. They are characterized by unlimited proliferation, transformation and migration, and can destroy normal cells. To meet the needs for cell proliferation and migration, tumor cells acquire molecular materials and energy through unusual metabolic pathways as their metabolism is more vigorous than that of normal cells. Multiple carcinogenic signaling pathways eventually converge to regulate three major metabolic pathways in tumor cells, including glucose, lipid, and amino acid metabolism. The distinct metabolic signatures of cancer cells reflect that metabolic changes are indispensable for the genesis and development of tumor cells. In this review, we report the unique metabolic alterations in tumor cells which occur through various signaling axes, and present various modalities available for cancer diagnosis and clinical therapy. We further provide suggestions for the development of anti‐tumor therapeutic drugs.

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.

A Novel Serum Metabolomic Profile for the Differential Diagnosis of Distal Cholangiocarcinoma and Pancreatic Ductal Adenocarcinoma

The diagnosis of adenocarcinomas located in the pancreas head, i.e., distal cholangiocarcinoma (dCCA) and pancreatic ductal adenocarcinoma (PDAC), constitutes a clinical challenge because they share many symptoms, are not easily distinguishable using imaging techniques and accurate biomarkers are not available. Searching for biomarkers with potential usefulness in the differential diagnosis of these tumors, we have determined serum metabolomic profiles in healthy controls and patients with dCCA, PDAC or benign pancreatic diseases (BPD). Ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) analysis was performed in serum samples from dCCA (n = 34), PDAC (n = 38), BPD (n = 42) and control (n = 25) individuals, divided into discovery and validation cohorts. This approach permitted 484 metabolites to be determined, mainly lipids and amino acids. The analysis of the results led to the proposal of a logistic regression model able to discriminate patients with dCCA and PDAC (AUC value of 0.888) based on the combination of serum levels of nine metabolites (acylcarnitine AC(16:0), ceramide Cer(d18:1/24:0), phosphatidylcholines PC(20:0/0:0) and PC(O-16:0/20:3), lysophosphatidylcholines PC(20:0/0:0) and PC(0:0/20:0), lysophosphatidylethanolamine PE(P-18:2/0:0), and sphingomyelins SM(d18:2/22:0) and SM(d18:2/23:0)) and CA 19-9. In conclusion, we propose a novel specific panel of serum metabolites that can help in the differential diagnosis of dCCA and PDAC. Further validation of their clinical usefulness in prospective studies is required.

Role of Ceramides in Drug Delivery

Ceramides belong to the sphingolipid group of lipids, which serve as both intracellular and intercellular messengers and as regulatory molecules that play essential roles in signal transduction, inflammation, angiogenesis, and metabolic disorders such as diabetes, neurodegenerative diseases, and cancer cell degeneration. Ceramides also play an important structural role in cell membranes by increasing their rigidity, creating micro-domains (rafts and caveolae), and altering membrane permeability; all these events are involved in the cell signaling. Ceramides constitute approximately half of the lipid composition in the human skin contributing to barrier function as well as epidermal signaling as they affect both proliferation and apoptosis of keratinocytes. Incorporation of ceramides in topical preparations as functional lipids appears to alter skin barrier functions. Ceramides also appear to enhance the bioavailability of drugs by acting as lipid delivery systems. They appear to regulate the ocular inflammation signaling, and external ceramides have shown relief in the anterior and posterior eye disorders. Ceramides play a structural role in liposome formulations and enhance the cellular uptake of amphiphilic drugs, such as chemotherapies. This review presents an overview of the various biological functions of ceramides, and their utility in topical, oral, ocular, and chemotherapeutic drug delivery.

Body Mass Index in Multiple Sclerosis modulates ceramide-induced DNA methylation and disease course

BACKGROUND

Multiple Sclerosis (MS) results from genetic predisposition and environmental variables, including elevated Body Mass Index (BMI) in early life. This study addresses the effect of BMI on the epigenome of monocytes and disease course in MS.

METHODS

Fifty-four therapy-naive Relapsing Remitting (RR) MS patients with high and normal BMI received clinical and MRI evaluation. Blood samples were immunophenotyped, and processed for unbiased plasma lipidomic profiling and genome-wide DNA methylation analysis of circulating monocytes. The main findings at baseline were validated in an independent cohort of 91 therapy-naïve RRMS patients. Disease course was evaluated by a two-year longitudinal follow up and mechanistic hypotheses tested in human cell cultures and in animal models of MS.

FINDINGS

Higher monocytic counts and plasma ceramides, and hypermethylation of genes involved in negative regulation of cell proliferation were detected in the high BMI group of MS patients compared to normal BMI. Ceramide treatment of monocytic cell cultures increased proliferation in a dose-dependent manner and was prevented by DNA methylation inhibitors. The high BMI group of MS patients showed a negative correlation between monocytic counts and brain volume. Those subjects at a two-year follow-up showed increased T1 lesion load, increased disease activity, and worsened clinical disability. Lastly, the relationship between body weight, monocytic infiltration, DNA methylation and disease course was validated in mouse models of MS.

INTERPRETATION

High BMI negatively impacts disease course in Multiple Sclerosis by modulating monocyte cell number through ceramide-induced DNA methylation of anti-proliferative genes. FUND: This work was supported by funds from the Friedman Brain Institute, NIH, and Multiple Sclerosis Society.

Quantitative assessment of serum heat shock protein 27 for the diagnosis of epithelial ovarian cancer using targeted proteomics coupled with immunoaffinity enrichment

BACKGROUND

Heat shock protein 27 (HSP27) may take part in the epithelial ovarian cancer (EOC) malignant process because it is elevated in the serum of EOC patients, suggesting that HSP27 may serve as an EOC biomarker to complement the standard serum carbohydrate antigen 125 (CA125) test. Thus, accurate quantification of serum HSP27 would assist the diagnosis of EOC.

METHODS

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based targeted proteomics coupled with an immunoaffinity enrichment assay was developed and validated to monitor HSP27 concentrations in serum.

RESULTS

Tryptic peptide 80QLSSGVSEIR89 was selected as a surrogate analyte for quantification, and an immuno-depleted serum extract was used as a surrogate matrix. Immunoaffinity enrichment was effective for protein enrichment and sensitivity enhancement, and the resulting LOQ was 500 pg/ml (>10-fold increase). Then, serum HSP27 concentrations in EOC patients, benign ovarian tumors patients and healthy volunteers were accurately determined to be 4.95 ± 0.37 ng/ml, 2.98 ± 0.16 ng/ml and 2.82 ± 0.15 ng/ml, respectively, suggesting that the EOC samples had significantly higher concentrations of HSP27 than a sample from benign ovarian tumor patients. The experimental values for the samples were compared with those obtained from enzyme-linked immune sorbent assays (ELISAs). The ROC curve analysis showed that the combined area under the curve (AUC) for CA125 and HSP27 was 0.88, which is significantly superior to that of CA125 alone.

CONCLUSIONS

Targeted proteomics coupled with immunoaffinity enrichment may provide more accurate quantification of low-abundant proteins.

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.

Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers

The data presented here are related to the research paper entitled “Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer.” (Kozar et al., 2018) [1]. Metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign gynecological conditions. HPLC-TQ/MS was performed on all samples. PLS-DA was used for the first line classification of epithelial ovarian cancer and healthy control group based on metabolomic profiles. Random forest algorithm was used for building a prediction model based over most significant markers. Univariate analysis was performed on individual markers to determine their distinctive roles. Furthermore, markers were also evaluated for their biological significance in cancer progression.