Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

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.

Multi-omic assessment shows dysregulation of pulmonary and systemic immunity to e-cigarette exposure

Electronic cigarette (Ecig) use has become more common, gaining increasing acceptance as a safer alternative to tobacco smoking. However, the 2019 outbreak of Ecig and Vaping-Associated Lung Injury (EVALI) alerted the community to the potential for incorporation of deleterious ingredients such as vitamin E acetate into products without adequate safety testing. Understanding Ecig induced molecular changes in the lung and systemically can provide a path to safety assessment and protect consumers from unsafe formulations. While vitamin E acetate has been largely removed from commercial and illicit products, many Ecig products contain additives that remain largely uncharacterized. In this study, we determined the lung-specific effects as well as systemic immune effects in response to exposure to a common Ecig base, propylene glycol and vegetable glycerin (PGVG), with and without a 1% addition of phytol, a diterpene alcohol that has been found in commercial products. We exposed animals to PGVG with and without phytol and assessed metabolite, lipid, and transcriptional markers in the lung. We found both lung-specific as well as systemic effects in immune parameters, metabolites, and lipids. Phytol drove modest changes in lung function and increased splenic CD4 T cell populations. We also conducted multi-omic data integration to better understand early complex pulmonary responses, highlighting a central enhancement of acetylcholine responses and downregulation of palmitic acid connected with conventional flow cytometric assessments of lung, systemic inflammation, and pulmonary function. Our results demonstrate that Ecig exposure not only leads to changes in pulmonary function but also affects systemic immune and metabolic parameters.

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.

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.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis

Ovarian cancer (OC) is a heterogeneous disease with the highest mortality rate and the poorest prognosis among gynecological malignancies. Because of the absence of specific early symptoms, most OC patients are often diagnosed at late stages. Thus, improved biomarkers of OC for use in research and clinical practice are urgently needed. The last decade has seen increasingly rapid advances in sequencing and biotechnological methodologies. Consequently, multiple omics technologies, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra, have been widely applied to analyze tissue- and liquid-derived samples from OC patients. The integration of multi-omics data has increased our knowledge of the disease and identified valuable OC biomarkers. In this review, we summarize the recent advances and perspectives in the use of multi-omics technologies in OC research and highlight potential applications of multi-omics for identifying novel biomarkers and improving clinical assessments.

Lipid Alterations in Early-Stage High-Grade Serous Ovarian Cancer

Epithelial ovarian cancer (OC) ranks first in the number of deaths among diseases of the female reproductive organs. Identification of OC at early stages is highly beneficial for the treatment but is highly challenging due to the asymptomatic or low-symptom disease development. In this study, lipid extracts of venous blood samples from 41 female volunteers, including 28 therapy-naive patients with histologically verified high-grade serous ovarian cancer at different stages (5 patients with I-II stages; 23 patients with III-IV stages) and 13 apparently healthy women of reproductive age, were profiled by high-performance liquid chromatography mass spectrometry (HPLC-MS). Based on MS signals of 128 differential lipid species with statistically significant level variation between the OC patients and control group, an OPLS-DA model was developed for the recognition of OC with 100% sensitivity and specificity R2 = 0.87 and Q2 = 0.80. The second OPLS-DA model was developed for the differentiation between I-II OC stages and control group with R2 = 0.97 and Q2 = 0.86 based on the signal levels of 108 differential lipid species. The third OPLS-DA model was developed for the differentiation between I-II OC stages and III-IV stages based on the signal levels of 99 differential lipid species. Various lipid classes (diglycerides, triglycerides, phosphatidylchlorines, ethanolamines, sphingomyelins, ceramides, phosphatidylcholines and phosphoinositols) in blood plasma samples display distinctly characteristic profiles in I-II OC, which indicates the possibility of their use as marker oncolipids in diagnostic molecular panels of early OC stages. Our results suggest that lipid profiling by HPLC-MS can improve identification of early-stage OC and thus increase the efficiency of treatment.

Microbiome and metabolome profiles of high screen time in a cohort of healthy college students

As screens are increasingly integrated into every facet of modern life, there is growing concern over the potential effects of high screen time. Previous studies have largely utilized self-report data on mood and behavioral aspects of screen time, and no molecular theory has yet been developed. In this study, we explored the fecal microbiome and metabolome of a diverse group of 60 college students, classified by high (≥ 75 min/day) or low (0–75 min/day) self-reported screen time using 16S rRNA amplicon sequencing, targeted liquid chromatography-tandem mass spectrometry, and targeted detection of short-chain fatty acids using gas chromatography-mass spectrometry. Several key taxa and metabolites were significantly altered between groups and found to be highly co-occurrent. Results of pathway and enzyme enrichment analyses were synthesized to articulate an integrated hypothesis indicating widespread mitochondrial dysfunction and aberrant amino acid metabolism. High screen time was also predicted to be significantly associated with type I diabetes, obesity, chronic fatigue syndrome, and various manifestations of inflammatory bowel. This is the first-ever study to report the effects of high screen time at the molecular level, and these results provide a data-driven hypothesis for future experimental research.

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.

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.

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Objective

Prostate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.

Methods

A total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.

Results

Several differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).

Conclusion

The findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

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

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

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.

Lipidomic Analysis of Archival Pathology Specimens Identifies Altered Lipid Signatures in Ovarian Clear Cell Carcinoma

Cancer metabolism is associated with the enhanced lipogenesis required for rapid growth and proliferation. However, the magnitude of dysregulation of diverse lipid species still requires significant characterization, particularly in ovarian clear cell carcinoma (OCCC). Here, we have implemented a robust sample preparation workflow together with targeted LC-MS/MS to identify the lipidomic changes in formalin-fixed paraffin-embedded specimens from OCCC compared to tumor-free ovarian tissue. We quantitated 340 lipid species, representing 28 lipid classes. We observed differential regulation of diverse lipid species belonging to several glycerophospholipid classes and trihexosylceramide. A number of unsaturated lipid species were increased in OCCC, whereas saturated lipid species showed a decrease in OCCC compared to the controls. We also carried out total fatty acid analysis and observed an increase in the levels of several unsaturated fatty acids with a concomitant increase in the index of stearoyl-CoA desaturase (SCD) in OCCC. We confirmed the upregulation of SCD (the rate-limiting enzyme for the synthesis of monounsaturated fatty acids) by immunohistochemistry (IHC) assays. Hence, by carrying out a mass spectrometry analysis of archival tissue samples, we were able to provide insights into lipidomic alterations in OCCC.

Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review

Gynecological and breast cancers still remain a significant health problem worldwide. Diagnostic methods are not sensitive and specific enough to detect the disease at an early stage. During carcinogenesis and tumor progression, the cellular need for DNA and protein synthesis increases leading to changes in the levels of amino acids. An important role of amino acids in many biological pathways, including biosynthesis of proteins, nucleic acids, enzymes, etc., which serve as an energy source and maintain redox balance, has been highlighted in many research articles. The aim of this review is a detailed analysis of the literature on metabolomic studies of gynecology and breast cancers with particular emphasis on alterations in free amino acid profiles. The work includes a brief overview of the metabolomic methodology and types of biological samples used in the studies. Special attention was paid to the possible role of selected amino acids in the carcinogenesis, especially proline and amino acids related to its metabolism. There is a clear need for further research and multiple external validation studies to establish the role of amino acid profiling in diagnosing gynecological and breast cancers.

Application of Comparative Lipidomics to Elucidate Postprandial Metabolic Excursions Following Dairy Milk Ingestion in Individuals with Prediabetes

Nutrient-dense dairy foods are an important component of a healthy diet. Recommendations, however, advise non- and low-fat dairy foods despite controversy concerning whether full-fat dairy foods adversely impact cardiometabolic health. Therefore, in this study, our objective is to examine the differential plasma lipidomic responses to non-fat or full-fat milk ingestion during postprandial hyperglycemia. Seven adults with prediabetes completed a randomized cross-over study in which glucose was consumed alone or with non-fat or full-fat dairy milk. Plasma samples collected at 90 min and 180 min post milk ingestion were used to perform untargeted lipidomics analysis. A total of 332 lipids from 20 classes and five lipid categories were detected at different time points during the postprandial period. Dairy milk, especially non-fat milk, protected against lipid changes otherwise induced by glucose ingestion. Co-ingestion of dairy milk with glucose, regardless of fat content, significantly altered lipid profiles although full-fat milk more substantially modulated lipid profiles. For the identified lipid biomarkers, 68.0% and 66.7% of the lipids significantly increased at 90 and 180 min, respectively, while phosphatidylcholines (GPs) contributed most for the significant increase. Comparative lipidomics analysis indicated that both types of dairy milk induced significant changes in several lipid pathways, including glycerophospholipid metabolism and α-linolenic acid metabolism, to protect against postprandial hyperglycemia. In summary, our comparative lipidomics results suggested that dairy milk-mediated lipid modulation may be an effective dietary approach to reduce the risk of metabolic diseases among those with prediabetes.

Serum Free Amino Acid Profiling in Differential Diagnosis of Ovarian Tumors-A Comparative Study with Review of the Literature

Proper preoperative ovarian cancer (OC) diagnosis remains challenging. Serum free amino acid (SFAA) profiles were investigated to identify potential novel biomarkers of OC and assess their performance in ovarian tumor differential diagnosis. Serum samples were divided based on the histopathological result: epithelial OC (n = 38), borderline ovarian tumors (n = 6), and benign ovarian tumors (BOTs) (n = 62). SFAA profiles were evaluated using aTRAQ methodology based on high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). Levels of eleven amino acids significantly differed between OC+borderline and BOTs. The highest area under the receiver operating characteristic curve (AUC of ROC) (0.787) was obtained for histidine. Cystine and histidine were identified as best single markers for early stage OC/BOT and type I OC. For advanced stage OC, seven amino acids differed significantly between the groups and citrulline obtained the best AUC of 0.807. Between type II OC and BOTs, eight amino acids differed significantly and the highest AUC of 0.798 was achieved by histidine and citrulline (AUC of 0.778). Histidine was identified as a potential new biomarker in differential diagnosis of ovarian tumors. Adding histidine to a multimarker panel together with CA125 and HE4 improved the differential diagnosis between OC and BOTs.

A MYC-Driven Plasma Polyamine Signature for Early Detection of Ovarian Cancer

MYC is an oncogenic driver in the pathogenesis of ovarian cancer. We previously demonstrated that MYC regulates polyamine metabolism in triple-negative breast cancer (TNBC) and that a plasma polyamine signature is associated with TNBC development and progression. We hypothesized that a similar plasma polyamine signature may associate with ovarian cancer (OvCa) development. Using mass spectrometry, four polyamines were quantified in plasma from 116 OvCa cases and 143 controls (71 healthy controls + 72 subjects with benign pelvic masses) (Test Set). Findings were validated in an independent plasma set from 61 early-stage OvCa cases and 71 healthy controls (Validation Set). Complementarity of polyamines with CA125 was also evaluated. Receiver operating characteristic area under the curve (AUC) of individual polyamines for distinguishing cases from healthy controls ranged from 0.74-0.88. A polyamine signature consisting of diacetylspermine + N-(3-acetamidopropyl)pyrrolidin-2-one in combination with CA125 developed in the Test Set yielded improvement in sensitivity at >99% specificity relative to CA125 alone (73.7% vs 62.2%; McNemar exact test 2-sided P: 0.019) in the validation set and captured 30.4% of cases that were missed with CA125 alone. Our findings reveal a MYC-driven plasma polyamine signature associated with OvCa that complemented CA125 in detecting early-stage ovarian cancer.

Lipid Metabolism and Lipidomics Applications in Cancer Research

Lipids are the critical components of cellular and plasma membrane, which constitute an impermeable barrier of cellular compartments, and play important roles on numerous cellular processes including cell growth, proliferation, differentiation, and signaling. Alterations in lipid metabolism have been implicated in the development and progression of cancers. However, unlike other biomolecules, the diversity in the structures and characteristics of lipid species results in the limited understanding of their metabolic alterations in cancers. Lipidomics is an emerging discipline that studies lipids in a large scale based on analytical chemistry principles and technological tools. Multidimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) uses direct infusion to avoid difficulties from alterations in concentration, chromatographic anomalies, and ion-pairing alterations to improve resolution and achieve rapid and accurate qualitative and quantitative analysis. In this chapter, lipids and lipid metabolism relevant to cancer research are introduced, followed by a brief description of MDMS-SL and other shotgun lipidomics techniques and some applications for cancer research.

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.

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

Simple Summary

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

Abstract

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

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

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

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

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

High-throughput approaches for precision medicine in high-grade serous ovarian cancer

High-grade serous carcinoma (HGSC) is the most prevalent and aggressive subtype of ovarian cancer. The large degree of clinical heterogeneity within HGSC has justified deviations from the traditional one-size-fits-all clinical management approach. However, the majority of HGSC patients still relapse with chemo-resistant cancer and eventually succumb to their disease, evidence that further work is needed to improve patient outcomes. Advancements in high-throughput technologies have enabled novel insights into biological complexity, offering a large potential for informing precision medicine efforts. Here, we review the current landscape of clinical management for HGSC and highlight applications of high-throughput biological approaches for molecular subtyping and the discovery of putative blood-based biomarkers and novel therapeutic targets. Additionally, we present recent improvements in model systems and discuss how their intersection with high-throughput platforms and technological advancements is positioned to accelerate the realization of precision medicine in HGSC.

Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage

Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.

Triple Negative Breast Cancer Detection Using LC-MS/MS Lipidomic Profiling

Breast cancer (BC) is a heterogeneous malignancy that is responsible for a great portion of female cancer cases and cancer-related deaths in the United States. In comparison to other major BC subtypes, triple negative breast cancer (TNBC) presents with a relatively low survival rate and a high rate of metastasis. This has led to a strong, though largely unmet, need for more sensitive and specific methods of early-stage TNBC (ES-TNBC) detection to combat its high-grade pathology and relatively low survival rate. The current study employs a liquid chromatography-tandem mass spectrometry assay capable of targeted, highly specific, and sensitive detection of lipids to propose two diagnostic biomarker panels for TNBC/ES-TNBC. Using this approach, 110 lipids were reliably detected in 166 human plasma samples, 45 controls, and 121 BC (96 non-TNBC and 25 TNBC) subjects. Univariate and multivariate analyses allowed the construction and application of a 19-lipid biomarker panel capable of distinguishing TNBC (and ES-TNBC) from controls, as well as a 5-lipid biomarker panel capable of differentiating TNBC from non-TNBC and ES-TNBC from ES-non-TNBC. Receiver operating characteristic curves with notable classification performances were generated from the biomarker panels according to their orthogonal partial least-squares discrimination analysis models. TNBC was distinguished from controls with an area under the receiving operating characteristic curve (AUROC) = 0.93, sensitivity = 0.96, and specificity = 0.76 and ES-TNBC from controls with an AUROC = 0.96, sensitivity = 0.95, and specificity = 0.89. TNBC was differentiated from non-TNBC with an AUROC = 0.88, sensitivity = 0.88, and specificity = 0.79 and ES-TNBC from ES-non-TNBC with an AUROC = 0.95, sensitivity = 0.95, and specificity = 0.87. A pathway enrichment analysis between TNBC and controls also revealed significant disturbances in choline metabolism, sphingolipid signaling, and glycerophospholipid metabolism. To the best of our knowledge, this is the first study to propose a diagnostic lipid biomarker panel for TNBC detection. All raw mass spectrometry data have been deposited to MassIVE (dataset identifier MSV000085324).

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.

Surfactant Lipidomics of Alveolar Lavage Fluid in Mice Based on Ultra-High-Performance Liquid Chromatography Coupled to Hybrid Quadrupole-Exactive Orbitrap Mass Spectrometry

Surfactant lipid metabolism is closely related to pulmonary diseases. Lipid metabolism disorder can cause lung diseases, vice versa. With this rationale, a useful method was established in this study to determine the lipidome in bronchoalveolar lavage fluid (BALF) of mice. The lipid components in BALF were extracted by liquid-liquid extraction (methanol and methyl tert-butyl ether, and water). Ultra-high-performance liquid chromatography coupled to hybrid Quadrupole-Exactive Orbitrap mass spectrometry was used to analyze the extracted samples, which showed a broad scanning range of 215-1800 m/z. With MS-DIAL software and built-in LipidBlast database, we identified 38 lipids in positive, and 31 lipids in negative, ion mode, including lysophosphatidylcholine (lysoPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), etc. Then, the changes of lipids in BALF of mice with acute lung injury (ALI) induced by lipopolysaccharide (LPS) was investigated, which may contribute to further exploration of the pathogenesis of ALI.

Ovarian tumours of different histologic type and clinical stage induce similar changes in lipid metabolism

Background

Previous results obtained from serum samples of late-stage, high-grade serous ovarian carcinoma patients showed large alterations in lipid metabolism. To validate and extend the results, we studied lipidomic changes in early-stage ovarian tumours. In addition to serous ovarian cancer, we investigated whether these changes occur in mucinous and endometrioid histological subtypes as well.

Methods

Altogether, 354 serum or plasma samples were collected from three centres, one from Germany and two from Finland. We performed lipidomic analysis of samples from patients with malignant (N = 138) or borderline (N = 25) ovarian tumours, and 191 controls with benign pathology. These results were compared to previously published data.

Results

We found 39 lipids that showed consistent alteration both in early- and late-stage ovarian cancer patients as well as in pre- and postmenopausal women. Most of these changes were already significant at an early stage and progressed with increasing stage. Furthermore, 23 lipids showed similar alterations in all investigated histological subtypes.

Conclusions

Changes in lipid metabolism due to ovarian cancer occur in early-stage disease but intensify with increasing stage. These changes occur also in other histological subtypes besides high-grade serous carcinoma. Understanding lipid metabolism in ovarian cancer may lead to new therapeutic and diagnostic alternatives.

Salivary metabolite profiling distinguishes patients with oral cavity squamous cell carcinoma from normal controls

Oral cavity squamous cell carcinoma (OCC) and oropharyngeal squamous cell carcinoma (OPC) are among the most common cancers worldwide and are associated with high mortality and morbidity. The purpose of this study is to identify potential biomarkers to distinguish OCC/OPC from normal controls and to distinguish OCC patients with and without nodal metastasis. We tested saliva samples from 101 OCC, 58 OPC, and 35 normal controls using four analytical platforms (NMR, targeted aqueous by LC-MS/MS, global aqueous and global lipidomics by LC-Q-TOF). Samples from OCC and normal controls were divided into discovery and validation sets. Using linear regression adjusting for age, sex, race and experimental batches, we found the levels of two metabolites (glycine and proline) to be significantly different between OCC and controls (FDR < 0.1 for both discovery and validation sets) but did not find any appreciable differences in metabolite levels between OPC and controls or between OCC with and without nodal metastasis. Four metabolites, including glycine, proline, citrulline, and ornithine were associated with early stage OCC in both discovery and validation sets. Further study is warranted to confirm these results in the development of salivary metabolites as diagnostic markers.

Serum Lipidomics Profiling to Identify Biomarkers for Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer death worldwide, which ranks top in both incidence and mortality. To broaden our understanding of the lipid metabolic alterations in NSCLC and to identify potential biomarkers for early diagnosis, we performed nontargeted lipidomics analysis in serum from 66 early-stage NSCLC, 40 lung benign disease patients (LBD), and 40 healthy controls (HC) using Ultrahigh Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-Q-TOF/MS). The identified biomarker candidates of phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs) were further externally validated in a cohort including 30 early-stage NSCLC, 30 LBD, and 30 HC by a targeted lipidomic analysis. We observed a significantly altered lipid metabolic profile in early-stage NSCLC and identified panels of PCs and PEs to distinguish NSCLC patients and HC. The levels of PCs and PEs were found to be dysregulated in glycerophospholipid metabolism, which was the top altered pathway in early-stage NSCLC. Receiver operating characteristic (ROC) curve analysis revealed that panels of PCs and PEs exhibited good performance in differentiating early-stage NSCLC and HC. The levels of PE(16:0/16:1), PE(16:0/18:3), PE(16:0/18:2), PE(18:0/16:0), PE(17:0/18:2), PE(18:0/17:1), PE(17:0/18:1), PE(20:5/16:0), PE(18:0/18:1), PE(18:1/20:4), PE(18:0/20:3), PC(15:0/18:1), PC(16:1/20:5), and PC(18:0/20:1) in early-stage NSCLC were significantly increased compared with HC (p<0.05). Overall, our study has thus highlighted the power of using comprehensive lipidomic approaches to identify biomarkers and underlying mechanisms in NSCLC.

Alterations of eicosanoids and related mediators in patients with schizophrenia

Schizophrenia (SCZ) is a multifactorial psychiatric disorder. Currently, its molecular pathogenesis remains largely unknown, and no reliable test for diagnosis and therapy monitoring is available. Polyunsaturated fatty acids (PUFAs) and their derived eicosanoid signaling abnormalities are relevant to the pathophysiology of schizophrenia. However, comprehensive analysis of eicosanoids and related mediators for schizophrenia is very rare. In this study, we applied a targeted liquid chromatography-mass spectrometry based method to monitor 158 PUFAs, eicosanoids and related mediators from enzyme-dependent or independent pathways, in the serum samples of 109 healthy controls, and 115 schizophrenia patients at baseline and after an 8-week period of antipsychotic therapy. Twenty-three metabolites were identified to be significantly altered in SCZ patients at baseline compared to healthy controls, especially arachidonic acid (AA) derived eicosanoids. These disturbances may be related to altered immunological reactions and neurotransmitter signaling. After 8-week antipsychotic treatment, there were 22 metabolites, especially AA and linoleic acid derived eicosanoids, significantly altered in posttreatment patients. Some metabolites, such as several AA derived prostaglandins, thromboxanes, and di-hydroxy-eicosatrienoic acids were reversed toward normal levels after treatment. Based on univariate analysis and orthogonal partial least-squares discriminant analysis, anandamide, oleoylethanolamine, and AA were selected as a panel of potential biomarkers for differentiating baseline SCZ patients from controls, which showed a high sensitivity (0.907), good specificity (0.843) and excellent area under the receiver operating characteristic curve (0.940). This study provided a new perspective to understand the pathophysiological mechanism and identify potential biomarkers of SCZ.

Multiplatform Metabolomics Investigation of Antiadipogenic Effects on 3T3-L1 Adipocytes by a Potent Diarylheptanoid

Obesity is fast becoming a serious health problem worldwide. Of the many possible antiobesity strategies, one interesting approach focuses on blocking adipocyte differentiation and lipid accumulation to counteract the rise in fat storage. However, there is currently no drug available for the treatment of obesity that works by inhibiting adipocyte differentiation. Here we use a broad-based metabolomics approach to interrogate and better understand metabolic changes that occur during adipocyte differentiation. In particular, we focus on changes induced by the antiadipogenic diarylheptanoid, which was isolated from a traditional Chinese medicine Dioscorea zingiberensis and identified as (3 R,5 R)-3,5-dihydroxy-1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-heptane (1). Targeted aqueous metabolic profiling indicated that a total of 14 metabolites involved in the TCA cycle, glycolysis, amino acid metabolism, and purine catabolism participate in regulating energy metabolism, lipogenesis, and lipolysis in adipocyte differentiation and can be modulated by diarylheptanoid 1. As indicated by lipidomics analysis, diarylheptanoid 1 restored the quantity and degree of unsaturation of long-chain free fatty acids and restored the levels of 171 lipids mainly from 10 lipid classes in adipocytes. In addition, carbohydrate metabolism in diarylheptanoid-1-treated adipocytes further demonstrated the delayed differentiation process by flux analysis. Our results provide valuable information for further understanding the metabolic adjustment in adipocytes subjected to diarylheptanoid 1 treatment. Moreover, this study offers new insight into developing antiadipogenic leading compounds based on metabolomics.

High-resolution lipidomics reveals dysregulation of lipid metabolism in respiratory syncytial virus pneumonia mice

Respiratory syncytial virus (RSV) is a leading viral pathogen responsible for lower respiratory tract infections, particularly in children under five years worldwide, often resulting in hospitalization. At present, the molecular-level interactions between RSV and its host and the underlying mechanisms of RSV-induced inflammation are poorly understood. Herein, we describe an untargeted high-resolution lipidomics platform based on UHPLC-Q-Exactive-MS to assess the lipid alterations of lung tissues and plasma from a mouse model of RSV pneumonia. Untargeted lipidomics using LC-MS with multivariate analysis was applied to describe the lipidomic profiling of the lung tissues and plasma in RSV pneumonia mice. Lipid identification was conducted via an in silico MS/MS LipidBlast library using the MS-DIAL software. We observed distinct compartmental lipid signatures in the mice lung tissues and plasma and significant lipid profile changes between the systematic and localized host responses to RSV. A total of 87 and 68 differential lipids were captured in the mice lung tissue and plasma, respectively, including phospholipids, sphingolipids, acylcarnitine, and fatty acids. Some of these lipids belong to pulmonary surfactants, illustrating that RSV pneumonia-induced aberrations of the pulmonary surfactant system may play a vital role in the etiology of respiratory inflammation. Our findings reveal that the host responses to RSV and various lipid metabolic pathways were linked to disease pathology. Furthermore, our findings could provide mechanistic insights into RSV pneumonia.

Respiratory syncytial virus (RSV) is a leading viral pathogen responsible for lower respiratory tract infections, particularly in children under five years worldwide, often resulting in hospitalization.

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.

Comparison of hepatic and serum lipid signatures in hepatocellular carcinoma patients leads to the discovery of diagnostic and prognostic biomarkers

We compared hepatic and serum lipid changes in hepatocellular carcinoma (HCC) patients to have a better understanding of the molecular pathogenesis of this disease and discovery novel lipid biomarkers. Hepatic and serum lipid profiling was conducted in paired liver and serum samples from 50 HCC patients and 24 healthy controls. A total of 20 hepatic and 40 serum lipid signatures were identified, yet there was hardly any significant correlation between them. The results indicated that triglycerides and phosphatidylcholines contributed significantly to altered hepatic lipids, whereas triglycerides and phosphatidylethanolamine-based plasmalogens (PEp) contributed most to altered serum lipids. In serum, PEp (36:4) and (40:6) showed a fair capability to discriminate HCC patients from healthy controls, and were significantly associated with HCC tumor grades (p < 0.05), and thus were identified as potential diagnostic and prognostic biomarkers of HCC. These findings were confirmed by a validation study conducted in an independent cohort consisting of 18 HCC, 20 cirrhosis patients, and 20 healthy controls. This study suggests that hepatic and serum lipid signatures of HCC have to be considered as mostly independent, and the results imply potential roles of PEp species, particularly PEp (36:4) and (40:6), as serum biomarkers for HCC diagnosis and progression.

High-grade ovarian serous carcinoma patients exhibit profound alterations in lipid metabolism

Ovarian cancer is a very severe type of disease with poor prognosis. Treatment of ovarian cancer is challenging because of the lack of tests for early detection and effective therapeutic targets. Thus, new biomarkers are needed for both diagnostics and better understanding of the cellular processes of the disease. Small molecules, consisting of metabolites or lipids, have shown emerging potential for ovarian cancer diagnostics. Here we performed comprehensive lipidomic profiling of serum and tumor tissue samples from high-grade serous ovarian cancer patients to find lipids that were altered due to cancer and also associated with progression of the disease. Ovarian cancer patients exhibited an overall reduction of most lipid classes in their serum as compared to a control group. Despite the overall reduction, there were also specific lipids showing elevation, and especially alterations in ceramide and triacylglycerol lipid species were dependent on their fatty acyl side chain composition. Several lipids showed progressive alterations in patients with more advanced disease and poorer overall survival, and outperformed CA-125 as prognostic markers. The abundance of many serum lipids correlated with their abundance in tumor tissue samples. Furthermore, we found a negative correlation of serum lipids with 3-hydroxybutyric acid, suggesting an association between decreased lipid levels and fatty acid oxidation. In conclusion, here we present a comprehensive analysis of lipid metabolism alterations in ovarian cancer patients, with clinical implications.

Primary Metabolism and Medium-Chain Fatty Acid Alterations Precede Long-Chain Fatty Acid Changes Impacting Neutral Lipid Metabolism in Response to an Anticancer Lysophosphatidylcholine Analogue in Yeast

The nonmetabolizable lysophosphatidylcholine (LysoPC) analogue edelfosine is the prototype of a class of compounds being investigated for their potential as selective chemotherapeutic agents. Edelfosine targets membranes, disturbing cellular homeostasis. Is not clear at this point how membrane alterations are communicated between intracellular compartments leading to growth inhibition and eventual cell death. In the present study, a combined metabolomics/lipidomics approach for the unbiased identification of metabolic pathways altered in yeast treated with sublethal concentrations of the LysoPC analogue was employed. Mass spectrometry of polar metabolites, fatty acids, and lipidomic profiling was used to study the effects of edelfosine on yeast metabolism. Amino acid and sugar metabolism, the Krebs cycle, and fatty acid profiles were most disrupted, with polar metabolites and short-medium chain fatty acid changes preceding long and very long-chain fatty acid variations. Initial increases in metabolites such as trehalose, proline, and γ-amino butyric acid with a concomitant decrease in metabolites of the Krebs cycle, citrate and fumarate, are interpreted as a cellular attempt to offset oxidative stress in response to mitochondrial dysfunction induced by the treatment. Notably, alanine, inositol, and myristoleic acid showed a steady increase during the period analyzed (2, 4, and 6 h after treatment). Of importance was the finding that edelfosine induced significant alterations in neutral glycerolipid metabolism resulting in a significant increase in the signaling lipid diacylglycerol.

Comparative metabolomics revealing Staphylococcus aureus metabolic response to different antibiotics

It is known that changes in bacterial metabolism can contribute to the modulation of bacterial susceptibility to antibiotics. Understanding how bacterial metabolism is impacted by antibiotics may improve our understanding of the antibiotic mechanism of actions from a metabolic perspective. Here, we utilized a mass spectrometry‐based targeted metabolic profiling technique to characterize the metabolome of a pair of isogenic methicillin‐susceptible and resistant Staphylococcus aureus (MSSA and MRSA) strains RN450 and 450M treated with the sublethal dose of three antibiotics from different classes (β‐lactams, aminoglycosides and quinolones). These treatments induced a set of metabolic alterations after 6 h of co‐incubation with antibiotics. Similar and divergent metabolic perturbations were observed from different antibiotics to the tested strains. Different metabolic response from MSSA and MRSA to the same antibiotics was also detected in the study and indicated the potentially different stress response mechanism in MSSA and MRSA metabolism. This work has shown that a complex set of metabolic changes can be induced by a variety of antibiotics, and the comparative metabolomics strategy can provide a good understanding of this process from a metabolic perspective.

Hyphenated MS-based targeted approaches in metabolomics

Review of targeted metabolomics, with a focus on the description of analytical methods.

Cardiolipins Are Biomarkers of Mitochondria-Rich Thyroid Oncocytic Tumors

Oncocytic tumors are characterized by an excessive eosinophilic, granular cytoplasm due to aberrant accumulation of mitochondria. Mutations in mitochondrial DNA occur in oncocytic thyroid tumors, but there is no information about their lipid composition, which might reveal candidate theranostic molecules. Here, we used desorption electrospray ionization mass spectrometry (DESI-MS) to image and chemically characterize the lipid composition of oncocytic thyroid tumors, as compared with nononcocytic thyroid tumors and normal thyroid samples. We identified a novel molecular signature of oncocytic tumors characterized by an abnormally high abundance and chemical diversity of cardiolipins (CL), including many oxidized species. DESI-MS imaging and IHC experiments confirmed that the spatial distribution of CLs overlapped with regions of accumulation of mitochondria-rich oncocytic cells. Fluorescent imaging and mitochondrial isolation showed that both mitochondrial accumulation and alteration in CL composition of mitochondria occurred in oncocytic tumors cells, thus contributing the aberrant molecular signatures detected. A total of 219 molecular ions, including CLs, other glycerophospholipids, fatty acids, and metabolites, were found at increased or decreased abundance in oncocytic, nononcocytic, or normal thyroid tissues. Our findings suggest new candidate targets for clinical and therapeutic use against oncocytic tumors. Cancer Res; 76(22); 6588-97. ©2016 AACR.

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

INTRODUCTION

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

OBJECTIVES

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs.

METHODS

A literature search of the current relevant primary research was performed.

RESULTS

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

CONCLUSION

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

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

INTRODUCTION

Metabolomics is the emerging member of "omics" sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

OBJECTIVES

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

METHODS

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

RESULTS

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

CONCLUSION

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.