Identification of metabolic biomarkers to diagnose epithelial ovarian cancer using a UPLC/QTOF/MS platform

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.

The role of microbial metabolites in endocrine tumorigenesis: From the mechanistic insights to potential therapeutic biomarkers

Microbial metabolites have been indicated to communicate with the host's endocrine system, regulating hormone production, immune-endocrine communications, and interactions along the gut-brain axis, eventually affecting the occurrence of endocrine cancer. Furthermore, microbiota metabolites such as short-chain fatty acids (SCFAs) have been found to affect the tumor microenvironment and boost immunity against tumors. SCFAs, including butyrate and acetate, have been demonstrated to exert anti-proliferative and anti-protective activity on pancreatic cancer cells. The employing of microbial metabolic products in conjunction with radiation and chemotherapy has shown promising outcomes in terms of reducing treatment side effects and boosting effectiveness. Certain metabolites, such as valerate and butyrate, have been made known to improve the efficiency of CAR T-cell treatment, whilst others, such as indole-derived tryptophan metabolites, have been shown to inhibit tumor immunity. This review explores the intricate interplay between microbial metabolites and endocrine tumorigenesis, spanning mechanistic insights to the discovery of potential therapeutic biomarkers.

Lysophosphatidylcholines are associated with P-tau181 levels in early stages of Alzheimer's Disease

Background

We profiled circulating plasma metabolites to identify systemic biochemical changes in clinical and biomarker-assisted diagnosis of Alzheimer's disease (AD).

Methods

We used an untargeted approach with liquid chromatography coupled to high-resolution mass spectrometry to measure small molecule plasma metabolites from 150 clinically diagnosed AD patients and 567 age-matched healthy elderly of Caribbean Hispanic ancestry. Plasma biomarkers of AD were measured including P-tau181, Aβ40, Aβ42, total-tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Association of individual and co-abundant modules of metabolites were tested with clinical diagnosis of AD, as well as biologically-defined AD pathological process based on P-tau181 and other biomarker levels.

Results

Over 6000 metabolomic features were measured with high accuracy. First principal component (PC) of lysophosphatidylcholines (lysoPC) that bind to or interact with docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AHA) was associated with decreased risk of AD (OR = 0.91 [0.89-0.96], p = 2e-04). Association was restricted to individuals without an APOE ε4 allele (OR = 0.89 [0.84-0.94], p = 8.7e-05). Among individuals carrying at least one APOE ε4 allele, PC4 of lysoPCs moderately increased risk of AD (OR = 1.37 [1.16-1.6], p = 1e-04). Essential amino acids including tyrosine metabolism pathways were enriched among metabolites associated with P-tau181 levels and heparan and keratan sulfate degradation pathways were associated with Aβ42/Aβ40 ratio.

Conclusions

Unbiased metabolic profiling can identify critical metabolites and pathways associated with β-amyloid and phosphotau pathology. We also observed an APOE-ε4 dependent association of lysoPCs with AD and biologically based diagnostic criteria may aid in the identification of unique pathogenic mechanisms.

Modulation of Cisplatin Sensitivity through TRPML1-Mediated Lysosomal Exocytosis in Ovarian Cancer Cells: A Comprehensive Metabolomic Approach

BACKGROUND

The lysosome has emerged as a promising target for overcoming chemoresistance, owing to its role in facilitating the lysosomal sequestration of drugs. The lysosomal calcium channel TRPML1 not only influences lysosomal biogenesis but also coordinates both endocytosis and exocytosis. This study explored the modulation of cisplatin sensitivity by regulating TRPML1-mediated lysosomal exocytosis and identified the metabolomic profile altered by TRPML1 inhibition.

METHODS

We used four types of ovarian cancer cells: two cancer cell lines (OVCAR8 and TOV21G) and two patient-derived ovarian cancer cells. Metabolomic analyses were conducted to identify altered metabolites by TRPML1 inhibition.

RESULTS

Lysosomal exocytosis in response to cisplatin was observed in resistant cancer cells, whereas the phenomenon was absent in sensitive cancer cells. Through the pharmacological intervention of TRPML1, lysosomal exocytosis was interrupted, leading to the sensitization of resistant cancer cells to cisplatin treatment. To assess the impact of lysosomal exocytosis on chemoresistance, we conducted an untargeted metabolomic analysis on cisplatin-resistant ovarian cancer cells with TRPML1 inhibition. Among the 1446 differentially identified metabolites, we focused on 84 significant metabolites. Metabolite set analysis revealed their involvement in diverse pathways.

CONCLUSIONS

These findings collectively have the potential to enhance our understanding of the interplay between lysosomal exocytosis and chemoresistance, providing valuable insights for the development of innovative therapeutic strategies.

Extracellular vesicles as mediators of cell-cell communication in ovarian cancer and beyond - A lipids focus

Extracellular vesicles (EVs) are messengers that carry information in the form of proteins, lipids, and nucleic acids and are not only essential for intercellular communication but also play a critical role in the progression of various pathologies, including ovarian cancer. There has been recent substantial research characterising EV cargo, specifically, the lipid profile of EVs. Lipids are involved in formation and cargo sorting of EVs, their release and cellular uptake. Numerous lipidomic studies demonstrated the enrichment of specific classes of lipids in EVs derived from cancer cells suggesting that the EV associated lipids can potentially be employed as minimally invasive biomarkers for early diagnosis of various malignancies, including ovarian cancer. In this review, we aim to provide a general overview of the heterogeneity of EV, biogenesis, their lipid content, and function in cancer progression focussing on ovarian cancer.

Lysophosphatidylcholines are associated with P-tau181 levels in early stages of Alzheimer's Disease

Background

We investigated systemic biochemical changes in Alzheimer's disease (AD) by investigating the relationship between circulating plasma metabolites and both clinical and biomarker-assisted diagnosis of AD.

Methods

We used an untargeted approach with liquid chromatography coupled to high-resolution mass spectrometry to measure exogenous and endogenous small molecule metabolites in plasma from 150 individuals clinically diagnosed with AD and 567 age-matched elderly without dementia of Caribbean Hispanic ancestry. Plasma biomarkers of AD were also measured including P-tau181, Aβ40, Aβ42, total tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Association of individual and co-expressed modules of metabolites were tested with the clinical diagnosis of AD, as well as biologically-defined AD pathological process based on P-tau181 and other biomarker levels.

Results

Over 4000 metabolomic features were measured with high accuracy. First principal component (PC) of lysophosphatidylcholines (lysoPC) that bind to or interact with docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AHA) was associated with decreased risk of AD (OR=0.91 [0.89-0.96], p=2e-04). Restricted to individuals without an APOE ε4 allele (OR=0.89 [0.84-0.94], p= 8.7e-05), the association remained. Among individuals carrying at least one APOE ε4 allele, PC4 of lysoPCs moderately increased risk of AD (OR=1.37 [1.16-1.6], p=1e-04). Essential amino acids including tyrosine metabolism pathways were enriched among metabolites associated with P-tau181 levels and heparan and keratan sulfate degradation pathways were associated with Aβ42/Aβ40 ratio reflecting different pathways enriched in early and middle stages of disease.

Conclusions

Our findings indicate that unbiased metabolic profiling can identify critical metabolites and pathways associated with β-amyloid and phosphotau pathology. We also observed an APOE ε4 dependent association of lysoPCs with AD and that biologically-based diagnostic criteria may aid in the identification of unique pathogenic mechanisms.

CPNE1 mediates glycolysis and metastasis of breast cancer through activation of PI3K/AKT/HIF-1α signaling

CPNE1 regulates multiple signaling pathways and can stimulate cell proliferation and differentiation by activating the AKT-mTOR signaling pathway. In addition, CPNE1 is associated with various cancers; however, its role in breast cancer, particularly in TNBC, has not been fully elucidated. Our study aimed to reveal the impact of the CPNE1/PI3K/AKT/HIF-1α axis on TNBC. We first measured the expression of CPNE1 in the tumor tissues of TNBC patients and examined its prognostic value. Subsequently, we used sh-CPNE1 and overexpression vectors to transfect TNBC cell lines and analyzed cell viability, migration, and invasive abilities using colony formation and CCK-8 assays. Metabolites were analyzed through metabolomics. We found that higher expression of CPNE1 predicted poor prognosis in TNBC patients. Knockdown of CPNE1 reduced the viability, migration, invasion, and proliferation capabilities of TNBC cells. Furthermore, metabolomics analysis showed that glucose metabolism was the most dominant pathway, and knockdown of CPNE1 significantly limited the glycolytic activity of TNBC cells. We verified these conclusions in mouse models. Additionally, we overexpressed CPNE1 and treated TNBC cell lines with a PI3K inhibitor (LY294002). The results indicated that CPNE1 promoted aerobic glycolysis in TNBC cells through the PI3K/AKT/HIF-1α signaling pathway. This suggests that CPNE1 regulates cell glycolysis and participates in the development of TNBC. Our study may provide a new therapeutic target for TNBC treatment.

The Female Reproductive Tract Microbiome and Cancerogenesis: A Review Story of Bacteria, Hormones, and Disease

The microbiota is the complex community of microorganisms that populate a particular environment in the human body, whereas the microbiome is defined by the entire habitat-microorganisms and their environment. The most abundant and, therefore, the most studied microbiome is that of the gastrointestinal tract. However, the microbiome of the female reproductive tract is an interesting research avenue, and this article explores its role in disease development. The vagina is the reproductive organ that hosts the largest number of bacteria, with a healthy profile represented mainly by Lactobacillus spp. On the other hand, the female upper reproductive tract (uterus, Fallopian tubes, ovaries) contains only a very small number of bacteria. Previously considered sterile, recent studies have shown the presence of a small microbiota here, but there are still debates on whether this is a physiologic or pathologic occurrence. Of particular note is that estrogen levels significantly influence the composition of the microbiota of the female reproductive tract. More and more studies show a link between the microbiome of the female reproductive tract and the development of gynecological cancers. This article reviews some of these findings.

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.

Fabricated Metal-Organic Frameworks (MOFs) as luminescent and electrochemical biosensors for cancer biomarkers detection

In the health domain, a major challenge is the detection of diseases using rapid and cost-effective techniques. Most of the existing cancer detection methods show poor sensitivity and selectivity and are time consuming with high cost. To overcome this challenge, we analyzed porous fabricated metal-organic frameworks (MOFs) that have better structures and porosities for enhanced biomarker sensing. Here, we summarize the use of fabricated MOF luminescence and electrochemical sensors in devices for cancer biomarker detection. Various strategies of fabrication and the role of fabricated materials in sensing cancer biomarkers have been studied and described. The structural properties, sensing mechanisms, roles of noncovalent interactions, limits of detection, modeling, advantages, and limitations of MOF sensors have been well-discussed. The study presents an innovative technique to detect the cancer biomarkers by the use of luminescence and electrochemical MOF sensors. In addition, the potential association studies have been opening the way for personalized patient treatments and the development of new cancer-detecting devices.

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.

Effect of Helicobacter Pylori on Plasma Metabolic Phenotype in Patients With Gastric Cancer

BACKGROUND

Although Helicobacter pylori (Hp) as high risk factor for gastric cancer have been investigated from human trial, present data is inadequate to explain the effect of Hp on the changes of metabolic phenotype of gastric cancer in different stages.

PURPOSE

Herein, plasma of human superficial gastritis (Hp negative and positive), early gastric cancer and advanced gastric cancer analyzed by UPLC-HDMS metabolomics can not only reveal metabolic phenotype changes in patients with gastric cancer of different degrees (30 Hp negative, 30 Hp positive, 20 early gastric cancer patients, and 10 advanced gastric cancer patients), but also auxiliarily diagnose gastric cancer.

RESULTS

Combined with multivariate statistical analysis, the results represented biomarkers different from Hp negative, Hp positive, and the alterations of metabolic phenotype of gastric cancer patients. Forty-three metabolites are involved in amino acid metabolism, and lipid and fatty acid metabolism pathways in the process of cancer occurrence, especially 2 biomarkers glycerophosphocholine and neopterin, were screened in this study. Neopterin was consistently increased with gastric cancer progression and glycerophosphocholine tended to consistently decrease from Hp negative to advanced gastric cancer.

CONCLUSION

This method could be used for the development of rapid targeted methods for biomarker identification and a potential diagnosis of gastric cancer.

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

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

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.

A comparative UPLC-Q-Orbitrap-MS untargeted metabolomics investigation of different parts of Clausena lansium (Lour.) Skeels

In this study, the non-targeted large-scale plant metabolomics (UPLC-Q-Orbitrap-MS) was performed for the comparison of chemical profiling of the leaves, barks, flowers, peels, pulps, and seeds of Clausena lansium (Lour.) Skeels (called "wampee"). A total of 364 metabolites were identified, and 62 potential biomarkers were selected by the multivariate statistical analysis. Hierarchical cluster analysis suggested that the selected biomarkers were significant differential metabolites among various parts of wampee. Metabolic pathway analysis showed a significant enrichment of the "Flavone and flavonol synthesis" and "Isoquinoline alkaloid biosynthesis" pathway. This study provides important information for the isolation and identification of functional components from different tissues of wampee and the metabolic biosynthesis pathway elucidation in detail.

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

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

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

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

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

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

Perspective: The Potential Role of Circulating Lysophosphatidylcholine in Neuroprotection against Alzheimer Disease

Alzheimer disease (AD), the most common cause of dementia, is a progressive disorder involving cognitive impairment, loss of learning and memory, and neurodegeneration affecting wide areas of the cerebral cortex and hippocampus. AD is characterized by altered lipid metabolism in the brain. Lower concentrations of long-chain PUFAs have been described in the frontal cortex, entorhinal cortex, and hippocampus in the brain in AD. The brain can synthesize only a few fatty acids; thus, most fatty acids must enter the brain from the blood. Recent studies show that PUFAs such as DHA (22:6) are transported across the blood-brain barrier (BBB) in the form of lysophosphatidylcholine (LPC) via a specific LPC receptor at the BBB known as the sodium-dependent LPC symporter 1 (MFSD2A). Higher dietary PUFA intake is associated with decreased risk of cognitive decline and dementia in observational studies; however, PUFA supplementation, with fatty acids esterified in triacylglycerols did not prevent cognitive decline in clinical trials. Recent studies show that LPC is the preferred carrier of PUFAs across the BBB into the brain. An insufficient pool of circulating LPC containing long-chain fatty acids could potentially limit the supply of long-chain fatty acids to the brain, including PUFAs such as DHA, and play a role in the pathobiology of AD. Whether adults with low serum LPC concentrations are at greater risk of developing cognitive decline and AD remains a major gap in knowledge. Preventing and treating cognitive decline and the development of AD remain a major challenge. The LPC pathway is a promising area for future investigators to identify modifiable risk factors for AD.

Mechanism of Chronic Kidney Disease Progression and Novel Biomarkers: A Metabolomic Analysis of Experimental Glomerulonephritis

While a complex network of cellular and molecular events is known to be involved in the pathophysiological mechanism of chronic kidney disease (CKD), the divergence point between reversal and progression and the event that triggers CKD progression are still unknown. To understand the different mechanisms between reversible and irreversible kidney disease and to search for urinary biomarkers that can predict prognosis, a metabolomic analysis was applied to compare acute and chronic experimental glomerulonephritis (GN) models. Four metabolites, namely, epoxyoctadecenoic acid (EpOME), epoxyeicosatetraenoic acid (EpETE), α-linolenic acid (ALA), and hydroxyretinoic acid, were identified as predictive markers after comparing the chronic nephritis model with acute nephritis and control groups (false discovery rate adjusted p-value (q-value) < 0.05). Renal mRNA expression of cytochrome P450 and epoxide hydrolase was also identified as being involved in the production of epoxide metabolites from these polyunsaturated fatty acids (p < 0.05). These results suggested that the progression of chronic kidney disease is associated with abnormally activated epoxide hydrolase, leading to an increase in EpOME and EpETE as pro-inflammatory eicosanoids.

Blood Metabolites Associate with Prognosis in Endometrial Cancer

Endometrial cancer has a high prevalence among post-menopausal women in developed countries. We aimed to explore whether certain metabolic patterns could be related to the characteristics of aggressive disease and poorer survival among endometrial cancer patients in Western Norway. Patients with endometrial cancer with short survival (n = 20) were matched according to FIGO (International Federation of Gynecology and Obstetrics, 2009 criteria) stage, histology, and grade, with patients with long survival (n = 20). Plasma metabolites were measured on a multiplex system including 183 metabolites, which were subsequently determined using liquid chromatography-mass spectrometry. Partial least square discriminant analysis, together with hierarchical clustering, was used to identify patterns which distinguished short from long survival. A proposed signature of metabolites related to survival was suggested, and a multivariate receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.820–0.965 (p ≤ 0.001). Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in these patients. In a subgroup with preoperative contrast-enhanced computed tomography data, selected metabolites correlated with the estimated abdominal fat distribution parameters. Metabolic signatures may predict prognosis and be promising supplements when evaluating phenotypes and exploring metabolic pathways related to the progression of endometrial cancer. In the future, this may serve as a useful tool in cancer management.

Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple-Knockout Mouse Model

High-grade serous carcinoma (HGSC) is the most common and deadliest ovarian cancer (OC) type, accounting for 70-80% of OC deaths. This high mortality is largely due to late diagnosis. Early detection is thus crucial to reduce mortality, yet the tumor pathogenesis of HGSC remains poorly understood, making early detection exceedingly difficult. Faithfully and reliably representing the clinical nature of human HGSC, a recently developed triple-knockout (TKO) mouse model offers a unique opportunity to examine the entire disease spectrum of HGSC. Metabolic alterations were investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to serum samples collected from these mice at premalignant, early, and advanced stages of HGSC. This comprehensive analysis revealed a panel of 29 serum metabolites that distinguished mice with HGSC from controls and mice with uterine tumors with over 95% accuracy. Meanwhile, our panel could further distinguish early-stage HGSC from controls with 100% accuracy and from advanced-stage HGSC with over 90% accuracy. Important identified metabolites included phospholipids, sphingomyelins, sterols, N-acyltaurine, oligopeptides, bilirubin, 2(3)-hydroxysebacic acids, uridine, N-acetylneuraminic acid, and pyrazine derivatives. Overall, our study provides insights into dysregulated metabolism associated with HGSC development and progression, and serves as a useful guide toward early detection.

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.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

One-Carbon Metabolism: Biological Players in Epithelial Ovarian Cancer

Metabolism is deeply involved in cell behavior and homeostasis maintenance, with metabolites acting as molecular intermediates to modulate cellular functions. In particular, one-carbon metabolism is a key biochemical pathway necessary to provide carbon units required for critical processes, including nucleotide biosynthesis, epigenetic methylation, and cell redox-status regulation. It is, therefore, not surprising that alterations in this pathway may acquire fundamental importance in cancer onset and progression. Two of the major actors in one-carbon metabolism, folate and choline, play a key role in the pathobiology of epithelial ovarian cancer (EOC), the deadliest gynecological malignancy. EOC is characterized by a cholinic phenotype sustained via increased activity of choline kinase alpha, and via membrane overexpression of the alpha isoform of the folate receptor (FRα), both of which are known to contribute to generating regulatory signals that support EOC cell aggressiveness and proliferation. Here, we describe in detail the main biological processes associated with one-carbon metabolism, and the current knowledge about its role in EOC. Moreover, since the cholinic phenotype and FRα overexpression are unique properties of tumor cells, but not of normal cells, they can be considered attractive targets for the development of therapeutic approaches.

Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

BACKGROUND

Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.

OBJECTIVE

The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.

METHODS

Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography-mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.

RESULTS

Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, L-beta-aspartyl-L-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.

CONCLUSION

Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.

Identification and functional analyses of differentially expressed metabolites in early stage endometrial carcinoma

Diagnosis of endometrial cancer is primarily based on symptoms and imaging, with early-stage disease being difficult to diagnose. Therefore, development of potential diagnostic biomarkers is required. Metabolomics, a quantitative measurement of the dynamic metabolism in living systems, can be applied to determine metabolite profiles in different disease states. Here, serum metabolomics was performed in 46 early stage endometrial cancer patients and 46 healthy volunteers. In addition, the effect of identified metabolites on tumor cell behavior (invasion, migration, proliferation, apoptosis and autophagy) was examined in endometrial cancer cell lines. Compared with controls, phenylalanine, indoleacrylic acid (IAA), phosphocholine and lyso-platelet-activating factor-16 (lyso-PAF) were differentially detected in patients. Functional analyses demonstrated that IAA, PAF and phenylalanine all dose-dependently inhibited tumor cell invasion and migration, and suppressed cell proliferation. PAF also induced tumor cell apoptosis and autophagy, while phenylalanine had no effect on apoptosis or autophagy. IAA triggered apoptosis and had a biphasic effect on autophagy: inhibiting autophagy with doses <1 mmol/L but inducing at 1 mmol/L. Interestingly, the alterations in proliferation, apoptosis and autophagy caused by 1 mmol/L IAA, were all reversed by the concomitant treatment of tryptophan (100 μmol/L). Phosphocholine inhibited tumor cell invasion and migration, and promoted cell proliferation and autophagy, all in a dose-dependent manner. Phosphocholine also protected cells from TNF-α-induced apoptosis. In conclusion, 4 serum metabolites were identified by serum metabolomics in endometrial cancer patients and functional analyses suggested that they may play roles in modulation of tumor cell behavior, although their exact mode of action still needs to be determined.

Potential serum biomarkers and metabonomic profiling of serum in ischemic stroke patients using UPLC/Q-TOF MS/MS

Background

Stroke still has a high incidence with a tremendous public health burden and it is a leading cause of mortality and disability. However, biomarkers for early diagnosis are absent and the metabolic alterations associated with ischemic stroke are not clearly understood. The objectives of this case-control study are to identify serum biomarkers and explore the metabolic alterations of ischemic stroke.

Methods

Metabonomic analysis was performed using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis was employed to study 60 patients with or without ischemic stroke (30 cases and 30 controls).

Results

Serum metabolic profiling identified a series of 12 metabolites with significant alterations, and the related metabolic pathways involved glycerophospholipid, sphingolipid, phospholipid, fat acid, acylcarnitine, heme, and purine metabolism. Subsequently, multiple logistic regression analyses of these metabolites showed uric acid, sphinganine and adrenoyl ethanolamide were potential biomarkers of ischemic stroke with an area under the receiver operating characteristic curve of 0.941.

Conclusions

These findings provide insights into the early diagnosis and potential pathophysiology of ischemic stroke.

Plasma lipid profiling and diagnostic biomarkers for oral squamous cell carcinoma

Biological requirements for tumor cell proliferation include the sustained increase of structural, energetic, signal transduction and biosynthetic precursors. Because lipids participate in membrane construction, energy storage, and cell signaling. We hypothesized that the differences in lipids between malignant carcinoma and normal controls could be reflected in the bio-fluids. A total of 100 pre-operative plasma samples were collected from 50 oral squamous cell carcinoma (OSCC), 50 normal patients and characterize by lipid profiling using ultra performance liquid chromatography/electro spray ionization mass spectrometry (UPLC-MS). The lipid profiles of the OSCC and control samples as well as the different stages were compared. Differentially expressed lipids were categorized as glycerophospholipids and sphingolipids. All glycerophospholipids were decreased, especially phosphatidylcholine and phosphoethanolamine plasmalogens, whereas sphingolipids were increased in the OSCC patients compared to the controls. We further identified 12 staging related lipids, which could be utilized to discriminate early stage patients from advanced stage patients. In the future, the differential lipids may provide biologists with additional information regarding lipid metabolism and guide clinicians in making individualized therapeutic decisions if these results are confirmed in a larger study.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

Screening and verifying endometrial carcinoma diagnostic biomarkers based on a urine metabolomic profiling study using UPLC-Q-TOF/MS

BACKGROUND

Endometrial carcinoma (EOC) is a gynecological disease with one of the highest worldwide incidences. Due to the lack of typical clinical symptoms and limited sensitive screening methods used to diagnose endometrial carcinoma, the disease is easily neglected before patients are aware of its presence. Therefore, EOC results in serious impacts on women's lives and health. We screened diagnostic biomarkers of EOC with a noninvasive method that compared healthy individuals and endometrial hyperplasia (EOH) patients.

METHODS

The morning urine of 25 healthy individuals, 25 patients with EOC and 10 patients with EOH were analyzed using an ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) platform. Metabolomics data were used to screen the different metabolites according to principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) analyses. Furthermore, the screened biomarkers of the newly diagnosed EOC and EOH candidates and healthy individuals were verified using the predictive model of the support vector machine (SVM) to obtain EOC diagnostic biomarkers.

RESULTS

An EOC diagnostic biomarker group was found according to the metabolomics method. Five diagnostic biomarkers, including porphobilinogen, acetylcysteine, N-acetylserine, urocanic acid and isobutyrylglycine, were significantly changed in the EOC patients. Among them, porphobilinogen and acetylcysteine were significantly down-regulated, while N-acetylserine, urocanic acid and isobutyrylglycine were significantly up-regulated.

CONCLUSIONS

Disturbances in these biomarkers have negative impacts on the body's metabolic functioning. The EOC diagnostic biomarker group can provide a clinical reference for diagnosing EOC and insight into the diagnosis of other diseases in the clinic.

Use of Plasma Metabolomics to Identify Diagnostic Biomarkers for Early Stage Epithelial Ovarian Cancer

The early detection of ovarian carcinoma is difficult due to the absence of recognizable physical symptoms and a lack of sensitive screening methods. The currently available biomarkers (such as CA125 and HE4) are insufficiently reliable to distinguish early stage (I/II) epithelial ovarian cancer (EOC) patients from normal individuals because they possess a relatively poor sensitivity and specificity. To evaluate the application of metabolomics to biomarker discovery in the early stages of epithelial ovarian cancer (EOC), plasma samples from 21 early stage EOC patients and 31 healthy controls were analyzed with ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-Tof/MS) in conjunction with multivariate statistical analysis. Eighteen metabolites, including lysophospholipids, 2-piperidone and MG (18:2), were found to be disturbed in early stage EOC with satisfactory diagnostic accuracy (AUC=0.920). These biomarkers were specifically validated in the EOC nude mouse model, and five of the biomarkers (lysophospholipids, adrenoyl ethanolamide et al.) were highly suspected of being associated with EOC because they were differentially expressed with the same tendency in the EOC nude mice versus normal controls. In conclusion, the selected metabolic biomarkers have considerable utility and significant potential for diagnosing early ovarian cancer and investigating its underlying mechanisms.

Principle, Instrumentation, and Applications of UPLC: A Novel Technique of Liquid Chromatography

The key focus of the pharmaceutical or chemical industries is to reduce the cost involved in the development of new drugs and to improve the selectivity, sensitivity, and resolution for their detection. The purpose can now be solved by the separation method called UPLC which is the modified HPLC method comprising high pressure and small sized particles (less than 2 µm) used in the column, so the length of the column decreases leading to time saving and reduction in the consumption of solvent. The underlying principle of UPLC is based on van Deemter statement which describes the connection between linear velocity with plate height. UPLC contributes to the improvement of the three areas: speed, resolution, and sensitivity. This is a new advanced category of the HPLC which has the same basic principle and methodology with improved chromatographic performance. This review is an effort to compile the principle, instrumentation, and applications of UPLC.

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.

Metabolic phenotyping for monitoring ovarian cancer patients

Epithelial ovarian cancer (EOC) is the most deadly of the gynecological cancers. New approaches and better tools for monitoring treatment efficacy and disease progression of EOC are required. In this study, metabolomics using rapid resolution liquid chromatography mass spectrometry was applied to a systematic investigation of metabolic changes in response to advanced EOC, surgery and recurrence. The results revealed considerable metabolic differences between groups. Moreover, 37, 30, and 26 metabolites were identified as potential biomarkers for primary, surgical and recurrent EOC, respectively. Primary EOC was characterized by abnormal lipid metabolism and energy disorders. Oxidative stress and surgical efficacy were clear in the post-operative EOC patients. Recurrent EOC patients showed increased amino acid and lipid metabolism compared with primary EOC patients. After cytoreductive surgery, eight metabolites (e.g. l-kynurenine, retinol, hydroxyphenyllactic acid, 2-octenoic acid) corrected towards levels of the control group, and four (e.g. hydroxyphenyllactic acid, 2-octenoic acid) went back again to primary EOC levels after disease relapse. In conclusion, this study delineated metabolic changes in response to advanced EOC, surgery and recurrence, and identified biomarkers that could facilitate both understanding and monitoring of EOC development and progression.

Analysis of human plasma metabolites across different liquid chromatography/mass spectrometry platforms: Cross-platform transferable chemical signatures

RATIONALE

The metabolite profiling of a NIST plasma Standard Reference Material (SRM 1950) on different liquid chromatography/mass spectrometry (LC/MS) platforms showed significant differences. Although these findings suggest caution when interpreting metabolomics results, the degree of overlap of both profiles allowed us to use tandem mass spectral libraries of recurrent spectra to evaluate to what extent these results are transferable across platforms and to develop cross-platform chemical signatures.

METHODS

Non-targeted global metabolite profiles of SRM 1950 were obtained on different LC/MS platforms using reversed-phase chromatography and different chromatographic scales (conventional HPLC, UHPLC and nanoLC). The data processing and the metabolite differential analysis were carried out using publically available (XCMS), proprietary (Mass Profiler Professional) and in-house software (NIST pipeline).

RESULTS

Repeatability and intermediate precision showed that the non-targeted SRM 1950 profiling was highly reproducible when working on the same platform (relative standard deviation (RSD) <2%); however, substantial differences were found in the LC/MS patterns originating on different platforms or even using different chromatographic scales (conventional HPLC, UHPLC and nanoLC) on the same platform. A substantial degree of overlap (common molecular features) was also found. A procedure to generate consistent chemical signatures using tandem mass spectral libraries of recurrent spectra is proposed.

CONLUSIONS

Different platforms rendered significantly different metabolite profiles, but the results were highly reproducible when working within one platform. Tandem mass spectral libraries of recurrent spectra are proposed to evaluate the degree of transferability of chemical signatures generated on different platforms. Chemical signatures based on our procedure are most likely cross-platform transferable.

Potential serum biomarkers from a metabolomics study of autism

BACKGROUND

Early detection and diagnosis are very important for autism. Current diagnosis of autism relies mainly on some observational questionnaires and interview tools that may involve a great variability. We performed a metabolomics analysis of serum to identify potential biomarkers for the early diagnosis and clinical evaluation of autism.

METHODS

We analyzed a discovery cohort of patients with autism and participants without autism in the Chinese Han population using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF MS/MS) to detect metabolic changes in serum associated with autism. The potential metabolite candidates for biomarkers were individually validated in an additional independent cohort of cases and controls. We built a multiple logistic regression model to evaluate the validated biomarkers.

RESULTS

We included 73 patients and 63 controls in the discovery cohort and 100 cases and 100 controls in the validation cohort. Metabolomic analysis of serum in the discovery stage identified 17 metabolites, 11 of which were validated in an independent cohort. A multiple logistic regression model built on the 11 validated metabolites fit well in both cohorts. The model consistently showed that autism was associated with 2 particular metabolites: sphingosine 1-phosphate and docosahexaenoic acid.

LIMITATIONS

While autism is diagnosed predominantly in boys, we were unable to perform the analysis by sex owing to difficulty recruiting enough female patients. Other limitations include the need to perform test-retest assessment within the same individual and the relatively small sample size.

CONCLUSION

Two metabolites have potential as biomarkers for the clinical diagnosis and evaluation of autism.

Systemic Metabolomic Changes in Blood Samples of Lung Cancer Patients Identified by Gas Chromatography Time-of-Flight Mass Spectrometry

Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

A metabolomic approach to identifying platinum resistance in ovarian cancer

BACKGROUND

Acquisition of metabolic alterations has been shown to be essential for the unremitting growth of cancer, yet the relation of such alterations to chemosensitivity has not been investigated. In the present study our aim was to identify the metabolic alterations that are specifically associated with platinum resistance in ovarian cancer. A global metabolic analysis of the A2780 platinum-sensitive and its platinum-resistant derivative C200 ovarian cancer cell line was performed utilizing ultra-high performance liquid chromatography/mass spectroscopy and gas chromatography/mass spectroscopy. Per-metabolite comparisons were made between cell lines and an interpretive analysis was carried out using the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic library and the Ingenuity exogenous molecule library.

RESULTS

We observed 288 identified metabolites, of which 179 were found to be significantly different (t-test p < 0.05) between A2780 and C200 cells. Of these, 70 had increased and 109 had decreased levels in platinum resistant C200 cells. The top altered KEGG pathways based on number or impact of alterations involved the cysteine and methionine metabolism. An Ingenuity Pathway Analysis also revealed that the methionine degradation super-pathway and cysteine biosynthesis are the top two canonical pathways affected. The highest scoring network of altered metabolites was related to carbohydrate metabolism, energy production, and small molecule biochemistry. Compilation of KEGG analysis and the common network molecules revealed methionine and associated pathways of glutathione synthesis and polyamine biosynthesis to be most significantly altered.

CONCLUSION

Our findings disclose that the chemoresistant C200 ovarian cancer cells have distinct metabolic alterations that may contribute to its platinum resistance. This distinct metabolic profile of platinum resistance is a first step towards biomarker development for the detection of chemoresistant disease and metabolism-based drug targets specific for chemoresistant tumors.

Interference with the expression of β-catenin reverses cisplatin resistance in A2780/DDP cells and inhibits the progression of ovarian cancer in mouse model

Cisplatin is a most active drug for the treatment of ovarian cancer; however, acquired cisplatin resistance is easily seen in patients with ovarian cancer. The aim of this study is to clarify the molecular mechanism of cisplatin resistance and try to reverse cisplatin resistance in ovarian cancer lines in vitro and in vivo. First, we used ovarian cancer cell line A2780, and its cisplatin-resistant subline, A2780/DDP as cell model. Cell viability was determined by MTT assay and the IC50 values were observed to increase in a dose- and time-dependent manner. Next, the expression of β-catenin was determined by western blotting analysis, and the results demonstrated that the expression level of β-catenin in A2780/DDP cells was significantly higher than that in A2780 cells (p<0.01). Moreover, we detected the distribution of cytoplasmic and nuclear β-catenin by western blot analysis, which showed that β-catenin was mainly located in nucleus. Compared with A2780 cells, there was no obvious change as the increasing dose of cisplatin in A2780/DDP cells reveal that cisplatin resistance was related to the exrpession of β-catenin. Furthermore, interference with the expression of β-catenin could effectively reverse cisplatin resistance as IC50 was significantly decreased from 123.7 to 42.43 μM in A2780/DDP cells. Additionally, transient interference of β-catenin by siRNA promoted the apoptosis of A2780/DDP cells, for increased apoptosis rates and cleaved caspase-3 levels were detected being treated with cisplatin. Finally, tumorigenicity experiments showed that tumor growth was significantly suppressed in β-catenin shRNA group. The body weight was not significantly changed during the experimental days. In conclusion, all the results showed that cisplatin resistance was partly induced by Wnt/β-catenin signaling pathway. Interfering the expression of β-catenin could reverse cisplatin resistance in vitro and in vivo. Thus, β-catenin could be a potential therapeutic target for the therapy of cisplatin-resistant ovarian cancer.

Prediction of advanced ovarian cancer recurrence by plasma metabolic profiling

Epithelial ovarian cancer (EOC) is the most lethal of gynecologic malignancies due to the high rate of recurrence and poor prognosis. Predicting the prognosis in patients with EOC is clinically challenging, partly because appropriate biomarkers of recurrence have yet to be explored. In this prospective study, pre-treatment plasma samples were collected from 38 patients with stage III or IV EOC who were subsequently followed up. Ultra-performance liquid chromatography mass spectrometry was used to perform metabolic profiling, which yielded five metabolites that were potential biomarkers for EOC recurrence: l-tryptophan, kynurenine, bilirubin, LysoPC (14 : 0) and LysoPE (18 : 2). A combination of these five potential biomarkers strongly predicted recurrence, the area under the curve being 0.91. In summary, the candidate biomarkers identified in this study may both facilitate clinical prediction of EOC recurrence and prognosis and serve as potential therapeutic targets in patients with EOC.

In 2014, can we do better than CA125 in the early detection of ovarian cancer?

Ovarian cancer is a lethal gynecologic malignancy with greater than 70% of women presenting with advanced stage disease. Despite new treatments, long term outcomes have not significantly changed in the past 30 years with the five-year overall survival remaining between 20% and 40% for stage III and IV disease. In contrast patients with stage I disease have a greater than 90% five-year overall survival. Detection of ovarian cancer at an early stage would likely have significant impact on mortality rate. Screening biomarkers discovered at the bench have not translated to success in clinical trials. Existing screening modalities have not demonstrated survival benefit in completed prospective trials. Advances in high throughput screening are making it possible to evaluate the development of ovarian cancer in ways never before imagined. Data in the form of human “-omes” including the proteome, genome, metabolome, and transcriptome are now available in various packaged forms. With the correct pooling of resources including prospective collection of patient specimens, integration of high throughput screening, and use of molecular heterogeneity in biomarker discovery, we are poised to make progress in ovarian cancer screening. This review will summarize current biomarkers, imaging, and multimodality screening strategies in the context of emerging technologies.

Proteomic profiling of serum from patients with tuberculosis

Background

Effective treatment and monitoring of tuberculosis (TB) requires biomarkers that can be easily evaluated in blood samples. The aim of this study was to analyze the serum proteome of patients with TB and to identify protein biomarkers for TB.

Methods

Serum samples from 26 TB patients and 31 controls were analyzed by using nano-flow ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry in data-independent mode, and protein and peptide amounts were calculated by using a label-free quantitative approach. The generated data were analyzed by using principal component analysis and partial least squares discriminant analysis, a multivariate statistical method.

Results

Of more than 500 proteins identified, alpha-1-antitrypsin was the most discriminative, which was 4.4 times higher in TB patients than in controls. Peptides from alpha-1-antitrypsin and antithrombin III increased in TB patients and showed a high variable importance in the projection scores and coefficient in partial least square discriminant analysis.

Conclusions

Sera from patients with TB had higher alpha-1-antitrypsin levels than sera from control participants. Alpha-1-antitrypsin levels may aid in the diagnosis of TB.

Large-scale profiling of metabolic dysregulation in ovarian cancer

Ovarian cancer is the leading cause of death in gynecologic malignancies. Profiling of endogenous metabolites has potential to identify changes caused by cancer and provide inspiring insights into cancer metabolism. To systematically investigate ovarian cancer metabolism, we performed metabolic profiling of 448 plasma samples related to epithelial ovarian cancer (EOC) based on ultra-performance liquid chromatography mass spectrometry in both positive and negative modes. These unbiased metabolomic profiles could well distinguish EOC from benign ovarian tumor (BOT) and uterine fibroid (UF). Fifty-three metabolites were identified as specific biomarkers for EOC, and this is the first report of piperine, 3-indolepropionic acid, 5-hydroxyindoleacetaldehyde and hydroxyphenyllactate as metabolic biomarkers of EOC. The AUC values of these metabolites for discriminating EOC from BOT/UF and early-stage EOC from BOT/UF were 0.9100/0.9428 and 0.8385/0.8624, respectively. Meanwhile, our metabolites were able to distinguish early-stage EOC from late-stage EOC with an AUC of 0.8801. Importantly, analysis of dysregulated metabolic pathways extends our current understanding of EOC metabolism. Metabolic pathways in EOC patients are mainly characterized by abnormal phospholipid metabolism, altered l-tryptophan catabolism, aggressive fatty acid β-oxidation and aberrant metabolism of piperidine derivatives. Together, these metabolic pathways provide a foundation to support cancer development and progression. In conclusion, our large-scale plasma metabolomics study yielded fundamental insights into dysregulated metabolism in ovarian cancer, which could facilitate clinical diagnosis, therapy, prognosis and shed new lights on ovarian cancer pathogenesis.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.