Multiplatform plasma metabolic and lipid fingerprinting of breast cancer: A pilot control-case study in Colombian Hispanic women

Exploring the complex relationship between metabolomics and breast cancer early detection (Review)

An overview of metabolomics in cancer research, focusing on the identification of biomarkers, pharmacological targets and therapeutic agents, is provided in the present review. The fundamentals of metabolomics, the role of metabolites in cancer emergence and the methods used in metabolomic analysis, are reviewed. The applications of metabolomics in cancer therapy and diagnostics, as well as the challenges encountered in metabolomic research, are discussed. Finally, the potential clinical uses of metabolomics in cancer research and its future possibilities are explored, emphasising the importance of non-invasive diagnostic and monitoring techniques. The present review highlights the significance of metabolite-based metabolomics as a specialised tool for illuminating disease processes and identifying treatment potentials. The malfunctioning of metabolomic pathways and metabolite accumulation or depletion is caused by metabolomics abnormalities. Metabolite signatures close to a subject's phenotypic informative dimension can be used to monitor therapies and disease prediction diagnosis and prognosis. Non-invasive diagnostic and monitoring techniques with high specificity and selectivity are urgently needed. Metabolite-based metabolomics is a specialised metabolic biomarker and pathway-analysis technique, illuminating the putative processes of numerous human illnesses and determining treatment potentials. Locating biochemical pathway modifications that are early warning signs of pathological malfunction and illness is possible by identifying functional biomarkers linked to phenotypic variance. Scientists generated numerous metabolomics profiles to disclose the underlying processes and metabolomics networks for therapeutic target research in biomedicine. The metabolomic analysis of the potential utility of metabolites as biomarkers for clinical events is summarised in the present review. The significance of metabolite-based metabolomics as a specialised tool for illuminating disease processes and identifying treatment potentials is highlighted.

Investigation of the Potential Material Basis and Mechanism of Astragali Radix Against Adriamycin-Induced Nephropathy Model Rat by 1H NMR and MS-Based Untargeted Metabolomics Analysis

Astragali Radix (AR) is one of the monarch drugs of Fangji Huangqi decoction and has the effects of inducing diuresis to alleviate edema, tonifying and strengthening the body. However, there is a paucity of research regarding the effective fraction and the underlying metabolic mechanism of AR on nephrotic syndrome (NS). This work aims to elucidate the potential mechanisms of AR treating NS, as well as to identify effective part and components. Firstly, body weight, kidney index, 24-h urea protein, and biochemical parameters were used to confirm the kidney injury. The most effective part of AR was determined based on the indicators above. Then, 1H NMR, UHPLC-QTOF/MS, and GC-MS-based metabolomic approaches were used to investigate differential metabolites closely associated with the effective part against NS. A "C-T-P-D" network (a network diagram of "TCM prescription-herbs-components-targets-metabolites-pathways-disease") was constructed by intersecting the targets of differential metabolites with those of AR treating NS. The efficacy indicators determined the n-butanol part of AR as the best effective part. Multiplatform metabolomics and network pharmacology study indicated that the potential mechanism for treating NS may be related to targets (MIF, SRC, and GBA) and metabolic pathways (citrate cycle, glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, and glycolysis/gluconeogenesis).

AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data

BACKGROUND

Cancer classification has consistently been a challenging problem, with the main difficulties being high-dimensional data and the collection of patient samples. Concretely, obtaining patient samples is a costly and resource-intensive process, and imbalances often exist between samples. Moreover, expression data is characterized by high dimensionality, small samples and high noise, which could easily lead to struggles such as dimensionality catastrophe and overfitting. Thus, we incorporate prior knowledge from the pathway and combine AutoEncoder and Generative Adversarial Network (GAN) to solve these difficulties.

RESULTS

In this study, we propose an effective and efficient deep learning method, named AEGAN, which combines the capabilities of AutoEncoder and GAN to generate synthetic samples of the minority class in imbalanced gene expression data. The proposed data balancing technique has been demonstrated to be useful for cancer classification and improving the performance of classifier models. Additionally, we integrate prior knowledge from the pathway and employ the pathifier algorithm to calculate pathway scores for each sample. This data augmentation approach, referred to as AEGAN-Pathifier, not only preserves the biological functionality of the data but also possesses dimensional reduction capabilities. Through validation with various classifiers, the experimental results show an improvement in classifier performance.

CONCLUSION

AEGAN-Pathifier shows improved performance on the imbalanced datasets GSE25066, GSE20194, BRCA and Liver24. Results from various classifiers indicate that AEGAN-Pathifier has good generalization capability.

Serum metabolite and metal ions profiles for breast cancer screening

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types-DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients' metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.

A Multiomics, Molecular Atlas of Breast Cancer Survivors

Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.

Depression promotes breast cancer progression by regulating amino acid neurotransmitter metabolism and gut microbial disturbance

INTRODUCTION

Breast cancer (BC) is the most prevalent type of cancer and has the highest mortality among women worldwide. BC patients have a high risk of depression, which has been recognized as an independent factor in the progression of BC. However, the potential mechanism has not been clearly demonstrated.

METHODS

To explore the correlation and mechanism between depression and BC progression, we induced depression and tumor in BC mouse models. Depression was induced via chronic unpredictable mild stress (CUMS) and chronic restraint stress (CRS). Amino acid (AA) neurotransmitter-targeted metabonomics and gut microbiota 16S rDNA gene sequencing were employed in the mouse model after evaluation with behavioral tests and pathological analysis.

RESULTS

The tumors in cancer-depression (CD) mice grew faster than those in cancer (CA) mice, and lung metastasis was observed in CD mice. Metabonomics revealed that the neurotransmitters and plasma AAs in CD mice were dysregulated, namely the tyrosine and tryptophan pathways and monoamine neurotransmitters in the brain. Gut microbiota analysis displayed an increased ratio of Firmicutes/Bacteroides. In detail, the abundance of f_Lachnospiraceae and s_Lachnospiraceae increased, whereas the abundance of o_Bacteroidales and s_Bacteroides_caecimuris decreased. Moreover, the gut microbiota was more closely associated with AA neurotransmitters than with plasma AA.

CONCLUSION

Depression promoted the progression of BC by modulating the abundance of s_Lachnospiraceae and s_Bacteroides_caecimuris, which affected the metabolism of monoamine neurotransmitters in the brain and AA in the blood.

Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer

Introduction

The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers.

Methods

We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions.

Results

Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95).

Discussion

In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review

This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.

Metabolic Reprogramming by Ribitol Expands the Therapeutic Window of BETi JQ1 against Breast Cancer

Many cancer patients still lack effective treatments, and pre-existing or acquired resistance limits the clinical benefit of even the most advanced medicines. Recently, much attention has been given to the role of metabolism in cancer, expanding from the Warburg effect to highlight unique patterns that, in turn, may improve diagnostic and therapeutic approaches. Our recent metabolomics study revealed that ribitol can alter glycolysis in breast cancer cells. In the current study, we investigate the combinatorial effects of ribitol with several other anticancer drugs (chrysin, lonidamine, GSK2837808A, CB-839, JQ1, and shikonin) in various breast cancer cells (MDA-MB-231, MCF-7, and T-47D). The combination of ribitol with JQ1 synergistically inhibited the proliferation and migration of breast cancer cells cell-type dependently, only observed in the triple-negative MDA-MB-231 breast cancer cells. This synergy is associated with the differential effects of the 2 compounds on expression of the genes involved in cell survival and death, specifically downregulation in c-Myc and other anti-apoptotic proteins (Bcl-2, Bcl-xL, Mcl-1), but upregulation in p53 and cytochrome C levels. Glycolysis is differentially altered, with significant downregulation of glucose-6-phosphate and lactate by ribitol and JQ1, respectively. The overall effect of the combined treatment on metabolism and apoptosis-related genes results in significant synergy in the inhibition of cell growth and induction of apoptosis. Given the fact that ribitol is a metabolite with limited side effects, a combined therapy is highly desirable with relative ease to apply in the clinic for treating an appropriate cancer population. Our results also emphasize that, similar to traditional drug development, the therapeutic potential of targeting metabolism for cancer treatment may only be achieved in combination with other drugs and requires the identification of a specific cancer population. The desire to apply metabolomic intervention to a large scope of cancer types may be one of the reasons identification of this class of drugs in a clinical trial setting has been delayed.

The diagnostic significance of circulating miRNAs and metabolite profiling in early prediction of breast cancer in Egyptian women

OBJECTIVE

Breast cancer (BC) is one of the most commonly diagnosed solid malignancies in women worldwide.

PURPOSE

Finding new non-invasive circulating diagnostic biomarkers will facilitate the early prediction of BC and provide valuable insight into disease progression and response to therapy using a safe and more accessible approach available every inspection time. Therefore, our present study aimed to investigate expression patterns of potentially circulating biomarkers that can differentiate well between benign, malignant, and healthy subjects.

METHODS

To achieve our target, quantitative analyses were performed for some circulating biomarkers which have a role in the proliferation and tumor growth, as well as, glutamic acid, and human epidermal growth receptor 2 (HER2) in blood samples of BC patients in comparison to healthy controls using qRT-PCR, liquid chromatography/mass spectrometry (LC/MS/MS), and ELISA.

RESULTS

Our findings showed that the two miRNAs (miRNA-145, miRNA-382) were expressed at lower levels in BC sera than healthy control group, while miRNA-21 was expressed at higher levels in BC patients than control subjects. Area under ROC curves of BC samples revealed that AUC of miRNA-145, miRNA-382, miRNA-21, and glutamic acid was evaluated to equal 0.99, 1.00, 1.00 and 1.00, respectively. Besides, there was a significantly positive correlation between miRNA-145 and miRNA-382 (r = 0.737), and a highly significant positive correlation between miRNA-21 and glutamic acid (r = 0.385).

CONCLUSION

Based on our results, we conclude that the detection of serum miRNA-145, -382 and -21 as a panel along with glutamic acid, and circulating HER2 concentrations could be useful as a non-invasive diagnostic profiling for early prediction of breast cancer in Egyptian patients. It can provide an insight into disease progression, discriminate between malignancy and healthy control, and overcome the use limitations (low sensitivity and specificity, repeated risky exposure, and high cost) of other detecting tools, including mammography, magnetic resonance imaging, and ultrasound.

Metabolomic biomarkers for the diagnosis and post-transplant outcomes of AFP negative hepatocellular carcinoma

Background

Early diagnosis for α-fetoprotein (AFP) negative hepatocellular carcinoma (HCC) remains a critical problem. Metabolomics is prevalently involved in the identification of novel biomarkers. This study aims to identify new and effective markers for AFP negative HCC.

Methods

In total, 147 patients undergoing liver transplantation were enrolled from our hospital, including liver cirrhosis patients (LC, n=25), AFP negative HCC patients (NEG, n=44) and HCC patients with AFP over 20 ng/mL (POS, n=78). 52 Healthy volunteers (HC) were also recruited in this study. Metabolomic profiling was performed on the plasma of those patients and healthy volunteers to select candidate metabolomic biomarkers. A novel diagnostic model for AFP negative HCC was established based on Random forest analysis, and prognostic biomarkers were also identified.

Results

15 differential metabolites were identified being able to distinguish NEG group from both LC and HC group. Random forest analysis and subsequent Logistic regression analysis showed that PC(16:0/16:0), PC(18:2/18:2) and SM(d18:1/18:1) are independent risk factor for AFP negative HCC. A three-marker model of Metabolites-Score was established for the diagnosis of AFP negative HCC patients with an area under the time-dependent receiver operating characteristic curve (AUROC) of 0.913, and a nomogram was then established as well. When the cut-off value of the score was set at 1.2895, the sensitivity and specificity for the model were 0.727 and 0.92, respectively. This model was also applicable to distinguish HCC from cirrhosis. Notably, the Metabolites-Score was not correlated to tumor or body nutrition parameters, but difference of the score was statistically significant between different neutrophil-lymphocyte ratio (NLR) groups (≤5 vs. >5, P=0.012). Moreover, MG(18:2/0:0/0:0) was the only prognostic biomarker among 15 metabolites, which is significantly associated with tumor-free survival of AFP negative HCC patients (HR=1.160, 95%CI 1.012-1.330, P=0.033).

Conclusion

The established three-marker model and nomogram based on metabolomic profiling can be potential non-invasive tool for the diagnosis of AFP negative HCC. The level of MG(18:2/0:0/0:0) exhibits good prognosis prediction performance for AFP negative HCC.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Metabolomics of Breast Cancer: A Review

Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.

Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer

Discovery of disease biomarker based on untargeted metabolomics is informative for pathological mechanism studies and facilitates disease early diagnosis. Numerous of metabolomic strategies emerge due to different sample properties or experimental purposes, thus, methodological evaluation before sample analysis is essential and necessary. In this study, sample preparation, data processing procedure and metabolite identification strategy were assessed aiming at the discovery of biomarker of breast cancer. First, metabolite extraction by different solvents, as well as the necessity of vacuum-dried and re-dissolution, was investigated. The extraction efficiency was assessed based on the number of eligible components (components with MS/MS data acquired), which was more reasonable for metabolite identification. In addition, a simplified data processing procedure was proposed involving the OPLS-DA, primary screening for eligible components, and secondary screening with constraints including VIP, fold change and p value. Such procedure ensured that only differential candidates were subjected to data interpretation, which greatly reduced the data volume for database search and improved analysis efficiency. Furthermore, metabolite identification and annotation confidence were enhanced by comprehensive consideration of mass and MS/MS errors, isotope similarity, fragmentation match, and biological source confirmation. On this basis, the optimized strategy was applied for the analysis of serum samples of breast cancer, according to which the discovery of differential metabolites highly encouraged the independent biomarkers/indicators used for disease diagnosis and chemotherapy evaluation clinically. Therefore, the optimized strategy simplified the process of differential metabolite exploration, which laid a foundation for biomarker discovery and studies of disease mechanism.

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

Potential of blood-based biomarker approaches in endometrium and breast cancer: a case-control comparison study

Purpose

Endometrial carcinoma is the second most common gynecological malignancy. Until today lacking a screening tool. A blood-based biomarker could help address this need.

Methods

The expression levels of 30 acylcarnitines, 18 amino acids, 6 miRNAs, and 7 DNA methylation sites were measured in blood samples from 331 women (20 EC, 14 benign uterine lesions (benign), 140 breast cancers (BC), 157 controls). Areas under the ROC curves (AUC), sensitivity (sens.) and specificity (spec.) were computed to identify the variables best distinguishing.

Results

The best top ten markers for the four comparisons (cancer vs. cancer-free; EC vs. BC, EC vs. controls; EC vs. benign), were identified via AUC. Malonylcarnitine distinguished best patients with EC from controls (AUC: 0.827, sens. 80%, spec. 73.1%) or BC (AUC: 0.819, sens. 84.3%, spec. 80%) being most notable. Tryptophan best differentiated benign from EC (AUC: 0.846, sens. 70%, spec. 92.9%).

Conclusions

The levels of the analyzed blood markers yielded promising results in the detection of EC and warrant further evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00404-022-06482-8.

Liquid biopsy markers for early diagnosis of brain metastasis patients with breast cancer by metabolomics

Introduction: Breast cancer is the most common cancer in women and is the second most common cause of cancer related mortality. Metabolomics, the identification of small metabolites, is a technique for determining the amount of these metabolites. Objectives: This study aimed to identify markers for the early diagnosis of brain metastasis by metabolomic methods in breast cancer patients. Methods: A total of 88 breast cancer patients with distant metastases were included in the study. The patients were divided into two groups according to their metastasis status: patients with brain metastases and distant metastases without any brain metastases. Liquid chromatography quadrupole time-of-flight mass spectrometry (LC-qTOF-MS) and gas chromatography-mass spectrometry (GC-MS) analysis methods were used for metabolomic analyses. Results: 33 of them, 88 patients had brain metastasis, and 55 patients had distant metastases without brain metastasis. A total of 72 and 35 metabolites were identified by the GC-MS and LC-qTOF-MS analysis, respectively. 47 of them were found to be significantly different in patients with brain metastasis. The pathway analysis, performed with significantly altered metabolites, showed that aminoacyl tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, alanine, aspartate, and glutamate metabolism, arginine biosynthesis, glycine, serine, and threonine metabolism pathways significantly altered in patients with brain metastasis. Predictive accuracies for have identifying the brain metastasis were performed with receiver operating characteristic (ROC) analysis, and the model with fifteen metabolites has 96.9% accuracy. Conclusions: While these results should be supported by prospective studies, these data are promising for early detection of brain metastasis with markers in liquid biopsy samples.

Dissecting fine-flavor cocoa bean fermentation through metabolomics analysis to break down the current metabolic paradigm

Cocoa fermentation plays a crucial role in producing flavor and bioactive compounds of high demand for food and nutraceutical industries. Such fermentations are frequently described as a succession of three main groups of microorganisms (i.e., yeast, lactic acid, and acetic acid bacteria), each producing a relevant metabolite (i.e., ethanol, lactic acid, and acetic acid). Nevertheless, this view of fermentation overlooks two critical observations: the role of minor groups of microorganisms to produce valuable compounds and the influence of environmental factors (other than oxygen availability) on their biosynthesis. Dissecting the metabolome during spontaneous cocoa fermentation is a current challenge for the rational design of controlled fermentations. This study evaluates variations in the metabolic fingerprint during spontaneous fermentation of fine flavor cocoa through a multiplatform metabolomics approach. Our data suggested the presence of two phases of differential metabolic activity that correlate with the observed variations on temperature over fermentations: an exothermic and an isothermic phase. We observed a continuous increase in temperature from day 0 to day 4 of fermentation and a significant variation in flavonoids and peptides between phases. While the second phase, from day four on, was characterized for lower metabolic activity, concomitant with small upward and downward fluctuations in temperature. Our work is the first to reveal two phases of metabolic activity concomitant with two temperature phases during spontaneous cocoa fermentation. Here, we proposed a new paradigm of cocoa fermentation that considers the changes in the global metabolic activity over fermentation, thus changing the current paradigm based only on three main groups of microorganism and their primary metabolic products.

Tissue-based metabolomics reveals metabolic signatures and major metabolic pathways of gastric cancer with help of transcriptomic data from TCGA

PURPOSE

The aim of the present study was to screen differential metabolites of gastric cancer (GC) and identify the key metabolic pathways of GC.

METHODS

GC (n=28) and matched paracancerous (PC) tissues were collected, and LC-MS/MS analysis were performed to detect metabolites of GC and PC tissues. Metabolite pathways based on differential metabolites were enriched by MetaboAnalyst, and genes related to metabolite pathways were identified using the KEGGREST function of the R software package. Transcriptomics data from The Cancer Genome Atlas (TCGA) was analyzed to obtain differentially expressed genes (DEGs) of GC. Overlapping genes were acquired from metabonimics and transcriptomics data. Pathway enrichment analysis was performed using String. The protein expression of genes was validated by the Human Protein Atlas (HPA) database.

RESULTS

A total of 325 key metabolites were identified, 111 of which were differentially expressed between the GC and PC groups. Seven metabolite pathways enriched by MetaboAnalyst were chosen, and 361 genes were identified by KEGGREST. A total of 2831 DEGs were identified from the TCGA cohort. Of these, 1317 were down-regulated, and 1636 were up-regulated. Twenty-two overlapping genes were identified between genes related to metabolism and DEGs. Glycerophospholipid (GPL) metabolism is likely associated with GC, of which AGPAT9 and ETNPPL showed lower expressed in GC tissues.

CONCLUSIONS

We investigated the tissue-based metabolomics profile of GC, and several differential metabolites were identified. GPL metabolism may affect on progression of GC.

Plasma lipidomic profiles of kidney, breast and prostate cancer patients differ from healthy controls

Early detection of cancer is one of the unmet needs in clinical medicine. Peripheral blood analysis is a preferred method for efficient population screening, because blood collection is well embedded in clinical practice and minimally invasive for patients. Lipids are important biomolecules, and variations in lipid concentrations can reflect pathological disorders. Lipidomic profiling of human plasma by the coupling of ultrahigh-performance supercritical fluid chromatography and mass spectrometry is investigated with the aim to distinguish patients with breast, kidney, and prostate cancers from healthy controls. The mean sensitivity, specificity, and accuracy of the lipid profiling approach were 85%, 95%, and 92% for kidney cancer; 91%, 97%, and 94% for breast cancer; and 87%, 95%, and 92% for prostate cancer. No association of statistical models with tumor stage is observed. The statistically most significant lipid species for the differentiation of cancer types studied are CE 16:0, Cer 42:1, LPC 18:2, PC 36:2, PC 36:3, SM 32:1, and SM 41:1 These seven lipids represent a potential biomarker panel for kidney, breast, and prostate cancer screening, but a further verification step in a prospective study has to be performed to verify clinical utility.

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.

Metabolomic analysis of plasma from breast tumour patients. A pilot study

BACKGROUND

Patients at risk of breast cancer are submitted to mammography, resulting in a classification of the lesions following the Breast Imaging Reporting and Data System (BI-RADS®). Due to BI-RADS 3 classification problems and the great uncertainty of the possible evolution of this kind of tumours, the integration of mammographic imaging with other techniques and markers of pathology, as metabolic information, may be advisable.

DESIGN AND METHODS

Our study aims to evaluate the possibility to quantify by gas chromatography-mass spectrometry (GC-MS) specific metabolites in the plasma of patients with mammograms classified from BI-RADS 3 to BI-RADS 5, to find similarities or differences in their metabolome. Samples from BI-RADS 3 to 5 patients were compared with samples from a healthy control group. This pilot project aimed at establishing the sensitivity of the metabolomic classification of blood samples of patients undergoing breast radiological analysis and to support a better classification of mammographic cases.

RESULTS

Metabolomic analysis revealed a panel of metabolites more abundant in healthy controls, as 3-aminoisobutyric acid, cholesterol, cysteine, stearic, linoleic and palmitic fatty acids. The comparison between samples from BI-RADS 3 and BI-RADS 5 patients, revealed the importance of 4-hydroxyproline, found in higher amount in BI-RADS 3 subjects.

CONCLUSION

Although the low sample number did not allow the attainment of high validated statistical models, some interesting data were obtained, revealing the potential of metabolomics for an improvement in the classification of different mammographic lesions.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

Predicting human health from biofluid-based metabolomics using machine learning

Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

Evaluation of the antitumor effects of PP242 in a colon cancer xenograft mouse model using comprehensive metabolomics and lipidomics

PP242, an inhibitor of mechanistic target of rapamycin (mTOR), displays potent anticancer effects against various cancer types. However, the underlying metabolic mechanism associated with the PP242 effects is not clearly understood. In this study, comprehensive metabolomics and lipidomics investigations were performed using ultra-high-performance chromatography-Orbitrap-mass spectrometry (UHPLC-Orbitrap-MS) in plasma and tumor tissue to reveal the metabolic mechanism of PP242 in an LS174T cell-induced colon cancer xenograft mouse model. After 3 weeks of PP242 treatment, a reduction in tumor size and weight was observed without any critical toxicities. According to results, metabolic changes due to the effects of PP242 were not significant in plasma. In contrast, metabolic changes in tumor tissues were very significant in the PP242-treated group compared to the xenograft control (XC) group, and revealed that energy and lipid metabolism were mainly altered by PP242 treatment like other cancer inhibitors. Additionally, in this study, it was discovered that not only TCA cycle but also fatty acid β-oxidation (β-FAO) for energy metabolism was inhibited and clear reduction in glycerophospholipid was observed. This study reveals new insights into the underlying anticancer mechanism of the dual mTOR inhibitor PP242, and could help further to facilitate the understanding of PP242 effects in the clinical application.

Lipid and protein tumor markers for head and neck squamous cell carcinoma identified by imaging mass spectrometry

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. To improve pre- and post-operative diagnosis and prognosis novel molecular markers are desirable. Here we used MALDI imaging mass spectrometry (IMS) and immunohistochemistry (IHC) to seek tumor specific expression of proteins and lipids in HNSCC samples. Among low molecular weight proteins visualized, S100A8 and S100A9 were found to be expressed in the regions of tumor tissue but not in the surrounding healthy stroma of a post-operative microdissected tissue. Marker potential of S100A8 and S100A9 was confirmed by immunohistochemistry of paraffin-embedded pathological samples. Imaging lipids showed a remarkable depletion of lysophosphatidylcholine species LPC[16:0], LPC[18:2] and, in parallel, accumulation of major glycerophospholipid species PE-P[36:4], PC[32:1], PC[34:1] in neoplastic areas. This was confirmed by shotgun lipidomics of dissected healthy and tumor tissue sections. A combination of the negative (LPC[16:0]) and positive (PC[32:1], PC[34:1]) markers was also applicable to uncover tumorous character of a pre-operative biopsy. Furthermore, marker potential of lysophospholipids was supported by elevated expression levels of the lysophospholipid degrading enzyme lysophospholipase A1 (LYPLA1) in the tumor regions of paraffin-embedded HNSCC samples. Finally, experimental evidence of 3D cell spheroid tests showed that LPC[16:0] facilitates HNSCC invasion, implying that HNSCC progression in vivo may be dependent on lysophospholipid supply. Altogether, a series of novel proteins and lipid species were identified by IMS and IHC screening, which may serve as potential molecular markers for tumor diagnosis, prognosis, and may pave the way to better understand HNSCC pathophyisiology.

Plasma Lipid Profile Reveals Plasmalogens as Potential Biomarkers for Colon Cancer Screening

In this era of precision medicine, there is an increasingly urgent need for highly sensitive tests for detecting tumors such as colon cancer (CC), a silent disease where the first symptoms may take 10–15 years to appear. Mass spectrometry-based lipidomics is an emerging tool for such clinical diagnosis. We used ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry operating in high energy collision spectral acquisition mode (MS^E) mode (UPLC-QTOF-MS^E) and gas chromatography (GC) to investigate differences between the plasmatic lipidic composition of CC patients and control (CTR) subjects. Key enzymes in lipidic metabolism were investigated using immuno-based detection assays. Our partial least squares discriminant analysis (PLS-DA) resulted in a suitable discrimination between CTR and CC plasma samples. Forty-two statistically significant discriminating lipids were putatively identified. Ether lipids showed a prominent presence and accordingly, a decrease in glyceronephosphate O-acyltransferase (GNPAT) enzyme activity was found. A receiver operating characteristic (ROC) curve built for three plasmalogens of phosphatidylserine (PS), named PS(P-36:1), PS(P-38:3) and PS(P-40:5), presented an area under the curve (AUC) of 0.998, and sensitivity and specificity of 100 and 85.7% respectively. These results show significant differences in CC patients’ plasma lipid composition that may be useful in discriminating them from CTR individuals with a special role for plasmalogens.

Exploring the Lipidome: Current Lipid Extraction Techniques for Mass Spectrometry Analysis

In recent years, high-throughput lipid profiling has contributed to understand the biological, physiological and pathological roles of lipids in living organisms. Across all kingdoms of life, important cell and systemic processes are mediated by lipids including compartmentalization, signaling and energy homeostasis. Despite important advances in liquid chromatography and mass spectrometry, sample extraction procedures remain a bottleneck in lipidomic studies, since the wide structural diversity of lipids imposes a constrain in the type and amount of lipids extracted. Differences in extraction yield across lipid classes can induce a bias on down-stream analysis and outcomes. This review aims to summarize current lipid extraction techniques used for untargeted and targeted studies based on mass spectrometry. Considerations, applications, and limitations of these techniques are discussed when used to extract lipids in complex biological matrices, such as tissues, biofluids, foods, and microorganisms.

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).

Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

Evaluation of MDA-MB-468 Cell Culture Media Analysis in Predicting Triple-Negative Breast Cancer Patient Sera Metabolic Profiles

Triple-negative breast cancer (TNBC) is characterized by limited survival, poor prognosis, and high recurrence. Understanding the metabolic adaptations of TNBC could help reveal improved treatment regiments. Here we performed a comprehensive ¹H NMR metabolic characterization of the MDA-MB-468 cell line, a commonly used model of TNBC, followed by an analysis of serum samples obtained from TNBC patients and healthy controls. MDA-MB-468 cells were cultured, and changes in the metabolic composition of the medium were monitored for 72 h. Based on time courses, metabolites were categorized as being consumed, being produced, or showing a mixed behavior. When comparing TNBC and control samples (HC), and by using multivariate and univariate analyses, we identified nine metabolites with differing profiles). The serum of TNBC patients was characterized by higher levels of glucose, glutamine, citrate, and acetoacetate and by lower levels of lactate, alanine, tyrosine, glutamate, and acetone. A comparative analysis between MDA-MB-468 cell culture media and TNBC patients' serum identified a potential systemic response to the carcinogenesis-associated processes, highlighting that MDA-MB-468 cells footprint does not reflect metabolic changes observed in studied TNBC serum fingerprint.

Application of metabolomics in the diagnosis of breast cancer: a systematic review

Breast cancer (BC) remains the most frequent type of cancer in females worldwide. However, the pathogenesis of BC is still under the cloud, along with the huge challenge of early diagnosis, which is widely acknowledged as the key to a successful therapy. Metabolomics, a newborn innovative technique in recent years, has demonstrated great potential in cancer-related researches. The aim of this review is to look back on clinical and cellular metabolomic studies in the diagnosis of BC over the past decade, and provide a systematic summary of metabolic biomarkers and pathways related to BC diagnosis.

Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population

Triple negative breast cancer (TNBC) is a devastating cancer disease characterized by its poor prognosis, distinct metastatic patterns, and aggressive biological behavior. Research indicates that the prevalence and presentation of TNBC varies among races, with Asian TNBC patients more commonly presenting with large invasive tumors, high node positivity, and high histologic grade. In this work, we applied ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomics to discover metabolic signatures in Asian female TNBC patients. Serum samples from 31 TNBC patients and 31 healthy controls (CN) were involved in this study. A total of 2860 metabolic features were detected in the serum samples. Among them, 77 metabolites, whose levels were significantly different between TNBC with CN, were confirmed. Using multivariate statistical analysis, literature mining, metabolic network and pathway analysis, we performed an in-depth study of the metabolic alterations in the Asian TNBC population. In addition, we discovered a panel of metabolic signatures that are highly correlated with the 5-year survival rate of the TNBC patients. This metabolomic study provides a better understanding of the metabolic details of TNBC in the Asian population.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

n-Butylamine for Improving the Efficiency of Untargeted Mass Spectrometry Analysis of Plasma Metabolite Composition

A comparative study of the impact of n-butylamine and traditionally used additives (ammonium hydroxide and formic acid) on the efficiency of the electrospray ionization (ESI) process for the enhancement of metabolite coverage was performed by direct injection mass spectrometry (MS) analysis in negative mode. Evaluation of obtained MS data showed that n-butylamine is one of the most effective additives for the analysis of metabolite composition in ESI in negative ion mode (ESI(-)) The limitations of the use of n-butylamine and other alkylamines in the analysis of metabolic composition and a decontamination procedure that can reduce MS device contamination after their application are discussed. The proposed procedure allows the performance of high-sensitivity analysis of low-molecular-weight compounds on the same MS device in both polarities.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

2-Carba cyclic phosphatidic acid inhibits lipopolysaccharide-induced prostaglandin E2 production in a human macrophage cell line

Cyclic phosphatidic acid (cPA) is a naturally occurring phospholipid mediator that contains a unique cyclic phosphate ring at the sn-2 and sn-3 positions of its glycerol backbone. Using mouse models for multiple sclerosis (cuprizone-induced demyelination and experimental autoimmune encephalomyelitis) and traumatic brain injury, we revealed that cPA and its metabolically stabilized cPA derivative, 2-carba-cPA (2ccPA), have potential to protect against neuroinflammation. In this study, we investigated whether 2ccPA has anti-inflammatory effect on peripheral immune function or not using inflammation-induced macrophages-like cell line, THP-1 monocytes differentiated by phorbol 12-myristate 13-acetate (PMA). Lipopolysaccharide (LPS)-stimulated THP-1 cells were found to have higher expression of the mRNAs of several inflammation-related cytokines and of the enzyme cyclooxygenase-2 (Cox-2); however, when THP-1 cells were stimulated by LPS in the presence of 2ccPA, the increase in the expression of pro-inflammatory cytokine and Cox-2 mRNA was attenuated. 2ccPA treatment also decreased the amount of prostaglandin E2 (PGE2) produced by LPS-stimulated THP-1 cells and decreased expression of the mRNA of prostaglandin E receptor 2 (EP2, PTGER2), a PGE2 receptor that mediates inflammation. These results indicate that 2ccPA has anti-inflammatory properties.

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2-Carba cyclic phosphatidic acid inhibits prostaglandin E2 production.

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2-Carba cyclic phosphatidic acid has anti-inflammatory effect.

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2-Carba cyclic phosphatidic acid has effect on peripheral immune function.

Breast Cancer Metabolomics: From Analytical Platforms to Multivariate Data Analysis. A Review

Cancer is a major health issue worldwide for many years and has been increasing significantly. Among the different types of cancer, breast cancer (BC) remains the leading cause of cancer-related deaths in women being a disease caused by a combination of genetic and environmental factors. Nowadays, the available diagnostic tools have aided in the early detection of BC leading to the improvement of survival rates. However, better detection tools for diagnosis and disease monitoring are still required. In this sense, metabolomic NMR, LC-MS and GC-MS-based approaches have gained attention in this field constituting powerful tools for the identification of potential biomarkers in a variety of clinical fields. In this review we will present the current analytical platforms and their applications to identify metabolites with potential for BC biomarkers based on the main advantages and advances in metabolomics research. Additionally, chemometric methods used in metabolomics will be highlighted.

Metabolic fingerprinting in breast cancer stages through 1H NMR spectroscopy-based metabolomic analysis of plasma

Breast cancer (BC) is one of the most common malignancies among women worldwide, which is indeed associated with metabolic reprogramming. However, BC is a very complex and heterogeneous disease, which can relate with the changes in metabolic profiles during BC progression. Hence, investigating the metabolic alterations during BC stage progression may reveal the deregulated pathways and useful metabolic signatures of BC. To demonstrate the metabolic insights, we opted ¹H NMR spectroscopy based metabolomics of blood plasma of early and late stage BC (N = 72) with age and gender matched healthy subjects (N = 50). Further, the metabolic profiles were analyzed to delineate the potential signatures of BC by performing multivariate and nonparametric statistical analysis in early and late stages of BC in comparison with healthy subjects. Sixteen metabolites levels were differentially changed (p < 0.05) in the early and late stages of BC from healthy subjects. Among them, the levels of hydroxybutyrate, lysine, glutamate, glucose, N-acetyl glycoprotein, Lactate were highly distinguished in BC stages and showed a good biomarker potential using receiver-operating curves based diagnostic models. Furthermore, the significant modulation and good diagnostic performances of glutamate, N-acetyl glycoprotein and Lactate in LBC as compared to EBC give their significance in the BC progression. In general, our observations demonstrate that these panels of metabolites may act as vital component of the metabolism of early to late stage BC progression. Our results also open new avenue towards early and late stage BC diagnosis and intervention implying metabolomics approaches.