Taurine, glutamic acid and ethylmalonic acid as important metabolites for detecting human breast cancer based on the targeted metabolomics

Simultaneous detection of volatile and non-volatile metabolites in urine using UPLC-Q-Exactive Orbitrap-MS and HS-SPME/GC-HRMS: A promising strategy for improving the breast cancer diagnosis accuracy

Breast cancer (BC) is the primary cause of cancer-related deaths in women. Currently, the discovery of biomarkers primarily relies on single platform, which might overlook other potential biomarkers and lead to inaccurate diagnoses. This study aims to: (1) expand the detection range of biomarkers through multiple analytical techniques, thereby improving the accuracy of BC diagnosis, and (2) analyze the metabolic pathways of the biomarkers to explore the metabolic mechanisms underlying BC. Urine samples from BC patients and healthy controls were analyzed using two techniques: Ultra-high performance liquid chromatography combined with Quadrupole-Exactive-Orbitrap mass spectrometry (UPLC-Q-Exactive Orbitrap-MS), and headspace solid-phase microextraction combined with gas chromatography-high resolution mass spectrometry (HS-SPME/GC-HRMS). Data from each platform was analyzed independently using both univariate and multivariate statistical approaches to identify candidate biomarkers. Subsequently, a mid-level data fusion approach was applied to integrate the candidate biomarkers identified by each platform. The fused data were used to construct orthogonal partial least squares discriminant analysis (OPLS-DA) models and random forest (RF) models, which were then compared against models based on individual platform. The fused RF and OPLS-DA models demonstrated enhanced diagnostic accuracy compared to the individual model. Integrating GC-HRMS and UPLC-Q-Exactive Orbitrap-MS achieved the best performance, with an AUC value of 0.967, sensitivity of 86.37 %, and specificity of 89.19 %. Metabolic pathway analysis revealed that 10 metabolic pathways exert an impact on BC. Four pathways-pyruvate metabolism, sulfur metabolism, taurine and hypotaurine metabolism, and tyrosine metabolism-were found to be associated with BC in both metabolomics and volatolomics studies, indicating that these pathways play pivotal roles in BC. This study confirmed the potential of merging multi-platforms to enhance the accuracy of BC diagnosis, offering new avenues for understanding the metabolic mechanisms of BC.

Application of Metabolic Biomarkers in Breast Cancer: A Literature Review

Breast cancer is the most common cancer and the second leading cause of cancer death in women worldwide. Novel biomarkers for early diagnosis, treatment, and prognosis in breast cancer are needed and extensively studied. Metabolites, which are small molecules produced during metabolic processes, provide links between genetics, environment, and phenotype, making them useful biomarkers for diagnosis, prognosis, and disease classification. With recent advancements in metabolomics techniques, metabolomics research has expanded, which has led to significant progress in biomarker research. In breast cancer, alterations in metabolic pathways result in distinct metabolomic profiles that can be harnessed for biomarker discovery. Studies using mass spectrometry and nuclear magnetic resonance spectroscopy have helped identify significant changes in metabolites, such as amino acids, lipids, and organic acids, in the tissues, blood, and urine of patients with breast cancer, highlighting their potential as biomarkers. Integrative analysis of these metabolite biomarkers with existing clinical parameters is expected to improve the accuracy of breast cancer diagnosis and to be helpful in predicting prognosis and treatment responses. However, to apply these findings in clinical practice, larger cohorts for validation and standardized analytical methods for QC are necessary. In this review, we provide information on the current state of metabolite biomarker research in breast cancer, highlighting key findings and their clinical implications.

Metabolomics-Driven Biomarker Discovery for Breast Cancer Prognosis and Diagnosis

Breast cancer is a cancer with global prevalence and a surge in the number of cases with each passing year. With the advancement in science and technology, significant progress has been achieved in the prevention and treatment of breast cancer to make ends meet. The scientific intradisciplinary subject of "metabolomics" examines every metabolite found in a cell, tissue, system, or organism from different sources of samples. In the case of breast cancer, little is known about the regulatory pathways that could be resolved through metabolic reprogramming. Evidence related to the significant changes taking place during the onset and prognosis of breast cancer can be obtained using metabolomics. Innovative metabolomics approaches identify metabolites that lead to the discovery of biomarkers for breast cancer therapy, diagnosis, and early detection. The use of diverse analytical methods and instruments for metabolomics includes Magnetic Resonance Spectroscopy, LC/MS, UPLC/MS, etc., which, along with their high-throughput analysis, give insights into the metabolites and the molecular pathways involved. For instance, metabolome research has led to the discovery of the glutamate-to-glutamate ratio and aerobic glycolysis as biomarkers in breast cancer. The present review comprehends the updates in metabolomic research and its processes that contribute to breast cancer prognosis and metastasis. The metabolome holds a future, and this review is an attempt to amalgamate the present relevant literature that might yield crucial insights for creating innovative therapeutic strategies aimed at addressing metastatic breast cancer.

Largescale multicenter study of a serum metabolite biomarker panel for the diagnosis of breast cancer

Breast cancer (BC) is currently the most prevalent malignancy worldwide, and finding effective non-invasive biomarkers for routine clinical detection of BC remains a significant challenge. Here, we performed non-targeted and targeted metabolomics analysis on the screening, training and validation cohorts of serum samples from 1,947 participants. A metabolite biomarker model including glutamate, erythronate, docosahexaenoate, propionylcarnitine, and patient's age was established for detecting BC. This model demonstrated better diagnostic performance than carbohydrate antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) alone in discriminating BC from healthy controls both in the training and validation cohorts [area under the curve (AUC), 0.954; sensitivity, 87.1% and specificity, 93.5% for the training cohort and 0.834, 68.3%, and 85.2%, respectively, for the validation cohort 1]. This study has established a noninvasive approach for the detection of BC, which shows potential as a suitable supplement to the clinical screening methods currently employed for BC.

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Metabolomics assisted by transcriptomics analysis to reveal metabolic characteristics and potential biomarkers associated with treatment response of neoadjuvant therapy with TCbHP regimen in HER2 + breast cancer

BACKGROUND

This study aimed to explore potential indicators associated with the neoadjuvant efficacy of TCbHP regimen (taxane, carboplatin, trastuzumab, and pertuzumab) in HER2 + breast cancer (BrCa) patients.

METHODS

A total of 120 plasma samples from 40 patients with HER2 + BrCa were prospectively collected at three treatment times of neoadjuvant therapy (NAT) with TCbHP regimen. Serum metabolites were analyzed based on LC-MS and GC-MS data. Random forest was used to establish predictive models based on pre-therapeutic differentially expressed metabolites. Time series analysis was used to obtain potential monitors for treatment response. Transcriptome analysis was performed in nine available pre‑therapeutic specimens of core needle biopsies. Integrated analyses of metabolomics and transcriptomics were also performed in these nine patients. qRT-PCR was used to detect altered genes in trastuzumab-sensitive and trastuzumab-resistant cell lines.

RESULTS

Twenty-one patients achieved pCR, and 19 patients achieved non-pCR. There were significant differences in plasma metabolic profiles before and during treatment. A total of 100 differential metabolites were identified between pCR patients and non-pCR patients at baseline; these metabolites were markedly enriched in 40 metabolic pathways. The area under the curve (AUC) values for discriminating the pCR and non-PCR groups from the NAT of the single potential metabolite [sophorose, N-(2-acetamido) iminodiacetic acid, taurine and 6-hydroxy-2-aminohexanoic acid] or combined panel of these metabolites were greater than 0.910. Eighteen metabolites exhibited potential for monitoring efficacy. Several validated genes might be associated with trastuzumab resistance. Thirty-nine altered pathways were found to be abnormally expressed at both the transcriptional and metabolic levels.

CONCLUSION

Serum-metabolomics could be used as a powerful tool for exploring informative biomarkers for predicting or monitoring treatment efficacy. Metabolomics integrated with transcriptomics analysis could assist in obtaining new insights into biochemical pathophysiology and might facilitate the development of new treatment targets for insensitive patients.

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.

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

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

The 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.

Advancements in Analyzing Tumor Metabolites through Chemical Derivatization-Based Chromatography

Understanding the metabolic abnormalities of tumors is crucial for early diagnosis, prognosis, and treatment. Accurate identification and quantification of metabolites in biological samples are essential to investigate the relationship between metabolite variations and tumor development. Common techniques like LC-MS and GC-MS face challenges in measuring aberrant metabolites in tumors due to their strong polarity, isomerism, or low ionization efficiency during MS detection. Chemical derivatization of metabolites offers an effective solution to overcome these challenges. This review focuses on the difficulties encountered in analyzing aberrant metabolites in tumors, the principles behind chemical derivatization methods, and the advancements in analyzing tumor metabolites using derivatization-based chromatography. It serves as a comprehensive reference for understanding the analysis and detection of tumor metabolites, particularly those that are highly polar and exhibit low ionization efficiency.

Updates on Triple-Negative Breast Cancer in Type 2 Diabetes Mellitus Patients: From Risk Factors to Diagnosis, Biomarkers and Therapy

Triple-negative breast cancer (TNBC) is usually the most malignant and aggressive mammary epithelial tumor characterized by the lack of expression for estrogen receptors and progesterone receptors, and the absence of epidermal growth factor receptor (HER)2 amplification. Corresponding to 15-20% of all breast cancers and well-known by its poor clinical outcome, this negative receptor expression deprives TNBC from targeted therapy and makes its management therapeutically challenging. Type 2 diabetes mellitus (T2DM) is the most common ageing metabolic disorder due to insulin deficiency or resistance resulting in hyperglycemia, hyperinsulinemia, and hyperlipidemia. Due to metabolic and hormonal imbalances, there are many interplays between both chronic disorders leading to increased risk of breast cancer, especially TNBC, diagnosed in T2DM patients. The purpose of this review is to provide up-to-date information related to epidemiology and clinicopathological features, risk factors, diagnosis, biomarkers, and current therapy/clinical trials for TNBC patients with T2DM compared to non-diabetic counterparts. Thus, in-depth investigation of the diabetic complications on TNBC onset, development, and progression and the discovery of biomarkers would improve TNBC management through early diagnosis, tailoring therapy for a better outcome of T2DM patients diagnosed with TNBC.

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.

Association between Levels of Trimethylamine N-Oxide and Cancer: A Systematic Review and Meta-Analysis

Cancer is the second leading cause of death in the world. Reports on the effect of Trimethylamine-N-oxide (TAMO), a small amine oxide generated by gut microbial metabolism of choline, betaine, and carnitine, on cancer are inconsistent. Therefore, this systematic review and meta-analysis summarize the effect of TAMO on cancer incidence. A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase. Data were pooled using the random-effects method and were expressed as weighted mean difference (WMD) and 95% confidence intervals (CI). The pooled results of 16 studies, including 5930 participants, showed that the association between TMAO levels and cancer incidence is insignificant (Odds Ratio: 0.97, 95% CI: (0.64, 1.46), P-value = 0.871). Subgroup analysis showed that urinary TMAO levels were negatively associated with cancer incidence; in contrast, a direct and positive association was observed between serum TMAO levels and cancer incidence. However, "gender" and the "TMAO measuring method" were the potential sources of discrepancies. Meta-regression analysis did not reveal any significant association between duration of studies, age, female ratio, subjects-control, and subjects-case. The present study demonstrates that serum TAMO levels were insignificantly associated with cancer incidence.

Metabonomic analysis of human and 12 kinds of livestock mature milk

Mature milk, as a nutrient-rich endogenous metabolite, has various beneficial effects on the human body. In order to investigate the specific nutrients provided by different dairy products to humans, we used UHPLC-Q-TOF MS to analyze the highly significantly differentially expressed metabolites in 13 species of mammalian mature milk, which were grouped into 17 major metabolite classes with 1992 metabolites based on chemical classification. KEGG shows that 5 pathways in which differentially significant metabolites are actively involved are ABC transporters, Purine metabolism, Pyrimidine metabolism, Phosphotransferase system, Galactose metabolism. The study found that pig milk and goat milk are closer to human milk and contain more nutrients that are beneficial to human health, followed by camel milk and cow milk. In the context of dairy production, the development of goat milk is more likely to meet human needs and health.

Breast adipose metabolites mediates the association of tetrabromobisphenol a with breast cancer: A case-control study in Chinese population

Studies exploring the association of tetrabromobisphenol A (TBBPA) with breast cancer and related mechanisms are limited. To investigate the relationship between TBBPA levels in breast adipose and breast cancer, we carried out case-control research. As well as further examine the mediating role of adipose metabolites between TBBPA and breast cancer using the metabolomics approach. In this study, the concentration of TBBPA was determined utilizing ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) after a solid phase extraction (SPE) pretreatment. High-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) was employed to analyze adipose metabolomics. Evaluation of metabolites linked to TBBPA exposure and breast cancer was performed utilizing mediation analysis. With an estimated OR (95%CI) of 1.153 (1.023, 1.299), TBBPA was firmly linked with breast cancer. We also used propensity score matching analysis and sensitivity analysis to reduce the effect of confounding factors on the results. Metabolomics of adipose suggested significant perturbation in the linoleic acid metabolism pathway. In addition, for PC (16:0/16:0) as phospholipids, a mediation effect on the associations of TBBPA exposure with breast cancer risks was observed (estimated mediation percentage: 56.58%). Understanding the relationship between TBBPA exposure and the risk of breast cancer may be facilitated by the findings, which point to potential mediation metabolites.

Metabolomic study of serum in patients with invasive ductal breast carcinoma with LC-MS/MS approach

BACKGROUND

Invasive ductal carcinoma (IDC) is the most common type of breast cancer so its early detection can lead to a significant decrease in mortality rate. However, prognostic factors for IDC are not adequate and we need novel markers for the treatment of different individuals. Although positron emission tomography and magnetic resonance imaging techniques are available, they are based on morphological features that do not provide any clue for molecular events accompanying cancer progression. In recent years, "omics" approaches have been extensively developed to propose novel molecular signatures of cancers as putative biomarkers, especially in biofluids. Therefore, a mass spectrometry-based metabolomics investigation was performed to find some putative metabolite markers of IDC and potential metabolites with prognostic value related to the estrogen receptor, progesterone receptor, lymphovascular invasion, and human epidermal growth factor receptor 2.

METHODS

An untargeted metabolomics study of IDC patients was performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The multivariate principal component analysis by XCMS online built a model that could separate the study groups and define the significantly altered m/z parameters. The most important biological pathways were also identified by pathway enrichment analysis.

RESULTS

The results showed that the significantly altered metabolites in IDC serum samples mostly belonged to amino acids and lipids. The most important involved pathways included arginine and proline metabolism, glycerophospholipid metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis.

CONCLUSIONS

Significantly altered metabolites in IDC serum samples compared to healthy controls could lead to the development of metabolite-based potential biomarkers after confirmation with other methods and in large cohorts.

Prognostic Value of Salivary Biochemical Indicators in Primary Resectable Breast Cancer

Despite the fact that breast cancer was detected in the early stages, the prognosis was not always favorable. In this paper, we examined the impact of clinical and pathological characteristics of patients and the composition of saliva before treatment on overall survival and the risk of recurrence of primary resectable breast cancer. The study included 355 patients of the Omsk Clinical Oncology Center with a diagnosis of primary resectable breast cancer (T1-3N0-1M0). Saliva was analyzed for 42 biochemical indicators before the start of treatment. We have identified two biochemical indicators of saliva that can act as prognostic markers: alkaline phosphatase (ALP) and diene conjugates (DC). Favorable prognostic factors were ALP activity above 71.7 U/L and DC level above 3.93 c.u. Additional accounting for aspartate aminotransferase (AST) activity allows for forming a group with a favorable prognosis, for which the relative risk is reduced by more than 11 times (HR = 11.49, 95% CI 1.43-88.99, p = 0.01591). Salivary AST activity has no independent prognostic value. Multivariate analysis showed that tumor size, lymph nodes metastasis status, malignancy grade, tumor HER2 status, and salivary ALP activity were independent predictors. It was shown that the risk of recurrence decreased with menopause and increased with an increase in the size of the primary tumor and lymph node involvement. Significant risk factors for recurrence were salivary ALP activity below 71.7 U/L and DC levels below 3.93 c.u. before treatment. Thus, the assessment of biochemical indicators of saliva before treatment can provide prognostic information comparable in importance to the clinicopathological characteristics of the tumor and can be used to identify a risk group for recurrence in primary resectable breast cancer.

Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study

Breast cancer (BC) is one of the leading causes of cancer mortality in women worldwide, and therefore, novel biomarkers for early disease detection are critically needed. We performed herein an untargeted plasma metabolomic profiling of 55 BC patients and 55 healthy controls (HC) using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). Pre-processed data revealed 2494 ions in total. Data matrices’ paired t-tests revealed 792 ions (both positive and negative) which presented statistically significant changes (FDR < 0.05) in intensity levels between cases versus controls. Metabolites identified with putative names via MetaboQuest using MS/MS and mass-based approaches included amino acid esters (i.e., N-stearoyl tryptophan, L-arginine ethyl ester), dipeptides (ile-ser, met-his), nitrogenous bases (i.e., uracil derivatives), lipid metabolism-derived molecules (caproleic acid), and exogenous compounds from plants, drugs, or dietary supplements. LASSO regression selected 16 metabolites after several variables (TNM Stage, Grade, smoking status, menopausal status, and race) were adjusted. A predictive conditional logistic regression model on the 16 LASSO selected ions provided a high diagnostic performance with an area-under-the-curve (AUC) value of 0.9729 (95% CI 0.96−0.98) on all 55 samples. This study proves that BC possesses a specific metabolic signature that could be exploited as a novel metabolomics-based approach for BC detection and characterization. Future studies of large-scale cohorts are needed to validate these findings.

Metabolic Characteristics and M2 Macrophage Infiltrates in Invasive Nonfunctioning Pituitary Adenomas

OBJECTIVE

The aim of this study was to investigate the metabolic differences between invasive and non-invasive nonfunctioning pituitary adenomas (NFPAs), determine the expression of an M2 macrophage marker in NFPAs, and analyze the effects of metabolic changes in invasive NFPAs on M2 macrophage infiltrates.

METHODS

Tissue samples of NFPAs from patients who underwent transsphenoidal or craniotomy surgery from January 2021 to August 2021 were collected. NFPA tissues were analyzed based on a gas chromatography-mass spectrometry non-targeted metabolomics platform, and immunohistochemical staining for M2 macrophage marker CD206 was performed.

RESULTS

We evaluated 15 invasive and 21 non-invasive NFPAs. A total of 22 metabolites were identified through non-targeted metabolomics analysis. Among them, the expression of 1-octadecanol, inosine 5'-monophosphate, adenosine 5'-monophosphate, guanosine 5'-monophosphate, creatinine, desmosterol, taurine, hypotaurine, lactic acid, and succinic acid was upregulated in invasive NFPAs, while that of 1-oleoylglycerol, arachidonic acid, cis-11-eicosenoic acid, docosahexaenoic acid, glyceric acid, hypoxanthine, linoleic acid, lysine, oleic acid, uracil, valine, and xanthine was downregulated. Immunohistochemical analysis suggested that the number of CD206-positive cells was higher in invasive NFPAs than in non-invasive NFPAs.

CONCLUSION

Invasive and non-invasive NFPAs showed distinct metabolite profiles. The levels of succinic acid and lactic acid were higher in invasive NFPAs, and the high expression of the M2 macrophage marker was verified in invasive NFPAs.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Metabolomics analysis identifies lysine and taurine as candidate prognostic biomarkers for AML-M2 patients

There is an ongoing search for potential biomarkers for acute myeloid leukemia (AML) patients using metabolic analysis. However, only few studies to date have focused on bone marrow samples or a specific subtype of AML. In the present study, we used gas chromatography time-of-flight mass spectrometry of plasma and bone marrow supernatants to compare the metabolic characteristics of patients with AML with maturation (AML-M2). This approach identified significantly altered metabolites. We next performed pathway analysis and determined relative mRNA expression by qRT-PCR. Our results show that lysine, methionine and serine were significantly decreased in AML-M2 patients compared with healthy control. Moreover, plasma abundance of lysine was negatively associated with patients' risk stratification. Taurine had higher plasma abundance in AML-M2 patients and plasma level of taurine was positively related with AML-M2 risk status, while the expression level of taurine transporter showed a negative correlation. Receiver operating characteristic curve analysis showed these four metabolites had high diagnostic value with lysine showing the highest sensitivity and specificity. These results suggest that plasma abundances of lysine and taurine may serve as potential metabolic biomarkers for the prognosis of patients with AML-M2.

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.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Metabolomics: a promising diagnostic and therapeutic implement for breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer among women and the leading cause of cancer death. Despite the advent of numerous diagnosis and treatment methods in recent years, this heterogeneous disease still presents great challenges in early diagnosis, curative treatments and prognosis monitoring. Thus, finding promising early diagnostic biomarkers and therapeutic targets and approaches is meaningful. Metabolomics, which focuses on the analysis of metabolites that change during metabolism, can reveal even a subtle abnormal change in an individual. In recent decades, the exploration of cancer-related metabolomics has increased. Metabolites can be promising biomarkers for the screening, response evaluation and prognosis of BC. In this review, we summarized the workflow of metabolomics, described metabolite signatures based on molecular subtype as well as reclassification and then discussed the application of metabolomics in the early diagnosis, monitoring and prognosis of BC to offer new insights for clinicians in breast cancer diagnosis and treatment.

Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination

PURPOSE

The aim of this study is to explore new salivary biomarkers to discriminate breast cancer patients from healthy controls.

METHODS

Saliva samples were collected after 9 h fasting and were immediately stored at - 80 °C. Capillary electrophoresis and liquid chromatography with mass spectrometry were used to quantify hundreds of hydrophilic metabolites. Conventional statistical analyses and artificial intelligence-based methods were used to assess the discrimination abilities of the quantified metabolites. A multiple logistic regression (MLR) model and an alternative decision tree (ADTree)-based machine learning method were used. The generalization abilities of these mathematical models were validated in various computational tests, such as cross-validation and resampling methods.

RESULTS

One hundred sixty-six unstimulated saliva samples were collected from 101 patients with invasive carcinoma of the breast (IC), 23 patients with ductal carcinoma in situ (DCIS), and 42 healthy controls (C). Of the 260 quantified metabolites, polyamines were significantly elevated in the saliva of patients with breast cancer. Spermine showed the highest area under the receiver operating characteristic curves [0.766; 95% confidence interval (CI) 0.671-0.840, P < 0.0001] to discriminate IC from C. In addition to spermine, polyamines and their acetylated forms were elevated in IC only. Two hundred each of two-fold, five-fold, and ten-fold cross-validation using different random values were conducted and the MLR model had slightly better accuracy. The ADTree with an ensemble approach showed higher accuracy (0.912; 95% CI 0.838-0.961, P < 0.0001). These prediction models also included spermine as a predictive factor.

CONCLUSIONS

These data indicated that combinations of salivary metabolomics with the ADTree-based machine learning methods show potential for non-invasive screening of breast cancer.

Metabolomics study of oral cancers

BACKGROUND

Oral cancer is one of the most frequently occurring cancers. Metabolic reprogramming is an important hallmark of cancer. Metabolomics characterizes all the small molecules in a biological sample, and a complete set of small molecules in such sample is referred as metabolome. Nuclear magnetic resonance spectroscopy and mass spectrometry are two widely used techniques in metabolomics studies. Increasing evidence demonstrates that metabolomics techniques can be used to explore the metabolic signatures in oral cancer. Elucidation of metabolic alterations in oral cancer is also important for the understanding of its pathological mechanisms.

AIM OF REVIEW

In this paper, we summarize the latest progress of metabolomics study in oral cancer and provide the suggestions for the future studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The metabolomics studies in saliva, serum, and tumor tissues revealed the existence of metabolic signatures in bio-fluids and tissues of oral cancer, and several tumor-specific metabolites identified in individual study could discriminate oral cancer from healthy controls or precancerous lesions, which are potential biomarkers for the screening or early diagnosis of oral cancer. Metabolomics study of oral cancers in the future should aim to establish a routine procedure with high sensitivity, profile intracellular metabolites to find out the metabolic characteristics of tumor cells, and investigate the mechanism behind metabolomic alterations and the metabolic response of cancer cells to chemotherapy.

A plasma metabolite panel as biomarkers for early primary breast cancer detection

In recent years, metabolites have attracted substantial attention as promising novel biomarkers of various diseases. However, breast cancer plasma metabolite studies are still in their infancy. Here, we investigated the potential of metabolites to serve as minimally invasive, early detection markers of primary breast cancer. We profiled metabolites extracted from the plasma of primary breast cancer patients and healthy controls using tandem mass spectrometry (UHPLC-MS/MS and FIA-MS/MS). Two metabolites were found to be upregulated, while 16 metabolites were downregulated in primary breast cancer patients compared to healthy controls in both the training and validation cohorts. A panel of seven metabolites was selected by LASSO regression analysis. This panel could differentiate primary breast cancer patients from healthy controls, with an AUC of 0.87 (95% CI: 0.81 ~ 0.92) in the training cohort and an AUC of 0.80 (95% CI: 0.71 ~ 0.87) in the validation cohort. These significantly differentiated metabolites are mainly involved in the amino acid metabolism and breast cancer cell growth pathways. In conclusion, using a metabolomics approach, we identified metabolites that have potential value for development of a multimarker blood-based test to complement and improve early breast cancer detection. The panel identified herein might be part of a prescreening tool, especially for younger women or for closely observing women with certain risks, to facilitate decision making regarding which individuals should undergo further diagnostic tests. In the future, the combination of metabolites and other blood-based molecular marker sets, such as DNA methylation, microRNA, and cell-free DNA mutation markers, will be an attractive option.