Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges

Landscape of Innovative Methods for Early Diagnosis of Gastric Cancer: A Systematic Review

From a global perspective, gastric cancer (GC) persists as a significant healthcare issue. In the Western world, the majority of cases are discovered at late stages, when the treatment is generally unsuccessful. There are no organized screening programs outside of Asia (Japan and Republic of Korea). Traditional diagnosis techniques (such as upper endoscopy), conventional tumor markers (CEA, CA19-9, and CA72-4), radiographic imaging, and CT scanning all have drawbacks. The gold standard for the earliest detection of cancer and related premalignant lesions is still endoscopy with a proper biopsy follow-up. Since there are currently no clinically approved biomarkers for the early diagnosis of GC, the identification of non-invasive biomarkers is expected to help improve the prognosis and survival rate of these patients. The search for new screening biomarkers is currently underway. These include genetic biomarkers, such as circulating tumor cells, microRNAs, and exosomes, as well as metabolic biomarkers obtained from biofluids. Meanwhile, cutting-edge high-resolution endoscopic technologies are demonstrating promising outcomes in the visual diagnosis of mucosal lesions with the aid of linked color imaging and machine learning models. Following the PRISMA guidelines, this study examined the articles in databases such as PubMed, resulting in 167 included articles. This review discusses the currently available and emerging methods for diagnosing GC early on, as well as new developments in the endoscopic detection of early lesions of the stomach.

Circulating tumor biomarkers in early-stage breast cancer: characteristics, detection, and clinical developments

Breast cancer is the most common form of cancer in women, contributing to high rates of morbidity and mortality owing to the ability of these tumors to metastasize via the vascular system even in the early stages of progression. While ultrasonography and mammography have enabled the more reliable detection of early-stage breast cancer, these approaches entail high rates of false positive and false negative results Mammograms also expose patients to radiation, raising clinical concerns. As such, there is substantial interest in the development of more accurate and efficacious approaches to diagnosing breast cancer in its early stages when patients are more likely to benefit from curative treatment efforts. Blood-based biomarkers derived from the tumor microenvironment (TME) have frequently been studied as candidate targets that can enable tumor detection when used for patient screening. Through these efforts, many promising biomarkers including tumor antigens, circulating tumor cell clusters, microRNAs, extracellular vesicles, circulating tumor DNA, metabolites, and lipids have emerged as targets that may enable the detection of breast tumors at various stages of progression. This review provides a systematic overview of the TME characteristics of early breast cancer, together with details on current approaches to detecting blood-based biomarkers in affected patients. The limitations, challenges, and prospects associated with different experimental and clinical platforms employed in this context are also discussed at length.

Phage vs. Phage: Direct Selections of Sandwich Binding Pairs

The sandwich format immunoassay is generally more sensitive and specific than more common assay formats, including direct, indirect, or competitive. A sandwich assay, however, requires two receptors to bind non-competitively to the target analyte. Typically, pairs of antibodies (Abs) or antibody fragments (Fabs) that are capable of forming a sandwiching with the target are identified through a slow, guess-and-check method with panels of candidate binding partners. Additionally, sandwich assays that are reliant on commercial antibodies can suffer from changes to reagent quality outside the researchers' control. This report presents a reimagined and simplified phage display selection protocol that directly identifies sandwich binding peptides and Fabs. The approach yielded two sandwich pairs, one peptide-peptide and one Fab-peptide sandwich for the cancer and Parkinson's disease biomarker DJ-1. Requiring just a few weeks to identify, the sandwich pairs delivered apparent affinity that is comparable to other commercial peptide and antibody sandwiches. The results reported here could expand the availability of sandwich binding partners for a wide range of clinical biomarker assays.

Screening of hepatocellular carcinoma via machine learning based on atmospheric pressure glow discharge mass spectrometry

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high mortality rate. The diagnosis of HCC is currently based on alpha-fetoprotein detection, imaging examinations, and liver biopsy, which are expensive or invasive. Here, we developed a cost-effective, time-saving, and painless method for the screening of HCC via machine learning based on atmospheric pressure glow discharge mass spectrometry (APGD-MS). Ninety urine samples from HCC patients and healthy control (HC) participants were analyzed. The relative quantification data were utilized to train machine learning models. Neural network was chosen as the best classifier with a classification accuracy of 94%. Besides, the levels of eleven urinary carbonyl metabolites were found to be significantly different between HCC and HC, including glycolic acid, pyroglutamic acid, acetic acid, etc. The possible reasons for the regulation were tentatively proposed. This method realizes the screening of HCC via potential urine metabolic biomarkers based on APGD-MS, bringing a hopeful point-of-care diagnosis of HCC in a patient-friendly manner.

Warburg Effect Revisited: Embodiment of Classical Biochemistry and Organic Chemistry. Current State and Prospects

The Nobel Prize Winner (1931) Dr. Otto H. Warburg had established that the primary energy source of the cancer cell is aerobic glycolysis (the Warburg effect). He also postulated the hypothesis about "the prime cause of cancer", which is a matter of debate nowadays. Contrary to the hypothesis, his discovery was recognized entirely. However, the discovery had almost vanished in the heat of battle about the hypothesis. The prime cause of cancer is essential for the prevention and diagnosis, yet the effects that influence tumor growth are more important for cancer treatment. Due to the Warburg effect, a large amount of data has been accumulated on biochemical changes in the cell and the organism as a whole. Due to the Warburg effect, the recovery of normal biochemistry and oxygen respiration and the restoration of the work of mitochondria of cancer cells can inhibit tumor growth and lead to remission. Here, we review the current knowledge on the inhibition of abnormal glycolysis, neutralization of its consequences, and normalization of biochemical parameters, as well as recovery of oxygen respiration of a cancer cell and mitochondrial function from the point of view of classical biochemistry and organic chemistry.

Breast cancer detection by analyzing the volatile organic compound (VOC) signature in human urine

A rising number of authors are drawing evidence on the diagnostic capacity of specific volatile organic compounds (VOCs) resulting from some body fluids. While cancer incidence in society is on the rise, it becomes clear that the analysis of these VOCs can yield new strategies to mitigate advanced cancer incidence rates. This paper presents the methodology implemented to test whether a device consisting of an electronic nose inspired by a dog’s olfactory system and olfactory neurons is significantly informative to detect breast cancer (BC). To test this device, 90 human urine samples were collected from control subjects and BC patients at a hospital. To test this system, an artificial intelligence-based classification algorithm was developed. The algorithm was firstly trained and tested with data resulting from gas chromatography-mass spectrometry (GC–MS) urine readings, leading to a classification rate of 92.31%, sensitivity of 100.00%, and specificity of 85.71% (N = 90). Secondly, the same algorithm was trained and tested with data obtained with our eNose prototype hardware, and class prediction was achieved with a classification rate of 75%, sensitivity of 100%, and specificity of 50%.

Establishing pteridine metabolism in a progressive isogenic breast cancer cell model - part II

INTRODUCTION

Determining the biological significance of pteridines in cancer development and progression remains an important step in understanding the altered levels of urinary pteridines seen in certain cancers. Our companion study revealed that several folate-derived pteridines and lumazines correlated with tumorigenicity in an isogenic, progressive breast cancer cell model, providing direct evidence for the tumorigenic origin of pteridines.

OBJECTIVES

This study sought to elucidate the pteridine biosynthetic pathway in a progressive breast cancer model via direct pteridine dosing to determine how pteridine metabolism changes with tumorigenicity.

METHODS

First, MCF10AT breast cancer cells were dosed individually with 15 pteridines to determine which pteridines were being metabolized and what metabolic products were being produced. Second, pteridines that were significantly metabolized were dosed individually across the progressive breast cancer cell model (MCF10A, MCF10AT, and MCF10ACA1a) to determine the relationship between each metabolic reaction and breast cancer tumorigenicity.

RESULTS

Several pteridines were found to have altered metabolism in breast cancer cell lines, including pterin, isoxanthopterin, xanthopterin, sepiapterin, 6-biopterin, lumazine, and 7-hydroxylumazine (p < 0.05). In particular, isoxanthopterin and 6-biopterin concentrations were differentially expressed (p < 0.05) with respect to tumorigenicity following dosing with pterin and sepiapterin, respectively. Finally, the pteridine biosynthetic pathway in breast cancer cells was proposed based on these findings.

CONCLUSIONS

This study, along with its companion study, demonstrates that pteridine metabolism becomes disrupted in breast cancer tumor cells. This work highlights several key metabolic reactions within the pteridine biosynthetic pathway that may be targeted for further investigation and clinical applications.

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

Development of a prognostic pyroptosis-related gene signature for head and neck squamous cell carcinoma patient

Objective

Head and neck squamous cell carcinoma (HNSCC) is a major threat to public health. Pyroptosis is a form of inflammatory programmed cell death that is still incompletely understood. The role of pyroptotic cell death in HNSCC remains to be fully defined. As such, the present study was developed to explore the potential prognostic utility of a pyroptosis-related gene (PRG) signature in HNSCC.

Methods

PRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858). The relative expression of the genes in the prognostic model was assessed via RT-qPCR in tumor and paired adjacent normal tissue samples from a 32-patient cohort. Potential predictors of patient outcomes associated with this 6-gene model were identified through topological degree analyses of a protein–protein interaction network. Moreover, the prognostic value of NLRP3 as a predictor of HNSCC patient prognosis was established through immunohistochemical (IHC) analyses of samples from 176 HNSCC patients. Lastly, in vitro studies were performed to further demonstrate the relevance of NLRP3 in the context of HNSCC development.

Results

Differentially expressed PRGs were able to readily differentiate between HNSCC tumors and normal tissues. Risk scores derived from the 6-gene PRG model were independent predictors of HNSCC patient prognosis, and genes that were differentially expressed between low- and high-risk groups were associated with tumor immunity. RT-qPCR assays also showed the potential protective role of NLRP3 in HNSCC patients. IHC analyses further supported the value of NLRP3 as a predictor of HNSCC patient outcomes. Invasion and migration assays demonstrated the potential role of NLRP3 in the inhibition of HNSCC development.

Conclusions

Overall, these results highlight a novel prognostic gene signature that offers value in the context of HNSCC patient evaluation, although additional research will be essential to elucidate the mechanisms linking these PRGs to HNSCC outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02476-3.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine

Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancers management for early diagnosis and treatment. Urine is a highly...

Urinary Biomarkers for Early Diagnosis of Lung Cancer

Lung cancer is the leading cause of cancer deaths worldwide. Its early detection has the potential to significantly impact the burden of the disease. The screening and diagnostic techniques in current use suffer from limited specificity. The need therefore arises for a reliable biomarker to identify the disease earlier, which can be integrated into a test. This test would also allow for the recurrence risk after surgery to be stratified. In this context, urine could represent a non-invasive alternative matrix, with the urinary metabolomic profile offering a potential source for the discovery of diagnostic biomarkers. This paper aims to examine the current state of research and the potential for translation into clinical practice.

Metal-coded hydrogel magnetic molecularly imprinted polymer for preconcentration and cleanup of sarcosine: Determination in urine; coupled to on-column capillary electrophoresis

In this study, sarcosine metal-coded hydrogel magnetic molecularly imprinted polymer (Hydro-MeC-MMIP) has been fabricated and coupled to on-column derivatization capillary electrophoresis (CE). As a metal-coding approach, sarcosine-Cu²⁺-ligand (Sar-Cu²⁺-L) chelate complex was introduced as a template to overcome the problems associated with the fabrication of MMIP for a small molecule having limited functional groups such as sarcosine. To our best knowledge, it is the first time that methacrylamide (MA) coated Fe₃O₄ (Fe₃O₄@MA) with abounded reactive double-bound on the surface has been used as a magnetic core in the one-pot synthesis of MMIPs. As prepared, Hydro-MeC-MMIP was characterized by different microscopic, spectroscopic, and thermal gravimetric methods. Hydro-MeC-MMIP was used to extract and preconcentrate sarcosine in the urine sample with no treatment and dilution. Sarcosine was quantified by on-column derivatization capillary electrophoresis equipped with a photodiode array detector. A mixture of thirteen amino acids was separated with a total run time of 12 min. Three structural analogs, including alanine, sarcosine, and glycine, were significantly resolved. Under optimal experimental conditions, the method's detection and quantification limits were 9.93 and 33.10 ng mL^-1, respectively. The linear range of 50-2000 ng mL^-1 and 96% recovery, along with the relative standard deviation of 6.07% (n = 6) for the target amino acid, were obtained. This method provides a simple, low-cost, fast, and efficient tool for extracting and quantifying sarcosine in the urine. The present method can address inconsistency in evaluating sarcosine as a candidate biomarker for prostate cancer with a simple CE/UV; no need for a sophisticated detection system such as a mass spectrometer.

Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-year-old men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate Curve-Resolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research.

Clinical importance of serum secreted clusterin in predicting invasive breast cancer and treatment responses

Enhanced serum secreted clusterin (sCLU) protein was associated with progression, poor prognosis and chemotherapy sensitivity evaluation in malignant patients. However, the clinical significance of serum sCLU protein levels in patients with invasive breast cancer (IBC) is unknown. In this study, the serum sCLU protein in 2648 patients with IBC was detected. The diagnostic value and treatment responses of serum sCLU protein in patients with IBC were also performed. The results showed that the serum sCLU protein level was significantly higher in IBC patients compared to the healthy controls (P < 0.0001), and strongly correlated with higher clinical tumor stage (P < 0.001), lymph node metastasis (P < 0.001), shorter overall survival (OS) (P = 0.032) and disease-free survival (DFS) (P = 0.029), respectively. Using the cutoff value of 18.46 μg/mL, the sensitivity and specificity were 86.26% and 73.46% to separate IBC patients from noncancerous and healthy controls. The postoperative patients showed lower serum sCLU levels compared to the preoperative patients (P = 0.003). The chemoresistant patients showed higher serum sCLU levels compared to the chemosensitive patients (P < 0.001). These data indicated that serum sCLU levels are effective indicators for diagnosis and chemotherapy sensitivity evaluation in patients with IBC.

Identification and prognostic value of a glycolysis-related gene signature in patients with bladder cancer

ABSTRACT

Bladder cancer (BC) is one of the most common malignancies worldwide. Several biomarkers related to the prognosis of patients with BC have previously been identified. However, these prognostic models use only one gene and are thus not reliable or accurate enough. The purpose of our study was to develop an innovative gene signature that has greater prognostic value in BC. So, in this study, we performed mRNA expression profiling of glycolysis-related genes in BC (n = 407) cohorts by mining data from The Cancer Genome Atlas (TCGA) database. The glycolysis-related gene sets were confirmed using the Gene Set Enrichment Analysis (GSEA). Using Cox regression analysis, a risk score staging model was built based on the genes that were determined to be significantly associated with BC outcome. Eventually, the system of risk score was structured to predict a patient's survival, and we identified four genes (CHPF, AK3, GALK1, and NUP188) that were associated with the outcomes of BC patients. According to the above-mentioned gene signature, patients were divided into two risk subgroups. The analysis showed that our constructed risk model was independent of clinical features and that the risk score was a highly powerful tool for predicting the overall survival (OS) of BC patients. Taking together, we identified a gene signature associated with glycolysis that could effectively predict the prognosis of BC patients. Our findings offer a new perspective for the clinical research and treatment of BC.

Analysis of urinary VOCs using mass spectrometric methods to diagnose cancer: A review

The development of non-invasive screening techniques for early cancer detection is one of the greatest scientific challenges of the 21st century. One promising emerging method is the analysis of volatile organic compounds (VOCs). VOCs are low molecular weight substances generated as final products of cellular metabolism and emitted through a variety of biological matrices, such as breath, blood, saliva and urine. Urine stands out for its non-invasive nature, availability in large volumes, and the high concentration of VOCs in the kidneys. This review provides an overview of the available data on urinary VOCs that have been investigated in cancer-focused clinical studies using mass spectrometric (MS) techniques. A literature search was conducted in ScienceDirect, Pubmed and Web of Science, using the keywords "Urinary VOCs", "VOCs biomarkers" and "Volatile cancer biomarkers" in combination with the term "Mass spectrometry". Only studies in English published between January 2011 and May 2020 were selected. The three most evaluated types of cancers in the reviewed studies were lung, breast and prostate, and the most frequently identified urinary VOC biomarkers were hexanal, dimethyl disulfide and phenol; with the latter seeming to be closely related to breast cancer. Additionally, the challenges of analyzing urinary VOCs using MS-based techniques and translation to clinical utility are discussed. The outcome of this review may provide valuable information to future studies regarding cancer urinary VOCs.

Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer

Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix™) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the Rametrix^TM screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix™ screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control Surine^TM. Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the Rametrix^TM screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, Rametrix^TM was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve Rametrix^TM models and demonstrate clinical relevance, this study demonstrates the ability of the Rametrix^TM screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.

Clinical significance of circulating tumor cells and metabolic signatures in lung cancer after surgical removal

Background

Lung cancer (LC) remains the deadliest form of cancer globally. While surgery remains the optimal treatment strategy for individuals with early-stage LC, what the metabolic consequences are of such surgical intervention remains uncertain.

Methods

Negative enrichment-fluorescence in situ hybridization (NE-FISH) was used in an effort to detect circulating tumor cells (CTCs) in pre- and post-surgery peripheral blood samples from 51 LC patients. In addition, targeted metabolomics analyses, multivariate statistical analyses, and pathway analyses were used to explore surgery-associated metabolic changes.

Results

LC patients had significantly higher CTC counts relative to healthy controls with 66.67% of LC patients having at least 1 detected CTC before surgery. CTC counts were associated with clinical outcomes following surgery. In a targeted metabolomics analysis, we detected 34 amino acids, 147 lipids, and 24 fatty acids. When comparing LC patients before and after surgery to control patients, metabolic shifts were detected via PLS-DA and pathway analysis. Further surgery-associated metabolic changes were identified when comparing LA (LC patients after surgery) and LB (LC patients before surgery) groups. We identified SM 42:4, Ser, Sar, Gln, and LPC 18:0 for inclusion in a biomarker panel for early-stage LC detection based upon an AUC of 0.965 (95% CI 0.900–1.000). This analysis revealed that SM 42:2, SM 35:1, PC (16:0/14:0), PC (14:0/16:1), Cer (d18:1/24:1), and SM 38:3 may offer diagnostic and prognostic benefits in LC.

Conclusions

These findings suggest that CTC detection and plasma metabolite profiling may be an effective means of diagnosing early-stage LC and identifying patients at risk for disease recurrence.

SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis

INTRODUCTION

While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition.

OBJECTIVES

Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools.

METHOD

Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics.

RESULTS

DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis.

CONCLUSIONS

Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.

Zeolitic imidazolate framework-8 reinforced hollow-fiber liquid-phase microextraction of free urinary biomarkers of whole grain intake followed by CE analysis

The whole grain intake is closely associated with human health. In this work, three-phase dynamic hollow-fiber liquid-phase microextraction reinforced with 0.10 mg/mL 30 nm zeolitic imidazolate framework-8 nanoparticles was introduced for purification and enrichment of free urinary metabolite biomarkers of whole grain intake. Eight milliliters of HCl (pH 3.00) and 8 μL of 300 mM NaOH solutions were used as the donor and acceptor phases, respectively. The temperature and stirring rate were kept at 25℃ and 500 rpm, and the extraction time was 40 min. The extraction process required no further desorption, and the resultant extract was directly used for electrophoretic analysis without derivatization. Based on the synergistic effect of hollow-fiber liquid-phase microextraction and the electrophoretic stacking, the enrichment factors of 3,5-dihydroxybenzoic acid and 3-(3,5-dihydroxyphenyl)-1-propionic acid reached 1018-1034 times, and their limits of detection achieved 0.33-0.67 ng/mL (S/N = 3) in urine matrix. The developed method has been successfully used for urine analysis, and the sample recovery data were in the range of 97.0-103.5%. This developed method provided an attractive alternative for the determination of urinary metabolite biomarkers of whole grain intake due to its sensitive, fast, low-cost, and environmental-friendly features.

Metabolomics profile in gastrointestinal cancers: Update and future perspectives

Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.

UHMK1 promotes gastric cancer progression through reprogramming nucleotide metabolism

UHMK1 is a nuclear serine/threonine kinase recently implicated in carcinogenesis. However, the functions and action mechanisms of UHMK1 in the pathogenesis of human gastric cancer (GC) are unclear. Here, we observed that UHMK1 was markedly upregulated in GC. UHMK1 silencing strongly inhibited GC aggressiveness. Interestingly, UHMK1-induced GC progression was mediated primarily via enhancing de novo purine synthesis because inhibiting purine synthesis reversed the effects of UHMK1 overexpression. Mechanistically, UHMK1 activated ATF4, an important transcription factor in nucleotide synthesis, by phosphorylating NCOA3 at Ser (S) 1062 and Thr (T) 1067. This event significantly enhanced the binding of NCOA3 to ATF4 and the expression of purine metabolism-associated target genes. Conversely, deficient phosphorylation of NCOA3 at S1062/T1067 significantly abrogated the function of UHMK1 in GC development. Clinically, Helicobacter pylori and GC-associated UHMK1 mutation induced NCOA3-S1062/T1067 phosphorylation and enhanced the activity of ATF4 and UHMK1. Importantly, the level of UHMK1 was significantly correlated with the level of phospho-NCOA3 (S1062/T1067) in human GC specimens. Collectively, these results show that the UHMK1-activated de novo purine synthesis pathway significantly promotes GC development.

Evaluation of heavy metals and metabolites in the urine of patients with breast cancer

Epidemiologic studies demonstrated that the environment serves a crucial role in cancer development. Heavy metals, including arsenic (As), cadmium (cd), chromium (Cr), lead and mercury, are considered to be carcinogens or co-carcinogens. Furthermore, Cd has been detected in breast cancer (BC) tissue at high concentrations. The present study aimed to investigate the correlation between heavy metals detected in urine and urine metabolome of patients with BC, and their association with cancer development. Nuclear magnetic resonance was used to determine urine metabolites and an inductively coupled plasma mass spectrometry system was used to detect heavy metals in urine samples. The results demonstrated that Cd was markedly increased in the urine of patients with BC compared with the control population (approximately 2-fold). Cr and As were also increased in the urine of patients with BC. In addition, numerous small molecule metabolites were altered in the urine of patients with BC compared with the control population. This study also demonstrated that alterations in small molecule metabolites in the urine of patients with BC were very similar to results from a previous report. These findings indicated that environmental exposure to Cd, As, or Cr could influence the urine levels of metabolites, which may be involved in BC development. Further investigation is therefore required to examine a larger range of samples from different countries or areas in order to understand the impact of heavy metals on metabolism and BC development.

Metabolomics studies in gastrointestinal cancer: a systematic review

Introduction: This systemic review provides an overview of metabolic perturbations and possible mechanisms in gastrointestinal cancer. The authors discuss emerging challenges of technical and clinical applications.Areas covered: In this systemic review, the authors summarized the currently available results of metabolomic biomarkers linked to GI cancer, and discussed the altered metabolism pathways including carbohydrate metabolism, amino acid metabolism, lipids, and nucleotide metabolism and other metabolisms. Furthermore, future efforts need to adhere to normalize analysis procedures, validate with the larger cohort and utilize multiple-omics technologies. The search was conducted in PubMed with the following search terms (biomarker, gastrointestinal cancer, colorectal cancer, and esophageal cancer) from 2013 to 2019.Expert opinion: This systemic review summarized the currently available results of metabolomic biomarkers linked to gastrointestinal cancer, and discussed the altered metabolism pathways. The authors believe that metabolomics will benefit deeper understandings of the pathogenic mechanism, discovery of biomarkers and aid the search for drug targets as we move toward the era of personalized medicine. Personalized medication for tumors can improve the curative effect, avoid side effects and medical resource waste. As a promisingtool, metabolomics that targets the entire cancer-specific metabolite network should be applied more widely in cancer research.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Metabolomics of Non-muscle Invasive Bladder Cancer: Biomarkers for Early Detection of Bladder Cancer

Background: Clinical outcomes of bladder cancer (BC) are tightly associated with the stage and grade of the initial diagnosis of BC because early detection is clearly important for patients with BC. However, the diagnostic capability of current detection methods, such as urinary cytology, cystoscopy, imageology method, and several urine-based tests, is inadequate for early detection of BC. The objective of our study is to discover novel biomarkers for detecting BC at an early stage, called non-muscle invasive (NMI) BC, using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based metabolomics.

Methods: First, morning midstream urine samples were collected from healthy adult and NMIBC patients. The LC-HRMS-based metabolomics were applied to distinguish the NMIBC group without hematuria from the controls (gender- and age-matched volunteers with normal clinically healthy index), low-grade NMIBC from the controls, and high-grade from low-grade NMIBC.

Results: A total of 284 subjects were enrolled in our study including 117 healthy adults, 80 NMIBC patients without hematuria, and 87 NMIBC patients with hematuria. The metabolite panel including dopamine 4-sulfate, MG00/1846Z,9Z,12Z,15Z/00, aspartyl-histidine, and tyrosyl-methionine was found in a discovery set, which showed the predictive ability to distinguish the NMIBC group from the control group with an area under the curve (AUC) of 0.838 in an external validation set. The AUC of the panel for low-grade NMIBC samples, which consisted of 3-hydroxy-cis-5-tetradecenoylcarnitine, 6-ketoestriol, beta-cortolone, tetrahydrocorticosterone, and heptylmalonic acid, was 0.899. The sensitivity and specificity were 0.881 and 0.786, respectively. The AUC of the panel for distinction of low-grade NMIBC with and without hematuria against high-grade NMIBC with and without hematuria were 0.827 and 0.755, respectively. In addition, metabolites involved in tryptophan metabolism were upregulated in the urine of high-grade NMIBC patients when compared with low-grade NMIBC patients with the presence or absence of hematuria.

Conclusion: The NMIBC urine metabolic profiling was able to assist in the early detection of BC. Panels of metabolites were discovered to have a potential value for high-grade NMIBC and low-grade NMIBC diagnosis as well as for NMIBC grading distinction.

Identification of metabolites in plasma for predicting survival in glioblastoma

Circulating metabolomics profiling holds prognostic potential. However, such efforts have not been extensively carried out in glioblastoma. In this study, two-step (training and testing) metabolomics profiling was conducted from the plasma samples of 159 glioblastoma patients. Metabolomics profiling was tested for correlation with 2-year overall and disease-free survivals. Arginine, methionine, and kynurenate levels were significantly associated with 2-year overall survival in both the training and testing sets. In the combined sets, elevated levels of arginine and methionine were associated with a 34% and 37% increased probability whereas kynurenate was associated with a 55% decreased probability of 2-year overall survival. These three metabolites were also significantly associated with 2-year disease-free survival. Risk scores were generated using the linear combination of levels of these significant metabolites. Glioblastoma patients with a high-risk score exhibited a 2.41-fold decreased probability of 2-year overall survival (hazard ratio (HR) = 2.41; 95% Confidence Interval (CI) = 1.20-4.93) and a 3.17-fold decreased probability of 2-year disease free survival (HR = 3.17, 95%CI = 1.42-7.54) relative to those with a low-risk score. In conclusion, we identified a unique plasma metabolite profile that is predictive of glioblastoma prognosis.