Urinary metabolomics for discovering metabolic biomarkers of laryngeal cancer using UPLC-QTOF/MS

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

Clinical diagnostic value of amino acids in laryngeal squamous cell carcinomas

Background

Early diagnosis and treatment can improve the survival rates of patients with laryngeal squamous cell carcinoma (LSCC). Therefore, it is necessary to discover new biomarkers for laryngeal cancer screening and early diagnosis.

Methods

We collected fasting plasma from LSCC patients and healthy volunteers, as well as cancer and para-carcinoma tissues from LSCC patients, and performed quantitative detection of amino acid levels using liquid chromatography-mass spectrometry. We used overall analysis and multivariate statistical analysis to screen out the statistically significant differential amino acids in the plasma and tissue samples, conducted receiver operating characteristic (ROC) analysis of the differential amino acids to evaluate the sensitivity and specificity of the differential amino acids, and finally determined the diagnostic value of amino acids for laryngeal cancer. Additionally, we identified amino acids in the plasma and tissue samples that are valuable for the early diagnosis of laryngeal cancer classified according to the tumor-node-metastasis (TNM) classification.

Results

Asparagine (Asp) and homocysteine (Hcy) were two amino acids of common significance in plasma and tissue samples, and their specificity and sensitivity analysis showed that they may be new biomarkers for the diagnosis and treatment of LSCC. According to the TNM staging system, phenylalanine (Phe) and isoleucine (Ile) were screened out in the plasma of LSCC patients at early (I and II) and advanced (III and IV) stages; ornithine hydrochloride (Orn), glutamic acid (Glu), and Glycine (Gly) were selected in the tissue. These dysregulated amino acids found in LSCC patients may be useful as clinical biomarkers for the early diagnosis and screening of LSCC.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753–0.9825), 0.7 and 1, respectively.

Urinary metabolomics for discovering metabolic biomarkers of bladder cancer by UPLC-MS

Bladder cancer (BC) is one of the most frequent cancer in the world, and its incidence is rising worldwide, especially in developed countries. Urine metabolomics is a powerful approach to discover potential biomarkers for cancer diagnosis. In this study, we applied an ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to profile the metabolites in urine from 29 bladder cancer patients and 15 healthy controls. The differential metabolites were extracted and analyzed by univariate and multivariate analysis methods. Together, 19 metabolites were discovered as differently expressed biomarkers in the two groups, which mainly related to the pathways of phenylacetate metabolism, propanoate metabolism, fatty acid metabolism, pyruvate metabolism, arginine and proline metabolism, glycine and serine metabolism, and bile acid biosynthesis. In addition, a subset of 11 metabolites of those 19 ones were further filtered as potential biomarkers for BC diagnosis by using logistic regression model. The results revealed that the area under the curve (AUC) value, sensitivity and specificity of receiving operator characteristic (ROC) curve were 0.983, 95.3% and 100%, respectively, indicating an excellent discrimination power for BC patients from healthy controls. It was the first time to reveal the potential diagnostic markers of BC by metabolomics, and this will provide a new sight for exploring the biomarkers of the other disease in the future work.

Clinical Significance of Screening Differential Metabolites in Ovarian Cancer Tissue and Ascites by LC/MS

Tumor cells not only show a vigorous metabolic state, but also reflect the disease progression and prognosis from their metabolites. To judge the progress and prognosis of ovarian cancer is generally based on the formation of ascites, or whether there is ascites recurrence during chemotherapy after ovarian cancer surgery. To explore the relationship between the production of ascites and ovarian cancer tissue, metabolomics was used to screen differential metabolites in this study. The significant markers leading to ascites formation and chemoresistance were screened by analyzing their correlation with the formation of ascites in ovarian cancer and the clinical indicators of patients, and then provided a theoretical basis. The results revealed that nine differential metabolites were screened out from 37 ovarian cancer tissues and their ascites, among which seven differential metabolites were screened from 22 self-paired samples. Sebacic acid and 20-COOH-leukotriene E4 were negatively correlated with the high expression of serum CA125. Carnosine was positively correlated with the high expression of serum uric acid. Hexadecanoic acid was negatively correlated with the high expression of serum γ-GGT and HBDH. 20a,22b-Dihydroxycholesterol was positively correlated with serum alkaline phosphatase and γ-GGT. In the chemotherapy-sensitive and chemotherapy-resistant ovarian cancer tissues, the differential metabolite dihydrothymine was significantly reduced in the chemotherapy-resistant group. In the ascites supernatant of the drug-resistant group, the differential metabolites, 1,25-dihydroxyvitamins D3-26, 23-lactonel and hexadecanoic acid were also significantly reduced. The results indicated that the nine differential metabolites could reflect the prognosis and the extent of liver and kidney damage in patients with ovarian cancer. Three differential metabolites with low expression in the drug-resistant group were proposed as new markers of chemotherapy efficacy in ovarian cancer patients with ascites.

The Ameliorative Effects of Arctiin and Arctigenin on the Oxidative Injury of Lung Induced by Silica via TLR-4/NLRP3/TGF-β Signaling Pathway

Silicosis remains one of the most serious diseases worldwide, with no effective drug for its treatment. Our research results have indicated that arctiin and arctigenin could increase the mitochondrial membrane potential, which in turn reduces the production of reactive oxygen species (ROS), blocks the polarization of macrophages, and inhibits the differentiation of myofibroblasts to reduce oxidative stress, inflammation, and fibrosis. Further, our study revealed that arctiin and arctigenin suppressed the activation of NLRP3 inflammasome through the TLR-4/Myd88/NF-κB pathway and the silica-induced secretion of TNF-α, IL-1β, TGF-β, and α-SMA. Besides, the silica-induced increase in the levels of serum ceruloplasmin and HYP was also inhibited. Results of metabolomics indicated that arctiin and arctigenin could regulate the abnormal metabolic pathways associated with the development of silicosis, which involve pantothenate and CoA biosynthesis, cysteine and methionine metabolism, linoleic acid metabolism, and arginine and proline metabolism successively. Furthermore, the analysis of metabolomics, together with network topological analysis in different phases of silicosis, revealed that urine myristic acid, serum 4-hydroxyproline, and L-arginine could be regarded as diagnosis biomarkers in the early phase and formation of pulmonary fibrosis in the latter phases of silicosis. Arctiin and arctigenin could downregulate the increased levels of myristic acid in the early phase and serum 4-hydroxyproline in the latter phase of silicosis. Interestingly, the integration of TLR-4/NLRP3/TGF-β signaling and metabolomics verified the importance of macrophage polarization in the silicosis fibrosis process. To the best of our knowledge, this is the first study reporting that arctiin and arctigenin both can ameliorate silicosis effectively, and the former is a little stronger than its aglycone arctigenin because of its high oral bioavailability, low toxicity, and multimolecular active metabolites as determined by AdmetSAR and molecular docking analysis.

Metabolomics of Oral/Head and Neck Cancer

Oral/head and neck cancer is the sixth most common human malignancies in the world. Despite the treatment advances in surgery, chemotherapy, and radiotherapy, the patient survival has not been significantly improved in the past several decades. As a new methodological approach, metabolomics may help reveal the metabolic reprogramming mechanisms underlying head and neck cancer cell proliferation, invasion, and metastasis and may be used to identify metabolite biomarkers for clinical applications of the disease. In this chapter, we briefly review recent metabolomic applications in head and neck cancer.

Site- and structure-specific characterization of the human urinary N-glycoproteome with site-determining and structure-diagnostic product ions

RATIONALE

N-glycosylation is one of the most common protein post-translational modifications; it is extremely complex with multiple glycoforms from different monosaccharide compositions, sequences, glycosidic linkages, and anomeric positions. Each glycoform functions with a particular site- and structure-specific N-glycan that can be fully characterized using state-of-the-art tandem mass spectrometry (MS/MS) and the intact N-glycopeptide database search engine GPSeeker that we recently developed. Urine has recently gained increasing attention as a non-invasive source for disease marker discovery. In this study, we report our structure-specific N-glycoproteomics study of human urine.

METHODS

We performed trypsin digestion, Zwitterionic Hydrophilic Interaction chromatography (ZIC-HILIC) enrichment, C18-RPLC/nano-ESI-MS/MS using HCD with stepped normalized collisional energies, and GPSeeker database search for a comprehensive site- and structure-specific N-glycoproteomics characterization of the human urinary N-glycoproteome at the intact N-glycopeptide level. For this, we used b/y product ion pairs from the GlcNAc-containing site-determining peptide backbone and structure-diagnostic product ions from the N-glycan moieties, respectively.

RESULTS

We identified 2986 intact N-glycopeptides with comprehensive site and structure information for the peptide backbones (amino acid sequences and N-glycosites) and the N-glycan moieties (monosaccharide compositions, sequences/linkages). The 2986 intact N-glycopeptide IDs corresponded to 754 putative N-glycan linkage structures on 419 N-glycosites of 450 peptide backbones from 327 intact N-glycoproteins. Next, 146 linkage structures and 200 N-glycosites were confirmed with structure-diagnostic and GlcNAc-containing site-determining product ions, respectively.

CONCLUSIONS

We found 106 new N-glycosites not annotated in the current UniProt database. The elution-abundance patterns of urinary intact N-glycopeptide oxonium ions (m/z 138 and 204) of the same subject were temporally stable during the day and over 6 months. These patterns are rather different among different subjects. The results implied an interesting possibility that glycopeptide oxonium ion patterns could serve as distinguishing markers between individuals and/or between physiological and pathological states.

Investigation of pathogenesis and therapeutic targets of acute myeloid leukemia based on untargeted plasma metabolomics and network pharmacology approach

Acute myeloid leukemia (AML) is a malignant disease originating from bone marrow hematopoietic stem cells, characterized by anemia, hemorrhage, fever, and infection, with low survival rate. However, the pathogenesis of AML is not fully understood at present. In this work, an integrated approach based untargeted metabolomics and network pharmacology was adopted to elucidate the pathogenesis of AML. Metabolic profiling of plasma samples from 14 patients and 16 healthy individuals were performed based on UHPLC-MS platform. As a result, 23 metabolites were identified by using the human metabolite database based on PLS-DA (partial least squares discriminant analysis) and independent sample test. And metabolic pathways related to AML mainly included fatty acid metabolism, amino acid metabolism, energy metabolism and lipid metabolism. Meanwhile, biomarkers-targets-pathways-disease network was constructed, 75 biomarker targets and 122 disease targets were identified. Furthermore, 30 pathways were predicted, some of which were consistent with these in metabolomics. This is the first time that metabolomics and network pharmacology approach have been combined to investigate the pathogenesis and therapeutic targets of AML. ALDH, CYP2E1 and CYP3A4 were potential therapeutic targets for AML, which provide available way to elucidate the pathogenesis and treatment of AML.

Cancer metabolomic markers in urine: evidence, techniques and recommendations

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.