Metabolomics of papillary thyroid carcinoma tissues: potential biomarkers for diagnosis and promising targets for therapy

Systems biology approach delineates critical pathways associated with papillary thyroid cancer: a multi-omics data analysis

BACKGROUND

Papillary thyroid cancer (PTC) is the most prevalent follicular cell-derived subtype of thyroid cancer. A systems biology approach to PTC can elucidate the mechanism by which molecular components work and interact with one another to decipher a panoramic view of the pathophysiology.

METHODOLOGY

PTC associated genes and transcriptomic data were retrieved from DisGeNET and Gene Expression Omnibus database respectively. Published proteomic and metabolomic datasets in PTC from EMBL-EBI were used. Gene Ontology and pathway analyses were performed with SNPs, differentially expressed genes (DEGs), proteins, and metabolites linked to PTC. The effect of a nucleotide substitution on a protein's function was investigated. Additionally, significant transcription factors (TFs) and kinases were identified. An integrated strategy was used to analyse the multi-omics data to determine the key deregulated pathways in PTC carcinogenesis.

RESULTS

Pathways linked to carbohydrate, protein, and lipid metabolism, along with the immune response, signaling, apoptosis, gene expression, epithelial-mesenchymal transition (EMT), and disease onset, were identified as significant for the clinical and functional aspects of PTC. Glyoxylate and dicarboxylate metabolism and citrate cycle were the most common pathways among the PTC omics datasets. Commonality analysis deciphered five TFs and fifty-seven kinases crucial for PTC genesis and progression. Core deregulated pathways, TFs, and kinases modulate critical biological processes like proliferation, angiogenesis, immune infiltration, invasion, autophagy, EMT, and metastasis in PTC.

CONCLUSION

Identified dysregulated pathways, TFs and kinases are critical in PTC and may help in systems level understanding and device specific experiments, biomarkers, and drug targets for better management of PTC.

Emerging Potential of Metabolomics in Thyroid Cancer-A Comprehensive Review

Thyroid cancer is a very common endocrine system malignancy. Nevertheless, a dearth of precise markers makes it challenging to apply precision medicine to thyroid cancer. The limitations of standard diagnosis techniques (fine-needle aspiration biopsy), such as indeterminate cases and inaccuracies in distinguishing between different types of cancers, lead to unnecessary surgeries and thus warrant the development of more discriminatory biomarkers to improve the accuracy of existing diagnostic and prognostic techniques. Moreover, individualized therapies for thyroid cancer are necessary to avoid overtreatment of indolent lesions and undertreatment of high-risk progressive disease. As thyroid cancer metabolic signatures are associated with disease aggressiveness and responsiveness to therapy, metabolomics has been recently used for diagnostic and prognostic biomarker discovery. This strategy has enabled the detection of several metabolites from tissue samples or biofluids to facilitate the classification of disease aggressiveness and to potentially assist in individualized therapies. In this review, we summarize the utilization and potential of metabolomics in thyroid cancer.

In Search of Relevant Urinary Biomarkers for Thyroid Papillary Carcinoma and Benign Thyroid Nodule Differentiation, Targeting Metabolic Profiles and Pathways via UHPLC-QTOF-ESI+-MS Analysis

BACKGROUND

Identification of specific urine metabolic profiles for patients diagnosed with papillary thyroid carcinoma (TC) vs. benign nodules (B) to identify specific biomarkers and altered pathways compared to those of healthy controls (C).

METHODS

Patient urine samples were collected, before surgery and after a histological confirmation of TC (n = 30) and B (n = 30), in parallel with sample collection from healthy controls (n = 20). The untargeted and semi-targeted metabolomic protocols were applied using UPLC-QTOF-ESI+-MS analysis, and the statistical analysis was performed using the Metaboanalyst 6.0 platform. The results for the blood biomarkers, previously published, were compared with the data obtained from urine sampling using the Venny algorithm and multivariate statistics.

RESULTS

Partial least squares discrimination, including VIP values, random forest graphs, and heatmaps (p < 0.05), together with biomarker analysis (AUROC ranking) and pathway analysis, suggested a specific model for the urinary metabolic profile and pathway alterations in TC and B vs. C, based on 190 identified metabolites in urine that were compared with the serum metabolites. By semi-targeted metabolomics, 10 classes of metabolites, considered putative biomarkers, were found to be responsible for specific alterations in the metabolic pathways, from polar molecules to lipids. Specific biomarkers for discrimination were identified in each class of metabolites that were either upregulated or downregulated when compared to those of the controls.

CONCLUSIONS

The lipidomic window was the most relevant for identifying biomarkers related to thyroid cancer and benign conditions, since this study detected a stronger involvement of lipids and selenium-related molecules for metabolic discrimination.

The Role of Lipid Metabolism Disorders in the Development of Thyroid Cancer

Thyroid cancer (TC) is a neoplasm with an increasing incidence worldwide. Its etiology is complex and based on a multi-layered interplay of factors. Among these, disorders of lipid metabolism have emerged as an important area of investigation. Cancer cells are metabolically reprogrammed to promote their rapid growth, proliferation, and survival. This reprogramming is associated with significant changes at the level of lipids, mainly fatty acids (FA), as they play a critical role in maintaining cell structure, facilitating signaling pathways, and providing energy. These lipid-related changes help cancer cells meet the increased demands of continued growth and division while adapting to the tumor microenvironment. In this review, we examine lipid metabolism at different stages, including synthesis, transport, and oxidation, in the context of TC and the effects of obesity and hormones on TC development. Recent scientific efforts have revealed disturbances in lipid homeostasis that are specific to thyroid cancer, opening up potential avenues for early detection and targeted therapeutic interventions. Understanding the intricate metabolic pathways involved in FA metabolism may provide insights into potential interventions to prevent cancer progression and mitigate its effects on surrounding tissues.

Intestinal microbiota dysbiosis and liver metabolomic changes during brain death

Background

Whether a causative link exists between brain death (BD) and intestinal microbiota dysbiosis is unclear, and the distortion in liver metabolism associated with BD requires further exploration.

Methods

A rat model of BD was constructed and sustained for 9 h (BD group, n=6). The sham group (n=6) underwent the same procedures, but the catheter was inserted into the epidural space without ballooning. Intestinal contents and portal vein plasma were collected for microbiota sequencing and microbial metabolite detection. Liver tissue was resected to investigate metabolic alterations, and the results were compared with those of a sham group.

Results

α-diversity indexes showed that BD did not alter bacterial diversity. Microbiota dysbiosis occurred after 9 h of BD. At the family level, Peptostreptococcaceae and Bacteroidaceae were both decreased in the BD group. At the genus level, Romboutsia, Bacteroides, Erysipelotrichaceae_UCG_004, Faecalibacterium, and Barnesiella were enriched in the sham group, whereas Ruminococcaceae_UCG_007, Lachnospiraceae_ND3007_group, and Papillibacter were enriched in the BD group. Short-chain fatty acids, bile acids, and 132 other microbial metabolites remained unchanged in both the intestinal contents and portal vein plasma of the BD group. BD caused alterations in 65 metabolites in the liver, of which, carbohydrates, amino acids, and organic acids accounted for 64.6%. Additionally, 80.0% of the differential metabolites were decreased in the BD group livers. Galactose metabolism was the most significant metabolic pathway in the BD group.

Conclusions

BD resulted in microbiota dysbiosis in rats; however, this dysbiosis did not alter microbial metabolites. Deterioration in liver metabolic function during extended periods of BD may reflect a continuous worsening in energy deficiency.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Metabolomics-A Tool to Find Metabolism of Endocrine Cancer

Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.

Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy

Background

The incidence of esophageal squamous cell carcinoma in China ranks first in the world. The early diagnosis technology is underdeveloped, and the prognosis is poor, which seriously threatens the quality of life of the Chinese people. Epidemiological findings are related to factors such as diet, living habits, and age. The specific mechanism is not clear yet. Metabolomics is a kind of omics that simultaneously and quantitatively analyzes the comprehensive profile of metabolites in living systems. It has unique advantages in the study of the diagnosis and pathogenesis of tumor-related diseases, especially in the search for biomarkers. Therefore, it is desirable to perform metabolic profiling analysis of cancer tissues through metabolomics to find potential biomarkers for the diagnosis and treatment of esophageal squamous cell carcinoma.

Methods

HPLC-TOF-MS/MS technology and Illumina Hiseq Xten Sequencing was used for the analysis of 210 pairs of matched esophageal squamous cell carcinoma tissues and normal tissues in Zhenjiang City, Jiangsu Province, a high-incidence area of esophageal cancer in China. Bioinformatics analysis was also performed.

Results

Through metabolomic and transcriptomic analysis, this study found that a total of 269 differential metabolites were obtained in esophageal squamous cell carcinoma and normal tissues, and 48 differential metabolic pathways were obtained through KEGG enrichment analysis. After further screening and identification, 12 metabolites with potential biomarkers to differentiate esophageal squamous cell carcinoma from normal tissues were obtained.

Conclusions

From the metabolomic data, 4 unknown compounds were found to be abnormally expressed in esophageal squamous cell carcinoma for the first time, such as 9,10-epoxy-12,15-octadecadienoate; 3 metabolites were found in multiple abnormal expression in another tumor, but upregulation or downregulation was found for the first time in esophageal cancer, such as oleoyl glycine; at the same time, it was further confirmed that five metabolites were abnormally expressed in esophageal squamous cell carcinoma, which was similar to the results of other studies, such as PE.

Gas chromatography-mass spectrometry-based untargeted metabolomics reveals metabolic perturbations in medullary thyroid carcinoma

Medullary thyroid cancer (MTC) is a rare tumor that arises from parafollicular cells within the thyroid gland. The molecular mechanism underlying MTC has not yet been fully understood. Here, we aimed to perform plasma metabolomics profiling of MTC patients to explore the perturbation of metabolic pathways contributing to MTC tumorigenesis. Plasma samples from 20 MTC patients and 20 healthy subjects were obtained to carry out an untargeted metabolomics by gas chromatography–mass spectrometry. Multivariate and univariate analyses were employed as diagnostic tools via MetaboAnalyst and SIMCA software. A total of 76 features were structurally annotated; among them, 13 metabolites were selected to be differentially expressed in MTC patients compared to controls (P < 0.05). These metabolites were mainly associated with the biosynthesis of unsaturated fatty acids and amino acid metabolisms, mostly leucine, glutamine, and glutamate, tightly responsible for tumor cells' energy production. Moreover, according to the receiver operating characteristic curve analysis, metabolites with the area under the curve (AUC) value up to 0.90, including linoleic acid (AUC = 0.935), linolenic acid (AUC = 0.92), and leucine (AUC = 0.948) could discriminate MTC from healthy individuals. This preliminary work contributes to existing knowledge of MTC metabolism by providing evidence of a distinctive metabolic profile in MTC patients relying on the metabolomics approach.

Identifying potential metabolic tissue biomarkers for papillary thyroid cancer in different iodine nutrient regions

PURPOSE

To investigate the applicability of metabolomics to select thyroid cancer-associated biomarkers and discover the effects of iodine on metabolic changes in thyroid cancer.

METHODS

In this study, a total of 33 papillary thyroid cancer (PTC) patients from areas with iodine excess and 32 PTC patients from areas with adequate iodine were recruited, and their cancerous tissue and paracancerous tissue were collected. These specimens were analyzed by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS) in conjunction with multivariate statistical analysis.

RESULTS

Good separations were obtained for PTC tissue vs. paracancerous tissue, and 15 metabolites, including L-octanoylcarnitine, N-arachidonoylglycine, and others were found to be disturbed in PTC tissue. Moreover, the metabolic profile presented considerable separation between PTC tissue from different iodine areas, and 15 metabolomic biomarkers were found to be differentially expressed. Among them, 10 metabolites, including arachidonoylcarnitine and LysoPCs, were related to thyroid cancer and excess iodine. These biomarkers play a role in arachidonic acid metabolism pathways and others. In addition, biomarkers such as 3,5-tetradecadiencarnitine and oxidized glutathione were significantly correlated with thyroid function, and biomarkers such as L-octanoylcarnitine and arachidonic acid were significantly correlated with the clinical characteristics of PTC.

CONCLUSIONS

Distinct differences in metabolic profiles were found to exist between PTCs from areas with different levels of iodine nutrition. The identified biomarkers have significant potential for diagnosing PTC and investigating its underlying mechanisms.

A Canadian Study of Cisplatin Metabolomics and Nephrotoxicity (ACCENT): A Clinical Research Protocol

Background:

Cisplatin, a chemotherapy used to treat solid tumors, causes acute kidney injury (AKI), a known risk factor for chronic kidney disease and mortality. AKI diagnosis relies on biomarkers which are only measurable after kidney damage has occurred and functional impairment is apparent; this prevents timely AKI diagnosis and treatment. Metabolomics seeks to identify metabolite patterns involved in cell tissue metabolism related to disease or patient factors. The A Canadian study of Cisplatin mEtabolomics and NephroToxicity (ACCENT) team was established to harness the power of metabolomics to identify novel biomarkers that predict risk and discriminate for presence of cisplatin nephrotoxicity, so that early intervention strategies to mitigate onset and severity of AKI can be implemented.

Objective:

Describe the design and methods of the ACCENT study which aims to identify and validate metabolomic profiles in urine and serum associated with risk for cisplatin-mediated nephrotoxicity in children and adults.

Design:

Observational prospective cohort study.

Setting:

Six Canadian oncology centers (3 pediatric, 1 adult and 2 both).

Patients:

Three hundred adults and 300 children planned to receive cisplatin therapy.

Measurements:

During two cisplatin infusion cycles, serum and urine will be measured for creatinine and electrolytes to ascertain AKI. Many patient and disease variables will be collected prospectively at baseline and throughout therapy. Metabolomic analyses of serum and urine will be done using mass spectrometry. An untargeted metabolomics approach will be used to analyze serum and urine samples before and after cisplatin infusions to identify candidate biomarkers of cisplatin AKI. Candidate metabolites will be validated using an independent cohort.

Methods:

Patients will be recruited before their first cycle of cisplatin. Blood and urine will be collected at specified time points before and after cisplatin during the first infusion and an infusion later during cancer treatment. The primary outcome is AKI, defined using a traditional serum creatinine-based definition and an electrolyte abnormality-based definition. Chart review 3 months after cisplatin therapy end will be conducted to document kidney health and survival.

Limitations:

It may not be possible to adjust for all measured and unmeasured confounders when evaluating prediction of AKI using metabolite profiles. Collection of data across multiple sites will be a challenge.

Conclusions:

ACCENT is the largest study of children and adults treated with cisplatin and aims to reimagine the current model for AKI diagnoses using metabolomics. The identification of biomarkers predicting and detecting AKI in children and adults treated with cisplatin can greatly inform future clinical investigations and practices.

Serum metabolomics of Bama miniature pigs bitten by Bungarus multicinctus

Bungarus multicinctus is one of the top ten venomous snakes in China, and its bite causes acute and severe diseases, but its pathophysiology remains poorly elucidated. Thus, an animal model of Bungarus multicinctus bite was established by intramuscular injection of 30μg/kg of Bungarus multicinctus venom, and then the serum metabolites were subsequently screened, identified and validated by ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS) methods to explore the potential biomakers and possible metabolic pathways. Untargeted metabolomics analysis showed that 36 and 38 endogenous metabolites levels changed in ESI+ and ESI-, respectively, KEGG pathway analysis showed that 5 metabolic pathways, including mineral absorption, central carbon metabolism in cancer, protein digestion and absorption, aminoacyl-tRNA biosynthesis and ABC transporters might be closely related to Bungarus multicinctus bite. Targeted metabolomics analysis showed that there were significant differences in serum D-proline, L-leucine and L-glutamine after Bungarus multicinctus bite (P < 0.05). In addition, receiver operating characteristic (ROC) analysis showed that the diagnostic efficiency of L-Glutamine was superior to other potential biomarkers and the AUC value was 0.944. Moreover, we found evidence for differences in the pathophysiology of glutamine between Bungarus multicinctus bite group and normal group, specifically with the content of glutamine synthetase (GS) and glutaminase (GLS). Taken together, the current study has successfully established an animal model of Bungarus multicinctus bite, and further identified the links between the metabolic perturbations and the pathophysiology and the potential diagnostic biomakers of Bungarus multicinctus bite, which provided valuable insights for studying the mechanism of Bungarus multicinctus bite.

Effects of melatonin on the toxicity and proliferation of human anaplastic thyroid cancer cell line

BACKGROUND

Thyroid carcinoma is the most common endocrine malignancy and anaplastic thyroid carcinoma (ATC) is a rare but most aggressive cancer. Melatonin has enhanced or induced apoptosis in many different cancer cells, however, there has not been any study on the effects of melatonin in the treatment of ATC. In this study, we examined the effect of melatonin on cytotoxicity in the human ATC cell line.

MATERIALS AND METHODS

Cultured ATC cells were treated at melatonin concentrations 0.6, 1, 4, 16, 28 mM for 24 h. The MTT assay was performed to examine cell viability. Cytotoxicity was assayed with the determination of lactic dehydrogenase (LDH) activity. Apoptosis was detected by acridine orange/ethidium bromide and Hoechst 33342 staining. Giemsa staining is considered for evaluating the morphological changes of ATC cells. The reproductive ability of cells to form a colony was evaluated by the clonogenic assay.

RESULTS

Results showed that melatonin could significantly decrease cell viability and the lowest cell viability was observed at 28 mM, 10.26 % ± 0.858 versus control. Similar results were obtained when analyzing LDH activity. The highest LDH levels were observed at 16 and 28 mM (546.08 ± 4.66, 577.82 ± 3.14 munit/mL versus control) that confirmed the occurrence of late apoptosis. The clonogenic assay showed that cells at the high concentration of melatonin (16 and 28 mM) don't enable to form the colony that approved the occurrence of reproductive death.

CONCLUSION

Our results showed a dose-dependent cytotoxic effect of melatonin on ATC cells that significantly decreased cell viability and induced cell reproductive death at the concentration greater than 1 mM and findings suggested that MLT might be useful as an adjuvant in ATC therapy.

The Potential of Metabolomics in the Diagnosis of Thyroid Cancer

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. "Omics" approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics.

A comprehensive analysis of metabolomics and transcriptomics in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high efficiency of identifying key metabolic pathways and functional genes in lung cancer patients. In this study, we performed an untargeted metabolomics analysis of 142 NSCLC patients and 159 healthy controls; 35 identified metabolites were significantly different between NSCLC patients and healthy controls, of which 6 metabolites (hypoxanthine, inosine, L-tryptophan, indoleacrylic acid, acyl-carnitine C10:1, and lysoPC(18:2)) were chosen as combinational potential biomarkers for NSCLC. The area under the curve (AUC) value, sensitivity (SE), and specificity (SP) of these six biomarkers were 0.99, 0.98, and 0.99, respectively. Potential diagnostic implications of the metabolic characteristics in NSCLC was studied. The metabolomics results were further verified by transcriptomics analysis of 1027 NSCLC patients and 108 adjacent peritumoral tissues from TCGA database. This analysis identified 2202 genes with significantly different expressions in cancer cells compared to normal controls, which in turn defined pathways implicated in the metabolism of the compounds revealed by metabolomics analysis. We built a fully connected network of metabolites and genes, which shows a good correspondence between the transcriptome analysis and the metabolites selected for diagnosis. In conclusion, this work provides evidence that the metabolic biomarkers identified may be used for NSCLC diagnosis and screening. Comprehensive analysis of metabolomics and transcriptomics data offered a validated and comprehensive understanding of metabolism in NSCLC.

Multi-omics Signatures and Translational Potential to Improve Thyroid Cancer Patient Outcome

Recent advances in high-throughput molecular and multi-omics technologies have improved our understanding of the molecular changes associated with thyroid cancer initiation and progression. The translation into clinical use based on molecular profiling of thyroid tumors has allowed a significant improvement in patient risk stratification and in the identification of targeted therapies, and thereby better personalized disease management and outcome. This review compiles the following: (1) the major molecular alterations of the genome, epigenome, transcriptome, proteome, and metabolome found in all subtypes of thyroid cancer, thus demonstrating the complexity of these tumors and (2) the great translational potential of multi-omics studies to improve patient outcome.

Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation

Thyroid cancers (TCs) are the most frequent endocrine malignancy with an unpredictable fast-growing incidence, especially in females all over the world. Fine-needle aspiration biopsy (FNAB) analysis is an accurate diagnostic method for detecting thyroid nodules and classification of TC. Though simplicity, safety, and accuracy of FNAB, 15-30% of cases are indeterminate, and it is not possible to determine the exact cytology of the specimen. This demands the need for innovative methods capable to find crucial biomarkers with adequate sensitivity for diagnosis and prediction in TC researches. Cancer-based metabolomics is a vast emerging field focused on the detection of a large set of metabolites extracted from biofluids or tissues. Using analytical chemistry procedures allows for the potential recognition of cancer-based metabolites for the purposes of advancing the era of personalized medicine. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with separation techniques e.g., gas chromatography (GC) and liquid chromatography (LC) are the main approaches for metabolic studies in cancers. The immense metabolite profiling has provided a chance to discover novel biomarkers for early detection of thyroid cancer and reduce unnecessary aggressive surgery. In this review, we recapitulate the recent advances and developed methods of diverse metabolomics tools and metabolic phenotypes of thyroid cancer, following a brief discussion of recent challenges in the thyroid cancer diagnosis.

The antifungal potential of (Z)-ligustilide and the protective effect of eugenol demonstrated by a chemometric approach

Mankind is on the verge of a postantibiotic era. New concepts are needed in our battle to attenuate infectious diseases around the world and broad spectrum plant-inspired synergistic pharmaceutical preparations should find their place in the global fight against pathogenic microorganisms. To progress towards the discovery of potent antifungal agents against human pathologies, we embarked upon developing chemometric approach coupled with statistical design to unravel the origin of the anticandidal potential of a set of 66 essential oils (EOs). EOs were analyzed by GC-MS and tested against Candida albicans and C. parapsilosis (Minimal Inhibitory Concentration, MIC). An Orthogonal Partial Least Square (OPLS) analysis allowed us to identify six molecules presumably responsible for the anticandidal activity of the oils: (Z)-ligustilide, eugenol, eugenyl acetate, citral, thymol, and β-citronellol. These compounds were combined following a full factorial experimental design approach in order to optimize the anticandidal activity and selectivity index (SI = IC50(MRC5 cells)/MIC) through reconstituted mixtures. (Z)-Ligustilide and citral were the most active compounds, while (Z)-ligustilide and eugenol were the two main factors that most contributed to the increase of the SI. These two terpenes can, therefore, be used to construct bioinspired synergistic anticandidal mixtures.

Plasma metabolomic profiling of amino acids and polar lipids in Iranian obese adults

BACKGROUND

Obesity, widely recognized as a serious health concern, is characterized by profoundly altered metabolism. However, the intermediate metabolites involved in this change remain largely unknown.

OBJECTIVE

We conducted targeted metabolomics profiling to identify moieties associated with adult obesity.

METHODS

In this case-control study of Iranian adults, 200 obese patients were compared with 100 controls based on 104 metabolites profiled by a targeted metabolomic approach using liquid chromatography coupled to triple quadrupole mass spectrometry (LC-MS/MS). The analysis comprised acylcarnitines, diacyl-phosphatidylcholines (PCaa), acyl-alkyl-phosphatidylcholines (PCae), sphingomyelins (SM), lyso-phospholipids (LPC) and amino acids. We performed multivariable linear regression to identify metabolites associated with obesity, adjusting for age, sex, total energy intake, total physical activity, smoking, and alcohol consumption. The Bonferroni correction was used to adjust for multiple testing.

RESULTS

A pattern of 19 metabolites was significantly associated with obesity. Branched chain amino acids, alanine, glutamic acid, proline, tyrosine LPCa C16:1, PCaa C32:1, PCaa C32:2 and PCaa C38:3 were positively, while serine, asparagine, LPCa C18:1, LPCa C18:2, LPCe C18:0, PCae C34:3, PCae C38:4 and PCae C40:6 were negatively associated with obesity (all p < 0.00048).

CONCLUSIONS

A metabolomic profile containing 9 amino acids and 10 polar lipids may serve as a potential biomarker of adult obesity. Further studies are warranted to replicate these findings as well as investigate potential changes in this profile after weight reduction.

Oncometabolites as biomarkers in thyroid cancer: a systematic review

INTRODUCTION

Thyroid cancer (TC) is an important common endocrine malignancy, and its incidence has increased in the past decades. The current TC diagnosis and classification tools are fine-needle aspiration (FNA) and histological examination following thyroidectomy. The metabolite profile alterations of thyroid cells (oncometabolites) can be considered for current TC diagnosis and management protocols.

METHODS

This systematic review focuses on metabolite alterations within the plasma, FNA specimens, and tissue of malignant TC contrary to benign, goiter, or healthy TC samples. A systematic search of MEDLINE (PubMed), Scopus, Embase, and Web of Science databases was conducted, and the final 31 studies investigating metabolite biomarkers of TC were included.

RESULTS

A total of 15 targeted studies and 16 untargeted studies revealed several potential metabolite signatures of TC such as glucose, fructose, galactose, mannose, 2-keto-d-gluconic acid and rhamnose, malonic acid and inosine, cholesterol and arachidonic acid, glycosylation (immunoglobulin G [IgG] Fc-glycosylation), outer mitochondrial membrane 20 (TOMM20), monocarboxylate transporter 4 (MCT4), choline, choline derivatives, myo-/scyllo-inositol, lactate, fatty acids, several amino acids, cell membrane phospholipids, estrogen metabolites such as 16 alpha-OH E1/2-OH E1 and catechol estrogens (2-OH E1), and purine and pyrimidine metabolites, which were suggested as the TC oncometabolite.

CONCLUSION

Citrate was suggested as the first most significant biomarker and lactate as the second one. Further research is needed to confirm these biomarkers as the TC diagnostic oncometabolite.

Network Analyses of Integrated Differentially Expressed Genes in Papillary Thyroid Carcinoma to Identify Characteristic Genes

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC.

Serum-plasma matched metabolomics for comprehensive characterization of benign thyroid nodule and papillary thyroid carcinoma

Metabolomics offers a noninvasive methodology to identify metabolic markers for pathogenesis and diagnosis of diseases. This work aimed to characterize circulating metabolic signatures of benign thyroid nodule (BTN) and papillary thyroid carcinoma (PTC) via serum-plasma matched metabolomics. A cohort of 1,540 serum-plasma matched samples and 114 tissues were obtained from healthy volunteers, BTN and PTC patients enrolled from 6 independent centers. Untargeted metabolomics was determined by liquid chromatography-quadrupole time-of-flight mass spectrometric and multivariate statistical analyses. The use of serum-plasma matched samples afforded a broad-scope detection of 1,570 metabolic features. Metabolic phenotypes revealed significant pattern differences for healthy versus BTN and healthy versus PTC. Perturbed metabolic pathways related mainly to amino acid and lipid metabolism. It is worth noting that, BTN and PTC showed no significant differences but rather overlap in circulating metabolic signatures, and this observation was replicated in all study centers. For differential diagnosis of healthy versus thyroid nodules (BTN + PTC), a panel of 6 metabolic markers, namely myo-inositol, α-N-phenylacetyl-L-glutamine, proline betaine, L-glutamic acid, LysoPC(18:0) and LysoPC(18:1) provided area under the curve of 97.68% in the discovery phase and predictive accuracies of 84.78-98.18% in the 4 validation centers. Taken together, serum-plasma matched metabolomics showed significant differences in circulating metabolites for healthy versus nodules but not for BTN versus PTC. Our results highlight the true metabolic nature of thyroid nodules, and potentially decrease overtreatment that exposes patients to unnecessary risks.

Combinational Biomarkers for Atrial Fibrillation Derived from Atrial Appendage and Plasma Metabolomics Analysis

Atrial fibrillation (AF) is one of the most common types of arrhythmias and often leads to clinical complications. The objectives of this study were to offer insights into the metabolites of AF and to determine biomarkers for AF diagnosis or prediction. Sixty atrial appendage samples (AF group: 30; non-AF group: 30) and 163 plasma samples (AF group: 48; non-AF group: 115) from 49 AF patients and 116 non-AF patients were subjected to liquid chromatography positive ion electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) metabolomics analysis. Consequently, 24 metabolites in atrial appendage samples and 24 metabolites in plasma samples were found to reflect metabolic differences between AF and non-AF patients (variable importance in projection (VIP) ≥ 1, P ≤ 0.05). Five identical metabolites including creatinine, D-glutamic acid, choline, hypoxanthine, and niacinamide (VIP ≥ 1.5, P < 0.01, FDR < 0.05) in atrial appendage and plasma samples were considered prominent features of AF patients, and the D-glutamine and D-glutamate metabolic pathway was also identified as a feature of AF patients. Finally, in plasma samples, the combination of D-glutamic acid, creatinine, and choline had an AUC value of 0.927 (95% CI: 0.875-0.979, P < 0.001) and displayed 90.5% sensitivity and 83.3% specificity; this group of metabolites was thus defined as a combinational biomarker for the recognition of AF and non-AF patients.

Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis

To identify metabolic pathways that were perturbed in pancreatic cancer (PC), we investigated gene‐metabolite networks by integration of metabolomic and transcriptomic. In this research, we undertook the metabolomic study of 43 paired human PC samples, aiming to identify key metabolic alterations in PC. We also carried out in vitro experiments to validate that the key metabolite cytidine and its related gene ENTPD8 played an important role in PC cell proliferation. We screened out 13 metabolites differentially expressed in PC tissue (PCT) by liquid chromatography/mass spectrometry analysis on 34 metabolites, and the partial least square discrimination analysis results revealed that 9 metabolites among them were remarkably altered in PCT compared to adjacent noncancerous tissue (variable importance in projection >1, P < .05). Among the 9 metabolites, 7 might be potential biomarkers. The most significantly enriched metabolic pathway was pyrimidine metabolism. We analyzed 351 differentially expressed genes from The Cancer Genome Atlas and intersected them with Kyoto Encyclopedia of Genes and Genomes metabolic pathways. We found that ENTPD8 had a gene‐metabolite association with cytidine in the CTP dephosphorylation pathway. We verified by in vitro experiments that the CTP dephosphorylation pathway was changed in PCT compared with adjacent noncancerous tissue. ENTPD8 was downregulated in PCT, causing a reduction in cytidine formation and hence weakened CTP dephosphorylation in pyrimidine metabolism.

Omics for understanding the tolerant mechanism of Trichoderma asperellum TJ01 to organophosphorus pesticide dichlorvos

BACKGROUD

Though it is toxic to humans, dichlorvos is a widely used chemical pesticide and plays an important role in the control of plant pests. The application of a combination of the biocontrol agent Trichoderma with dichlorvos may reduce the need for chemical pesticides. Therefore, revealing the specific molecular mechanism of Trichoderma tolerance to dichlorvos has become particularly important.

RESULTS

In this study, using transcriptome and metabolome analyses, changes in primary and secondary metabolisms in Trichoderma asperellum TJ01 were comprehensively studied in the presence of dichlorvos. A novel C₂H₂ zinc finger protein gene, zinc finger chimera 1 (zfc1), was discovered to be upregulated, along with a large number of oxidoreductase genes and ABC transporter genes under dichlorvos stress. In addition, gas chromatography-mass spectrometry (GC-TOF-MS), and liquid chromatography-mass spectrometry (LC-QQQ-MS) data revealed the global primary and secondary metabolic changes that occur in T. asperellum TJ01 under dichlorvos stress.

CONCLUSIONS

The tolerance mechanism of T. asperellum TJ01 to dichlorvos was proposed. In addition, the absorption and residue of dichlorvos were analyzed, laying the foundation for elucidation of the mechanism by which T. asperellum TJ01 degrades pesticide residues.

Effects of Different Pollens on Primary Metabolism and Lignin Biosynthesis in Pear

To investigate the effect of pollination on the fruit quality of ‘Dangshan Su’ pear, ‘Dangshan Su’ was fertilized by the pollen of ‘Wonhwang’ (Pyrus pyrifolia Nakai.) (DW) and ‘Jingbaili’ (Pyrus ussuriensis Maxim.) (DJ). The analysis of primary metabolites was achieved through untargeted metabolomics, and the quantitative analysis of intermediate metabolites of lignin synthesis was undertaken using targeted metabolomics. The untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry (GC-MS). The targeted metabolomics analysis was performed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) under the multiple reaction monitoring (MRM) mode. The results showed that the metabolite content was significantly different between DW and DJ. Compared with that in DJ, the sugar and amino acid content in DW was higher and the fatty acid content was lower at 47 days after pollination (DAPs), and the sugar, amino acid, and fatty acid content in DW was lower at 63 DAPs. The intermediate metabolites of lignin synthesis were analyzed using the orthogonal partial least squares discriminant analysis (OPLS-DA) model, and the differential metabolites at 47 DAPs were p-coumaric acid, ferulic acid, sinapaldehyde, coniferyl alcohol, and sinapyl alcohol. The differential significant metabolite at 63 DAPs was p-coumaric acid. At 47 DAPs and 63 DAPs, the p-coumaric acid level was significantly different, and the p-coumaric acid content was positively correlated with lignin synthesis. The pollination pollen affects the quality of ‘Dangshan Su’ pear fruit through regulation of the sugar, amino acid, and fatty acid content; at the same time, regulating the levels of intermediate metabolites of lignin synthesis, especially the p-coumaric acid content, to affect lignin synthesis ultimately affects the stone cell content and improves the quality of the pears.

Application of metabolomics in prediction of lymph node metastasis in papillary thyroid carcinoma

PURPOSE

The aim of this study was to find useful metabolites to predict lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC) through a metabolomics approach and investigate the potential role of metabolites as a novel prognostic marker.

MATERIALS AND METHODS

Fifty-two consecutive patients (median age: 41.5 years, range 15-74 years) were enrolled who underwent total thyroidectomy and central LN dissection with or without lateral LN dissection in Severance Hospital between October 2013 and July 2015. The study specimens were provided by the Severance Hospital Gene Bank, and consisted of PTC from each patient. The specimens were prepared for proton nuclear magnetic resonance (1H-NMR) spectroscopy. Spectral data by 1H-NMR spectroscopy were acquired, processed, and analyzed. Patients were grouped in three ways, according to the presence of LN metastasis, central LN metastasis and lateral LN metastasis. Chi-square test and the student t-test were used to analyze categorical variables and continuous variables, respectively. The Mann-Whitney U test was used for univariate analysis of metabolites. Orthogonal projections to latent structure discriminant analysis (OPLS-DA) was used for multivariate analysis to discriminate metabolic differences between the two groups.

RESULTS

Among 52 patients, 32 had central LN metastasis and 19 had lateral LN metastasis. No clinical or histopathological characteristic was significantly different for all comparisons. On univariate analysis, no metabolite showed significant difference for all comparisons. On multivariate analysis, OPLS-DA did not discriminate the presence and absence of LN metastasis. Lactate was found to be the most promising metabolite.

CONCLUSIONS

No metabolite could discriminate the presence of LN metastasis. However, lactate was found to be the most promising metabolite for discrimination. Further studies with larger sample sizes are needed to elucidate significant metabolites which can indicate the presence of LN metastasis in patients with PTC.

Metabolic changes associated with papillary thyroid carcinoma: A nuclear magnetic resonance-based metabolomics study

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. Nuclear magnetic resonance (NMR)‑based metabolomic technique is the gold standard in metabolite structural elucidation, and can provide different coverage of information compared with other metabolomic techniques. Here, we firstly conducted NMR based metabolomics study regarding detailed metabolic changes especially metabolic pathway changes related to PTC pathogenesis. 1H NMR-based metabolomic technique was adopted in conju-nction with multivariate analysis to analyze matched tumor and normal thyroid tissues obtained from 16 patients. The results were further annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG), and Human Metabolome Database, and then were analyzed using modules of pathway analysis and enrichment analysis of MetaboAnalyst 3.0. Based on the analytical techniques, we established the models of principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS‑DA) which could discriminate PTC from normal thyroid tissue, and found 15 robust differentiated metabolites from two OPLS-DA models. We identified 8 KEGG pathways and 3 pathways of small molecular pathway database which were significantly related to PTC by using pathway analysis and enrichment analysis, respectively, through which we identified metabolisms related to PTC including branched chain amino acid metabolism (leucine and valine), other amino acid metabolism (glycine and taurine), glycolysis (lactate), tricarboxylic acid cycle (citrate), choline metabolism (choline, ethanolamine and glycerolphosphocholine) and lipid metabolism (very-low‑density lipoprotein and low-density lipoprotein). In conclusion, the PTC was characterized with increased glycolysis and inhibited tricarboxylic acid cycle, increased oncogenic amino acids as well as abnormal choline and lipid metabolism. The findings in this study provide new insights into detailed metabolic changes of PTC, and hold great potential in the treatment of PTC.

Metabolomics-A Promising Approach to Pituitary Adenomas

Background: Metabolomics-the novel science that evaluates the multitude of low-molecular-weight metabolites in a biological system, provides new data on pathogenic mechanisms of diseases, including endocrine tumors. Although development of metabolomic profiling in pituitary disorders is at an early stage, it seems to be a promising approach in the near future in identifying specific disease biomarkers and understanding cellular signaling networks. Objectives: To review the metabolomic profile and the contributions of metabolomics in pituitary adenomas (PA). Methods: A systematic review was conducted via PubMed, Web of Science Core Collection and Scopus databases, summarizing studies that have described metabolomic aspects of PA. Results: Liquid chromatography tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectrometry, which are traditional techniques employed in metabolomics, suggest amino acids metabolism appears to be primarily altered in PA. N-acetyl aspartate, choline-containing compounds and creatine appear as highly effective in differentiating PA from healthy tissue. Deoxycholic and 4-pyridoxic acids, 3-methyladipate, short chain fatty acids and glucose-6-phosphate unveil metabolite biomarkers in patients with Cushing's disease. Phosphoethanolamine, N-acetyl aspartate and myo-inositol are down regulated in prolactinoma, whereas aspartate, glutamate and glutamine are up regulated. Phosphoethanolamine, taurine, alanine, choline-containing compounds, homocysteine, and methionine were up regulated in unclassified PA across studies. Intraoperative use of ultra high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which allows localization and delineation between functional PA and healthy pituitary tissue, may contribute to achievement of complete tumor resection in addition to preservation of pituitary cell lines and vasopressin secretory cells, thus avoiding postoperative diabetes insipidus. Conclusion: Implementation of ultra high performance metabolomics analysis techniques in the study of PA will significantly improve diagnosis and, potentially, the therapeutic approach, by identifying highly specific disease biomarkers in addition to novel molecular pathogenic mechanisms. Ultra high mass resolution MALDI-MSI emerges as a helpful clinical tool in the neurosurgical treatment of pituitary tumors. Therefore, metabolomics appears to be a science with a promising prospect in the sphere of PA, and a starting point in pituitary care.

A distinct serum metabolic signature of distant metastatic papillary thyroid carcinoma

BACKGROUND

Although the incidence rate for thyroid cancer seems to have begun stabilizing in recent years, an increased rate of advanced stage of this disease has been reported. Additionally, distant metastasis is one of the most important prognostic factors of patients with papillary thyroid carcinoma (PTC). Unfortunately, the underlying mechanisms of distant metastasis, as well as cell status like metabolism changes in distant metastatic tumours have not been clearly elucidated.

OBJECTIVE

To identify serum metabolic signature of distant metastatic PTC.

DESIGN, PATIENTS AND MEASUREMENTS

In this study, gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) was used to analyse the serum from 77 patients diagnosed with PTC (37 in distant metastasis group and 40 in ablation group). Principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) scores plots were used to analyse the data.

RESULTS

Principal component analysis and OPLS-DA analyses demonstrated an evident trend of separation between 40 serum samples from the ablation group and 37 samples from distant metastasis group. A total of 31 metabolites were identified, which are related to amino acid, lipid, glucose, vitamin metabolism and diet/gut microbiota interaction. Pathway analysis showed "alanine, aspartate and glutamate metabolism" and "inositol phosphate metabolism" were the most relevant pathways.

CONCLUSION

Serum metabolomics profiling could significantly discriminate papillary thyroid cancer patients according to distant metastasis. Potential metabolic aberration in distant metastatic PTC could be involved in different biological behaviours of tumour cells including proliferation, invasion/migration and immune escape. Diet/gut microbiota-produced metabolites could play an important role in these effects. This work may provide new clues to find the underlying mechanisms regarding the distant metastasis of PTC as well as potential adjuvant therapy targets.

Mitochondrial Dysfunction and Altered Renal Metabolism in Dahl Salt-Sensitive Rats

BACKGROUND/AIMS

The kidney plays a critical role in the control of blood pressure and its elevation in salt-induced hypertension. Mitochondrial dysfunction, especially in energy metabolism, has been associated with hypertension. Here, we aimed to investigate mitochondrial function and metabolic features in renal mitochondria of Dahl salt-sensitive (SS) rats to gain further insight into the relationship between mitochondrial metabolism and predisposition to hypertension.

METHODS

In this study, SS rats fed low-salt (LS) or high-salt (HS) diets were used to investigate mitochondrial function and metabolism including mitochondrial enzyme activities, pyridine nucleotides, metabolites, and oxidative stress by biochemical analysis and gas chromatography-mass spectrometer (GC-MS).

RESULTS

Significantly lower activity levels of fumarase, isocitrate dehydrogenase and succinyl-CoA synthetase were observed in renal mitochondria of SS rats compared with SS.13BN control rats fed LS diets. Intra-mitochondrial pyridine nucleotide content and mitochondrial metabolism were adversely affected in SS rats. In accordance with this, reduced ATP production, Δψm, and superoxide dismutase (SOD) activity were also observed in mitochondria of the renal medulla and cortex of SS rats. Moreover, ATP production was further impaired and oxidative stress was increased, confirming that the mitochondria of SS rats fed HS diets were dysfunctional compared to those of rats fed LS diets.

CONCLUSIONS

Our data demonstrated that the renal mitochondria of SS rats exhibited complicated metabolic alteration and dysfunction in low-salt diets, and high-salt diets aggravated these dysfunctions. Thus, these results may be associated with renal dysfunction, which, in turn, would help in understanding the development of salt-sensitive hypertension.

Applications of Metabolomics in Cancer Studies

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.