Follicular adenomas exhibit a unique metabolic profile. ¹H NMR studies of thyroid lesions

Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

Plasma metabolites analysis of patients with papillary thyroid cancer: A preliminary untargeted 1H NMR-based metabolomics

Metabolomics plays a crucial role in identifying molecular biomarkers that can differentiate pathological conditions. In the case of thyroid cancer, it is essential to accurately diagnose malignancy from benignity to avoid unnecessary surgeries. The objective of this research was to apply untargeted NMR-based metabolomics in order to identify metabolic biomarkers that can distinguish between plasma samples of patients with papillary thyroid cancer (PTC) and multinodular goiter (MNG), as well as PTC and healthy individuals. The study included a cohort of 55 patients who were divided into three groups: PTC (n=20), MNG (n=16), and healthy (n=19). Plasma samples were collected from all participants and subjected to 1H NMR spectroscopy. Differential metabolites were identified using chemometric pattern recognition algorithms. The obtained metabolic profile had the potential to differentiate PTC from healthy plasma, but not from MNG. In patients diagnosed with PTC, a total of 18 compounds were discovered, revealing elevated levels of leucine, lysine, and 4-acetamidobutyric acid, while acetate, proline, acetoacetate, 3-hydroxybutyrate, glutamate, pyruvate, cystine, glutathione, asparagine, ethanolamine, histidine, tyrosine, myo-inositol, and glycerol along with a lipid compound were found to be lower in comparison to those of healthy individuals. According to the area under the curve (AUC) of the receiver operating characteristic curve, this particular profile exhibited an impressive capability of 85% to discern PTC from healthy subjects (AUC=0.853, sensitivity=78.95, specificity=84.21). The utilization of the 1H NMR-based metabolomics approach revealed considerable promise in the identification of PTC from healthy plasma specimens. The modifications noticed in the plasma metabolites have the potential to act as practical biomarkers that are non-invasive and could suggest transformations in the metabolic profile of thyroid tumors.

Metabolite signature of human malignant thyroid tissue: A systematic review and meta-analysis

BACKGROUND

Thyroid cancer (TC) is the predominant malignancy within the endocrine system. However, the standard method for TC diagnosis lacks the capability to identify the pathological condition of all thyroid lesions. The metabolomics approach has the potential to manage this problem by identifying differential metabolites.

AIMS

This study conducted a systematic review and meta-analysis of the NMR-based metabolomics studies in order to identify significant altered metabolites associated with TC.

METHODS

A systematic search of published literature in any language in three databases including Embase, PubMed, and Scopus was conducted. Out of 353 primary articles, 12 studies met the criteria for inclusion in the systematic review. Among these, five reports belonging to three articles were eligible for meta-analysis. The correlation coefficient of the orthogonal partial least squares discriminant analysis, a popular model in the multivariate statistical analysis of metabolomic data, was chosen for meta-analysis. The altered metabolites were chosen based on the fact that they had been found in at least three studies.

RESULTS

In total, 49 compounds were identified, 40 of which were metabolites. The increased metabolites in thyroid lesions compared normal samples included lactate, taurine, alanine, glutamic acid, glutamine, leucine, lysine, phenylalanine, serine, tyrosine, valine, choline, glycine, and isoleucine. Lipids were the decreased compounds in thyroid lesions. Lactate and alanine were increased in malignant versus benign thyroid lesions, while, myo-inositol, scyllo-inositol, citrate, choline, and phosphocholine were found to be decreased. The meta-analysis yielded significant results for three metabolites of lactate, alanine, and citrate in malignant versus benign specimens.

DISCUSSION

In this study, we provided a concise summary of 12 included metabolomic studies, making it easier for future researchers to compare their results with the prior findings.

CONCLUSION

It appears that the field of TC metabolomics will experience notable advancement, leading to the discovery of trustworthy diagnostic and prognostic biomarkers.

Distinguishing benign and malignant thyroid nodules using plasma trimethylamine N-oxide, carnitine, choline and betaine

PURPOSE

Trimethylamine N-oxide (TMAO), a gut microbiome-derived metabolite, and its precursors (carnitine, choline, betaine) have not been fully examined in relation to thyroid cancer (TC) risk. The aim of this study was to assess the value of TMAO and its precursors in diagnosis of benign and malignant thyroid nodules.

METHODS

In this study, high-performance liquid chromatography-tandem mass spectrometry was utilized to measure the levels of plasma TMAO and its precursors (choline, carnitine, and betaine) in 215 TC patients, 63 benign thyroid nodules (BTN) patients and 148 healthy controls (HC). The distribution of levels of TMAO and its precursors among the three groups were compared by the Kruskal-Wallis test. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the sensitivity, specificity, and the predictive accuracy of single and combined biomarkers.

RESULTS

In comparison to HC, TC showed higher levels of TMAO and lower levels of its precursors (carnitine, choline, and betaine) (all P < 0.001). Plasma choline (P < 0.01) and betaine (P < 0.05) were declined in BTN than HC. The levels of carnitine (P < 0.001) and choline (P < 0.05) were significantly higher in BTN than that in TC group. Plasma TMAO showed lower levels in TC with lymph node metastasis (101.5 (73.1-144.5) ng/ml) than those without lymph node metastasis (131 (84.8-201) ng/ml, P < 0.05). Combinations of these four metabolites achieved good performance in the differential diagnosis, with the area under the ROC curve of 0.703, 0.741, 0.793 when discriminating between TC and BTN, BTN and HC, TC and HC, respectively.

CONCLUSION

Plasma TMAO, along with its precursors could serve as new biomarkers for the diagnosis of benign and malignant thyroid nodules.

Alterations in the amino acid profile in patients with papillary thyroid carcinoma with and without Hashimoto's thyroiditis

Purpose

Amino acids (AAs) play important physiological roles in living cells. Some amino acid changes in blood are specific for autoimmune disorders, and some are specific for thyroid cancer. The aims of this study were to profile AA metabolites in the serum of patients with papillary thyroid carcinoma (PTC0) without Hashimoto's thyroiditis (HT) and patients with PTC with HT (PTC1) and predict whether AA metabolites are associated with thyroid disease, thyroid hormone and thyroid autoantibodies.

Methods

A total of 95 serum samples were collected, including 28 healthy controls (HCs), 28 PTC0 patients and 39 PTC1 patients. Serum samples were analyzed by high-performance liquid chromatography-triple stage quadrupole-mass spectrometry (HPLC-TSQ-MS), and twenty-one amino acids (AAs) were detected.

Results

The serum concentration of glutamic acid was significantly elevated in PTC1 patients compared with PTC0 patients. Lysine was the second amino acid that differentiated these two groups of PTC patients. In addition, the serum concentrations of glycine, alanine and tyrosine were significantly reduced in both PTC patient groups compared to the HC group. These AAs were also correlated with thyroid hormones and antibodies. Five amino acid markers, namely, glycine, tyrosine, glutamic acid, glutamine and arginine, separated/distinguished PTC0 patients from healthy subjects, and eight AA markers, the same AAs as above without arginine but with alanine, leucine, valine and histidine, separated/distinguished PTC1 patients from healthy subjects based on ROC analysis.

Conclusion

Compared with the HCs, changes in AAs in PTC0 and PTC1 patients showed similar patterns, suggesting the possibility of a common pathophysiological basis, which confirms preliminary research that PTC is significantly associated with pathologically confirmed HT. We found two AAs, lysine and alanine, that can perform diagnostic functions in distinguishing PTC1 from PTC0.

Pilot Study on the Use of Untargeted Metabolomic Fingerprinting of Liquid-Cytology Fluids as a Diagnostic Tool of Malignancy for Thyroid Nodules

Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20-30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich^®) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich^® FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842-1), an AUC of 0.945 (0.833-1), and an F1 score of 0.947 (0.842-1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures.

Reprogramming of Cellular Metabolism and Its Therapeutic Applications in Thyroid Cancer

Metabolism is a series of life-sustaining chemical reactions in organisms, providing energy required for cellular processes and building blocks for cellular constituents of proteins, lipids, carbohydrates and nucleic acids. Cancer cells frequently reprogram their metabolic behaviors to adapt their rapid proliferation and altered tumor microenvironments. Not only aerobic glycolysis (also termed the Warburg effect) but also altered mitochondrial metabolism, amino acid metabolism and lipid metabolism play important roles for cancer growth and aggressiveness. Thus, the mechanistic elucidation of these metabolic changes is invaluable for understanding the pathogenesis of cancers and developing novel metabolism-targeted therapies. In this review article, we first provide an overview of essential metabolic mechanisms, and then summarize the recent findings of metabolic reprogramming and the recent reports of metabolism-targeted therapies for thyroid cancer.

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.

ACLY inhibitors induce apoptosis and potentiate cytotoxic effects of sorafenib in thyroid cancer cells

PURPOSE

ATP-citrate lyase (ACLY) is a critical enzyme at the intersection of glucose and lipid metabolism. ACLY is often upregulated or activated in cancer cells to accelerate lipid synthesis and promote tumor progression. In this study, we aimed to explore the possibility of utilizing ACLY inhibition as a new strategy in the treatment of thyroid cancer.

METHODS

Bioinformatics analysis of the public datasets was performed. Thyroid cancer cells were treated with two different ACLY inhibitors, SB-204990 and NDI-091143.

RESULTS

Bioinformatics analysis revealed that ACLY expression was increased in anaplastic thyroid cancer. In thyroid cancer cell lines FTC-133 and 8505C, ACLY inhibitors suppressed monolayer cell growth and clonogenic ability in a dose-dependent and time-dependent manner. Flow cytometry analysis showed that ACLY inhibitors increased the proportion of sub-G1 cells in the cell cycle and the number of annexin V-positive cells. Immunoblotting confirmed caspase-3 activation and PARP1 cleavage following treatment with ACLY inhibitors. Compromised cell viability could be partially rescued by co-treatment with the pan-caspase inhibitor Z-VAD-FMK. Additionally, we showed that ACLY inhibitors impeded three-dimensional growth and cell invasion in thyroid cancer cells. Isobolograms and combination index analysis indicated that ACLY inhibitors synergistically potentiated the cytotoxicity rendered by sorafenib.

CONCLUSIONS

Targeting ACLY holds the potential for being a novel therapeutic strategy for thyroid cancer.

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.

Myoinositol in Autoimmune Thyroiditis

Myoinositol (Myo) is an isoform of inositol, a cyclic polyol with 6 hydroxyl groups. Myo is mainly derived from dietary intake while its endogenous production is generated from glucose by enzymatic reactions. Moreover, Myo is also synthesized de novo by catabolism of phosphatidylinositol (PI), phosphoinositides (PIP), and inositol phosphates (IP). Myo has a determinant role in thyroid function and autoimmune diseases as it regulates iodine organification and thyroid hormone biosynthesis by the formation of hydrogen peroxide (H₂O₂) in thyrocytes. Depletion of Myo that is involved in the thyroid stimulating hormone (TSH) signaling pathway, may cause the development of thyroid diseases such as hypothyroidism. TSH levels significantly decreased in patients with subclinical hypothyroidism, with or without autoimmune thyroiditis, after treatment with Myo plus Selenium (Myo+Se). In addition to TSH, antithyroid autoantibodies are reduced. This review summarizes the role of Myo in the thyroidal physiology and its role in the management of some thyroid diseases.

Redox metabolism correlates with cellular turnover and clinical phenotype of papillary thyroid carcinoma and colloid goiter

INTRODUCTION

Papillary thyroid carcinoma (PTC) and colloid goiter (CG) represent the most common thyroid malignant and benign diseases, respectively. Oxidative stress is considered to have an important role in the pathogenesis of both diseases, but without sufficient and comprehensive data. The aim was to evaluate the redox profile, its influence on cell survival of PTC, comparing it with CG as a control and its relation with demographic, pathological and clinical parameters.

MATERIAL AND METHODS

We evaluated for the first time the PTC and CG tissue profile of advanced oxidation protein products (AOPP) and total thiols as parameters of redox metabolism and deoxyribonuclease I (DNase I) and deoxyribonuclease II (DNase II) activity as biomarkers of cell turnover and apoptosis. Tissue levels of biochemical parameters were quantified in PTC and CG tissue using spectrophotometric methods. Study parameters were evaluated in light of different demographic, clinical and pathological features of PTC and CG.

RESULTS

Papillary thyroid carcinoma tissue is characterized by increased antioxidant activity and a normal prooxidation level. Biochemical parameters show numerous correlations with demographic and clinical characteristics of PTC and CG patients. DNase I and II activities are dependent upon the AOPP concentration in PTC tissue. The size of CG can be predicted with combined use of AOPP, DNase I and DNase II. AOPP is the most powerful predictor of PTC capsular invasion, multicentric intrathyroid dissemination and lymph node metastasis phenotype.

CONCLUSIONS

Evaluated parameters can be used for assessment of tumor redox and survival status and the clinical course of PTC and CG.

A glance at the actual role of glutamine metabolism in thyroid tumorigenesis

Thyroid cancers (TCs) are the most prevalent malignancy of the endocrine system and the seventh most common cancer in women. According to estimates from the Global Cancer Observatory (GCO) in 2020, the incidence of thyroid cancer globally was 586,000 cases. As thyroid cancer incidences have dramatically increased, identifying the most important metabolic pathways and biochemical markers involved in thyroid tumorigenesis can be critical strategies for controlling the prevalence and ultimately treatment of this disease. Cancer cells undergo cellular metabolism and energy alteration in order to promote cell proliferation and invasion. Glutamine is one of the most abundant free amino acids in the human body that contributes to cancer metabolic remodeling as a carbon and nitrogen source to sustain cell growth and proliferation. In the present review, glutamine metabolism and its regulation in cancer cells are highlighted. Thereafter, emphasis is given to the perturbation of glutamine metabolism in thyroid cancer, focusing on metabolomics studies.

The Role of Inositol in Thyroid Physiology and in Subclinical Hypothyroidism Management

Myo-Inositol (MYO) is the most abundant stereoisomer of inositols' family, cyclic polyols with 6 hydroxyl groups. Myo-Inositol has a relevant role in thyroid function and autoimmune diseases, as a precursor of phosphoinositides that takes part in the phosphatidylinositol (PI) signal transduction pathway. Among phosphoinositides, phosphatidylinositol 4,5- bisphosphate (PIP2) is the precursor of inositol triphosphates (IP3), second messenger of several hormones including thyroid-stimulating hormone (TSH). As a second messenger in the phospholipase C (PLC)-dependent inositol phosphate Ca2+/DAG pathway, Myo-Inositol is essential to produce H2O2 required for the synthesis of thyroid hormones. Consequently, depletion of Myo-Inositol or impaired inositol dependent TSH signaling pathway may predispose to the development of some thyroid diseases, such as hypothyroidism. Many clinical studies have shown that after treatment with Myo-Inositol plus Selenium (MYO+Se), TSH levels significantly decreased in patients with subclinical hypothyroidism with or without autoimmune thyroiditis. The TSH reduction was accompanied by a decline of antithyroid autoantibodies. Moreover, Myo-Inositol supplementation seemed to be involved also in the management of thyroidal benign nodules, with a possible effect in the size reduction. This review proposes a summary of the role of inositol, especially of Myo-Inositol, in the thyroidal physiology and its contribution on the management of some thyroid diseases.

Shared and unique metabolic features of the malignant and benign thyroid lesions determined with use of 1H HR MAS NMR spectroscopy

The purpose of this work was to investigate the distinct and common metabolic features of the malignant and benign thyroid lesions in reference to the non-transformed tissue from the contralateral gland (chronic thyroiditis and colloid goiter). 1H HR MAS NMR spectra of 38 malignant lesions, 32 benign lesions and 112 samples from the non-tumoral tissue (32 from chronic thyroiditis and 80 samples from colloid goiter) were subjected both to multivariate and univariate analysis. The increased succinate, glutamine, glutathione, serine/cysteine, ascorbate, lactate, taurine, threonine, glycine, phosphocholine/glycerophosphocholine and decreased lipids were found in both lesion types in comparison to either colloid goiter or chronic thyroiditis. The elevated glutamate and choline, and reduced citrate and glucose were additionally evident in these lesions in reference to goiter, while the increased myo-inositol-in comparison to thyroiditis. The malignant lesions were characterized by the higher alanine and lysine levels than colloid goiter and thyroiditis, while scyllo-inositol was uniquely increased in the benign lesions (not in cancer) in comparison to both non-tumoral tissue types. Moreover, the benign lesions presented with the unique increase of choline in reference to thyroiditis (not observed in the cancerous tissue). The metabolic heterogeneity of the non-tumoral tissue should be considered in the analysis of metabolic reprogramming in the thyroid lesions.

Redox Status and Antioxidative Cofactor Metals Influence Clinical and Pathological Characteristics of Papillary Thyroid Carcinoma and Colloid Goiter

Papillary thyroid carcinoma (PTC) is the endocrine neoplasm that occurs the most often worldwide, and its molecular pathophysiology is still not well characterized. Redox status is recognized as an important factor of carcinogenesis, but its influence on the PTC's clinical course needs to be better elucidated. The aim of this research was to determine the tissue redox status of 65 PTC and 45 colloid goiter (CG) patients together with antioxidative cofactor metal profiling. The malondialdehyde (MDA) concentration was used to access the prooxidation level, while antioxidant mechanisms were estimated by assaying the activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and glutathione reductase (GR). The antioxidative cofactor metals included quantification of Se, Cu, Zn, and Mn concentration. PTC tissues had normal prooxidation levels and increased GPx and GR activity. The activity of SOD has been significantly reduced in multicentric PTC dissemination and increased in smokers. SOD activity was directly dependent on MDA levels in CG tissues. CG patients with retrosternal goiter had reduced MDA concentration and SOD activity. Numerous correlations between redox parameters in PTC tissues reveal good co-activation of antioxidative mechanisms and cooperative response on prooxidation. PTC tissues had decreased Se levels and increased concentration of Cu and Mn in comparison to other tissues. MDA concentration and SOD activity were significant predictors of PTC's multicentric dissemination and for the existence of lymph node metastases, respectively. Particularly, the concentration of Cu predicted the retrosternal localization in CG patients. Significant findings presented in this study provide a possibility for development of novel prognostic molecular biomarkers of PTC and CG.

Diagnosis of papillary thyroid carcinoma by 1H NMR spectroscopy-based metabolomic analysis of whole blood

The incidence rate of thyroid carcinoma, especially papillary thyroid carcinoma (PTC), has increased significantly over time. As a primary pathway for metastasis, the lymphatic system is an important prognostic factor for PTC patients. Although the metabolic changes in PTC patients have been investigated in extensive studies, few studies focused on the whole blood metabolic profiling of PTC patients. In this study, we investigated the ¹H NMR-based metabolic profiles of whole blood samples that were obtained from healthy individuals and PTC patients, with or without lymph node metastasis. The estimation of the predictive potential of metabolites was evaluated using multivariate statistical analyses, which revealed that the whole blood carries information that is sufficient for distinguishing between PTC patients and healthy individuals. However, PTC patients were not well classified as positive or negative according to the lymph nodes. We did not find a metabolite that could discriminate the presence of lymph node metastasis. Further studies with larger sample sizes are needed to elucidate significant metabolites to indicate the presence of lymph node metastasis in patients with PTC.

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.

Plasma Metabolic Profiling of Human Thyroid Nodules by Gas Chromatography-Mass Spectrometry (GC-MS)-Based Untargeted Metabolomics

One of the challenges in the area of diagnostics of human thyroid cancer is a preoperative diagnosis of thyroid nodules with indeterminate cytology. Herein, we report an untargeted metabolomics analysis to identify circulating thyroid nodule metabolic signatures, to find new novel metabolic biomarkers. Untargeted gas chromatography-quadrupole-mass spectrometry was used to ascertain the specific plasma metabolic changes of thyroid nodule patients, which consisted of papillary thyroid carcinoma (PTC; n = 19), and multinodular goiter (MNG; n = 16), as compared to healthy subjects (n = 20). Diagnostic models were constructed using multivariate analyses such as principal component analysis, orthogonal partial least squares-discriminant analysis, and univariate analysis including One-way ANOVA and volcano plot by MetaboAnalyst and SIMCA software. Because of the multiple-testing issue, false discovery rate p-values were also computed for these functions. A total of 60 structurally annotated metabolites were subjected to statistical analysis. A combination of univariate and multivariate statistical analyses revealed a panel of metabolites responsible for the discrimination between thyroid nodules and healthy subjects, with variable importance in the projection (VIP) value greater than 0.8 and p-value less than 0.05. Significantly altered metabolites between thyroid nodules versus healthy persons are those associated with amino acids metabolism, the tricarboxylic acid cycle, fatty acids, and purine and pyrimidine metabolism, including cysteine, cystine, glutamic acid, α-ketoglutarate, 3-hydroxybutyric acid, adenosine-5-monophosphate, and uracil, respectively. Further, sucrose metabolism differed profoundly between thyroid nodule patients and healthy subjects. Moreover, according to the receiver operating characteristic (ROC) curve analysis, sucrose could discriminate PTC from MNG (area under ROC curve value = 0.92). This study enhanced our understanding of the distinct metabolic pathways associated with thyroid nodules, which enabled us to distinguish between patients and healthy subjects. In addition, our study showed extensive sucrose metabolism in the plasma of thyroid nodule patients, which provides a new metabolic signature of the thyroid nodule’s tumorigenesis. Accordingly, it suggests that sucrose can be considered as a circulating biomarker for differential diagnosis between malignancy and benignity in indeterminate thyroid nodules.

Metabolomic Reprogramming Detected by 1H-NMR Spectroscopy in Human Thyroid Cancer Tissues

Thyroid cancer cells demonstrate an increase in oxidative stress and decreased antioxidant action, but the effects of this increased oxidative stress on cell function remain unknown. We aimed to identify changes in the metabolism of thyroid cancer cells caused by oxidative stress, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Samples of thyroid cancer and healthy thyroid tissue were collected from patients undergoing thyroidectomy and analyzed with ¹H-NMR spectroscopy for a wide array of metabolites. We found a significant increase in lactate content in thyroid cancer tissue compared to healthy tissue. Metabolomic analysis demonstrated significant differences between cancer tissue and healthy tissue, including an increase in aromatic amino acids, and an average decrease in citrate in thyroid cancer tissue. We hypothesize that these changes in metabolism may be due to an oxidative stress-related decrease in activity of the Krebs cycle, and a shift towards glycolysis in cancer tissue. Thus, thyroid cancer cells are able to reprogram their metabolic activity to survive in conditions of high oxidative stress and with a compromised antioxidant system. Our findings, for the first time, suggested a connection between oxidative stress and the alteration of the metabolic profile in thyroid tumors.

Combined quantitation of HMGA2 mRNA, microRNAs, and mitochondrial-DNA content enables the identification and typing of thyroid tumors in fine-needle aspiration smears

Background

Analysis of molecular markers in addition to cytological analysis of fine-needle aspiration (FNA) samples is a promising way to improve the preoperative diagnosis of thyroid nodules. Nonetheless, in clinical practice, applications of existing diagnostic solutions based on the detection of somatic mutations or analysis of gene expression are limited by their high cost and difficulties with clinical interpretation.

The aim of our work was to develop an algorithm for the differential diagnosis of thyroid nodules on the basis of a small set of molecular markers analyzed by real-time PCR.

Methods

A total of 494 preoperative FNA samples of thyroid goiters and tumors from 232 patients with known histological reports were analyzed: goiter, 105 samples (50 patients); follicular adenoma, 101 (48); follicular carcinoma, 43 (28); Hürthle cell carcinoma, 25 (11); papillary carcinoma, 121 (56); follicular variant of papillary carcinoma, 80 (32); and medullary carcinoma, 19 (12). Total nucleic acids extracted from dried FNA smears were analyzed for five somatic point mutations and two translocations typical of thyroid tumors as well as for relative concentrations of HMGA2 mRNA and 13 microRNAs and the ratio of mitochondrial to nuclear DNA by real-time PCR. A decision tree–based algorithm was built to discriminate benign and malignant tumors and to type the thyroid cancer. Leave-p-out cross-validation with five partitions was performed to estimate prediction quality. A comparison of two independent samples by quantitative traits was carried out via the Mann–Whitney U test.

Results

A minimum set of markers was selected (levels of HMGA2 mRNA and miR-375, − 221, and -146b in combination with the mitochondrial-to-nuclear DNA ratio) and yielded highly accurate discrimination (sensitivity = 0.97; positive predictive value = 0.98) between goiters with benign tumors and malignant tumors and accurate typing of papillary, medullary, and Hürthle cell carcinomas. The results support an alternative classification of follicular tumors, which differs from the histological one.

Conclusions

The study shows the feasibility of the preoperative differential diagnosis of thyroid nodules using a panel of several molecular markers by a simple PCR-based method. Combining markers of different types increases the accuracy of classification.

1H-NMR-based metabolomics to investigate the effects of Phoenix dactylifera seed extracts in LPS-IFN-γ-induced RAW 264.7 cells

Inflammation has been revealed to play a central role in the onset and progression of many illnesses. Nuclear magnetic resonance (NMR) based metabolomics method was adopted to evaluate the effects of Phoenix dactylifera seeds, in particular the Algerian date variety of Deglet on the metabolome of the LPS-IFN-γ-induced RAW 264.7 cells. Variations in the extracellular and intracellular profiles emphasized the differences in the presence of tyrosine, phenylalanine, alanine, proline, asparagine, isocitrate, inosine and lysine. Principal component analysis (PCA) revealed noticeable clustering patterns between the treated and induced RAW cells based on the metabolic profile of the extracellular metabolites. However, the effects of treatment on the intracellular metabolites appears to be less distinct as suggested by the PCA and heatmap analyses. A clear group segregation was observed for the intracellular metabolites from the treated and induced cells based on the orthogonal partial least squares-discriminant analysis (OPLS-DA) score plot. Likewise, 11 of the metabolites in the treated cells were significantly different from those in the induced groups, including amino acids and succinate. The enrichment analysis demonstrated that treatment with Deglet seed extracts interfered with the energy and of amino acids metabolism. Overall, the obtained data reinforced the possible application of Deglet seeds as a functional food with anti-inflammatory properties.

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.

Metabolomic studies as a tool for determining the post-mortem interval (PMI) in stillborn calves

Background

Perinatal mortality may vary between herds, but the cost of deaths are always higher than value of the calf. When diagnosing the cause of a calf’s death it is important to determine when it occurred, before or after calving. Metabolomics is widely used to identify many human diseases, but quite rarely applied in veterinary science. The aim of this study was to compare the metabolic profiles of calves with different times of death and those of calves born alive. Into the study, twenty one healthy controls (singleton, normal assisted calving, born alive) and 75 stillborn (SB) calves (with a gestation length of ≥260 days, SB, or dead within 6 h of birth) were enrolled. Plasma and urine from SB and control calves were investigated by proton nuclear magnetic resonance based metabolomic methods. SB calves were divided into four PMI groups. One PMI group included calves that died after calving and the other groups - three comprised in utero deaths, based on pathophysiological changes (lung inflation, autolysis in internal organs, hemoglobin imbibition in the pleura and aortic arch). Partial Least Squares - Discriminant Analysis models based on plasma metabolites were calculated, reflecting assumed data clustering.

Results

Twenty six metabolites in plasma and 29 in urine changed significantly with PMI according to one way analysis of variance. Half the metabolites in plasma and the majority in urine increased with PMI. Six metabolites increased simultaneously in plasma and urine: acetate, sn-glycero-3-phosphocholine (GPC), leucine, valine, creatine, and alanine.

Conclusions

Post-mortem changes in calves were associated with molecular variations in blood plasma and urine, showing the greatest differences for the group in which the post-mortem pathological changes were the most advanced. The results of the study show that evaluation of calf plasma or urine may be used as a diagnostic method for the determination of the PMI. Moreover, the metabolites, which unambiguously increased or decreased, can be used as potential biomarkers of PMI.

Electronic supplementary material

The online version of this article (10.1186/s12917-019-1935-4) contains supplementary material, which is available to authorized users.

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.

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow-up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.

Diagnosis of post-surgical fine-needle aspiration biopsies of thyroid lesions with indeterminate cytology using HRMAS NMR-based metabolomics

INTRODUCTION

Ultrasound examination coupled with fine-needle aspiration (FNA) cytology is the gold standard for the diagnosis of thyroid cancer. However, about 10-40% of these analyses cannot be conclusive on the malignancy of the lesions and lead to surgery. The cytological indeterminate FNA biopsies are mainly constituted of follicular-patterned lesions, which are benign in 80% of the cases.

OBJECTIVES

The development of a FNAB classification approach based on the metabolic phenotype of the lesions, complementary to cytology and other molecular tests in order to limit the number of patients undergoing unnecessary thyroidectomy.

METHODS

We explored the potential of a NMR-based metabolomics approach to improve the quality of the diagnosis from FNABs, using thyroid tissues collected post-surgically.

RESULTS

The NMR-detected metabolites were used to produce a robust OPLSDA model to discriminate between benign and malignant tumours. Malignancy was correlated with amino acids such as tyrosine, serine, alanine, leucine and phenylalanine and anti-correlated with myo-inositol, scyllo-inositol and citrate. Diagnosis accuracy was of 84.8% when only indeterminate lesions were considered.

CONCLUSION

These results on model FNAB indicate that there is a clear interest in exploring the possibility to export NMR metabolomics to pre-surgical diagnostics.

Proteome profiles of different types of thyroid cancers

Proteomics profiling of tissue specimens representative for major types of thyroid cancers: papillary (classical and follicular variant), follicular, anaplastic and medullary, as well as benign follicular adenoma, was performed using shotgun LC-MS/MS approaches. A combination of Orbitrap and MALDI-TOF approach allowed to identify protein products of 3700 unique genes and revealed large differences between medullary, anaplastic and epithelium-derived differentiated cancers (papillary and follicular). Proteins characteristic for medullary and anaplastic cancers included factors associated with neuroendocrine functions and factors typically associated with advanced malignancies, respectively. Proteomes of different types of epithelium-derived differentiated cancers and follicular adenoma were compared using multi-enzyme LC-MS/MS approach, which revealed products of 4800 unique genes. A comparable overall similarity of follicular cancers to both variants of papillary cancers was found. Moreover, follicular adenoma showed higher overall similarity to follicular cancer than to either variant of papillary cancer. Proteins discriminating differentiated thyroid neoplasms included factors associated with lipid and hormone metabolism, regulation of gene expression and maintenance of DNA structure. Importantly, proteome data matched several features of transcriptome and metabolome profiles of thyroid cancers contributing to systems biology of this malignancy.

Serum NMR metabolomics to differentiate haematologic malignancies

Haematological malignancies are a frequently diagnosed group of neoplasms and a significant cause of cancer deaths. The successful treatment of these diseases relies on early and accurate detection. Specific small molecular compounds released by malignant cells and the simultaneous response by the organism towards the pathological state may serve as diagnostic/prognostic biomarkers or as a tool with relevance for cancer therapy management. To identify the most important metabolites required for differentiation, an ¹H NMR metabolomics approach was applied to selected haematological malignancies. This study utilized 116 methanol serum extract samples from AML (n= 38), nHL (n= 26), CLL (n= 21) and HC (n= 31). Multivariate and univariate data analyses were performed to identify the most abundant changes among the studied groups. Complex and detailed VIP-PLS-DA models were calculated to highlight possible changes in terms of biochemical pathways and discrimination ability. Chemometric model prediction properties were validated by receiver operating characteristic (ROC) curves and statistical analysis. Two sets of eight important metabolites in HC/AML/CLL/nHL comparisons and five in AML/CLL/nHL comparisons were selected to form complex models to represent the most significant changes that occurred.

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.

Non-invasive diagnosis of papillary thyroid microcarcinoma: a NMR-based metabolomics approach

Papillary thyroid microcarcinoma (PTMC) is a subtype of papillary thyroid carcinoma (PTC). Because its diameter is less than 10 mm, diagnosing it accurately is difficult with traditional methods such as image examinations and FNA (Fine Needle Aspiration). Investigating the metabolic changes induced by PTMC may enhance the understanding of its pathogenesis and provide important information for a new diagnosis method and treatment plan. In this study, high resolution magic angle spin (HRMAS) spectroscopy and ¹H-nuclear magnetic resonance (¹H-NMR) spectroscopy were used to screen metabolic changes in thyroid tissues and plasma from PTMC patients respectively. The results revealed reduced levels of fatty acids and elevated levels of several amino acids (phenylalanine, tyrosine, lactate, serine, cystine, lysine, glutamine/glutamate, taurine, leucine, alanine, isoleucine and valine) in thyroid tissues, as well as reduced levels of amino acids such as valine, tyrosine, proline, lysine, leucine and elevated levels of glucose, mannose, pyruvate and 3-hydroxybutyrate in plasma, are involved in the metabolic alterations in PTMC. In addition, a receiver operating characteristic (ROC) curve model for PTMC prediction was able to classify cases with good sensitivity and specificity using 9 significant changed metabolites in plasma. This work illustrates that the NMR-based metabolomics approach is capable of providing more sensitive diagnostic results and more systematic therapeutic information for PTMC.

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.

Metabolic Alterations of Thyroid Cancer as Potential Therapeutic Targets

Thyroid cancer (TC) is the most frequent endocrine tumor with a growing incidence worldwide. Besides the improvement of diagnosis, TC increasing incidence is probably due to environmental factors and lifestyle modifications. The actual diagnostic criteria for TC classification are based on fine needle biopsy (FNAB) and histological examination following thyroidectomy. Since in some cases it is not possible to make a proper diagnosis, classical approach needs to be supported by additional biomarkers. Recently, new emphasis has been given to the altered cellular metabolism of proliferating cancer cells which require high amount of glucose for energy production and macromolecules biosynthesis. Also TC displays alteration of energy metabolism orchestrated by oncogenes activation and tumor suppressors inactivation leading to abnormal proliferation. Furthermore, TC shows significant metabolic heterogeneity within the tumor microenvironment and metabolic coupling between cancer and stromal cells. In this review we focus on the current knowledge of metabolic alterations of TC and speculate that targeting TC metabolism may improve current therapeutic protocols for poorly differentiated TC. Future studies will further deepen the actual understandings of the metabolic phenotype of TC cells and will give the chance to provide novel prognostic biomarkers and therapeutic targets in tumors with a more aggressive behavior.

Nutritional and Acquired Deficiencies in Inositol Bioavailability. Correlations with Metabolic Disorders

Communities eating a western-like diet, rich in fat, sugar and significantly deprived of fibers, share a relevant increased risk of both metabolic and cancerous diseases. Even more remarkable is that a low-fiber diet lacks some key components-as phytates and inositols-for which a mechanistic link has been clearly established in the pathogenesis of both cancer and metabolic illness. Reduced bioavailability of inositol in living organisms could arise from reduced food supply or from metabolism deregulation. Inositol deregulation has been found in a number of conditions mechanistically and epidemiologically associated to high-glucose diets or altered glucose metabolism. Indeed, high glucose levels hinder inositol availability by increasing its degradation and by inhibiting both myo-Ins biosynthesis and absorption. These underappreciated mechanisms may likely account for acquired, metabolic deficiency in inositol bioavailability.

Serum and urine 1H NMR-based metabolomics in the diagnosis of selected thyroid diseases

Early detection of nodular thyroid diseases including thyroid cancer is still primarily based on invasive procedures such as fine-needle aspiration biopsy. Therefore, there is a strong need for development of new diagnostic methods that could provide clinically useful information regarding thyroid nodular lesions in a non-invasive way. In this study we investigated ¹H NMR based metabolic profiles of paired urine and blood serum samples, that were obtained from healthy individuals and patients with nodular thyroid diseases. Estimation of predictive potential of metabolites was evaluated using chemometric methods and revealed that both urine and serum carry information sufficient to distinguish between patients with nodular lesions and healthy individuals. Data fusion allowed to further improve prediction quality of the models. However, stratification of tumor types and their differentiation in relation to each other was not possible.

1H NMR Metabolomics Reveals Association of High Expression of Inositol 1, 4, 5 Trisphosphate Receptor and Metabolites in Breast Cancer Patients

¹H NMR is used to detect alterations in metabolites and their linkage to metabolic processes in a number of pathological conditions including breast cancer. Inositol 1, 4, 5 trisphosphate (IP₃R) receptor is an intracellular calcium channel known to regulate metabolism and cellular bioenergetics. Its expression is up regulated in a number of cancers. However, its linkage to metabolism in disease conditions has not been evaluated. This study was designed to determine the association if any, of these metabolites with altered expression of IP3R in breast cancer. We used ¹H NMR to identify metabolites in the serum of breast cancer patients (n = 27) and performed Real-time Polymerase Chain Reaction analysis for quantifying the expression of IP₃R type 3 and type 2 in tissues from breast cancer patients (n = 40). Principal Component Analysis (PCA) and Partial Least Square-Discriminant Analysis (PLS-DA) clearly distinguished patients with high/low IP₃R expression from healthy subjects. The present study revealed high expression of IP₃R type 2 and type 3 in human breast tumor tissue compared to adjacent non-tumorous tissue. Moreover, patients with ≥ 2-fold increase in IP₃R (high IP₃R group) had significantly higher concentration of metabolic intermediates compared to those with < 2-fold increase in IP₃R (low IP₃R group). We observed an increase in lipoprotein content and the levels of metabolites like lactate, lysine and alanine and a decrease in the levels of pyruvate and glucose in serum of high IP₃R group patients when compared to those in healthy subjects. Receiver operating characteristic (ROC) curve analysis was performed to show the clinical utility of metabolites. In addition to the human studies, functional relevance of IP₃Rs in causing metabolic disruption was observed in MCF-7 and MDA MB-231 cells. Results from our studies bring forth the importance of metabolic (or metabolomics) profiling of serum by ¹H NMR in conjunction with tissue expression studies for characterizing breast cancer patients. The results from this study provide new insights into relationship of breast cancer metabolites with IP₃R.

Metabolomic analysis of percutaneous fine-needle aspiration specimens of thyroid nodules: Potential application for the preoperative diagnosis of thyroid cancer

Thyroid nodules are a very common problem. Since malignant thyroid nodules should be treated surgically, preoperative diagnosis of thyroid cancer is very crucial. Cytopathologic analysis of percutaneous fine-needle aspiration (FNA) specimens is the current gold standard for diagnosing thyroid nodules. However, this method has led to high rates of inconclusive results. Metabolomics has emerged as a useful tool in medical fields and shown great potential in diagnosing various cancers. Here, we evaluated the potential of nuclear magnetic resonance (NMR) analysis of percutaneous FNA specimens for preoperative diagnosis of thyroid cancer. We analyzed metabolome of FNA samples of papillary thyroid carcinoma (n = 35) and benign follicular nodule (n = 69) using a proton NMR spectrometer. The metabolomic profiles showed a considerable discrimination between benign and malignant nodules. Receiver operating characteristic (ROC) curve analysis indicated that seven metabolites could serve as discriminators (area under ROC curve value, 0.64–0.85). These findings demonstrated that NMR analysis of percutaneous FNA specimens of thyroid nodules can be potentially useful in the accurate and rapid preoperative diagnosis of thyroid cancer.

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

Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. Our study was to construct a tissue-targeted metabolomics analysis method based on untargeted and targeted metabolic multi-platforms to identify a comprehensive PTC metabolic network in clinical samples. We applied untargeted gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) for preliminary screening of potential biomarkers. With diagnostic models constructed using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA), 45 differentially abundant metabolites with a variable importance in the projection (VIP) value greater than 1 and a P value less than 0.05 were identified, and we show that our approach was able to discriminate PTC tissues from healthy tissues. We then performed validation experiments based on targeted GC-TOF-MS combined with ultra-high-performance liquid chromatography-triple-quadrupole mass spectrometry (UHPLC-QqQ-MS) through constructing linear standard curves of analytes. Ultimately, galactinol, melibiose, and melatonin were validated as significantly altered metabolites (p < 0.05). These three metabolites were defined as a combinatorial biomarker to assist needle biopsy for PTC diagnosis as demonstrated by receiver operating characteristic (ROC) curve analysis, which revealed an area under the ROC curve (AUC) value of 0.96. Based on the metabolite enrichment analysis results, the galactose metabolism pathway was regarded as an important factor influencing PTC development by affecting energy metabolism. Alpha-galactosidase (GLA) was considered to be a potential target for PTC therapy.

Detection of metabolites discriminating subtypes of thyroid cancer: Molecular profiling of FFPE samples using the GC/MS approach

One of the critical issues in thyroid cancer diagnostic is differentiation between follicular adenoma, follicular carcinoma and the follicular variant of papillary carcinoma, which in some cases is not possible based on histopathological features only. In this paper we performed molecular profiling of thyroid tissue aiming to identify metabolites characteristic for different types of thyroid cancer. FFPE tissue specimens were analysed from 5 different types of thyroid malignancies (follicular, papillary/classical variant, papillary/follicular variant, medullary and anaplastic cancers), benign follicular adenoma and normal thyroid. Extracted metabolites were identified and semi-quantified using the GC/MS approach. There were 28 metabolites identified, whose abundances were significantly different among different types of thyroid tumours, including lipids, carboxylic acids, and saccharides. We concluded, that multi-component metabolome signature could be used for classification of different subtypes of follicular thyroid lesions. Moreover, potential applicability of the GC/MS-based analysis of FFPE tissue samples in diagnostics of thyroid cancer has been proved.

GC-MS-based metabolomic analysis of human papillary thyroid carcinoma tissue

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Elucidating the molecular network that is altered in PTC may lead to the identification of the critical insight into the pathogenesis of PTC. Thus far, little is known regarding the global metabolomic alterations of PTC. Gas chromatography coupled with mass spectrometry-based metabolomics was used to analyze metabolomic alterations in matched PTC and normal thyroid tissues obtained from the patients. Multivariate statistical analyses were employed to determine the significant metabolomic differences. The mRNA levels of the associated metabolic enzyme genes were further assayed with reverse transcription-quantitative polymerase chain reaction analysis. Principal component analysis, partial least-squares discriminant analysis (PLS-DA) and orthogonal PLS-DA models were established, which could clearly separate human normal thyroid and PTC samples, and identified that metabolites in carbohydrate metabolism, including glucose, fructose, galactose, mannose, 2-keto-D-gluconic acid and rhamnose, consistently decreased, while metabolites in nucleotide metabolism, including malonic acid and inosine, and lipid metabolism, including cholesterol and arachidonic acid, significantly altered in PTC. Furthermore, the mRNA levels of metabolic enzyme genes, including glucose-6-phosphate dehydrogenase, phosphoglycerate kinase 1, lactate dehydrogenase A, phosphoglycerate dehydrogenase and prostaglandin-endoperoxide synthase 2, significantly increased in PTC. Based on the metabolomic and mRNA data, various metabolites may be used for increased synthesis of nucleotides and oncogenic lipids in PTC, which may contribute to the pathogenesis of PTC. The present study provides a new understanding of the dysregulated metabolism in PTC and identifies potential avenues for the therapeutic intervention for this disease.

Distinct Metabolomic Profiles of Papillary Thyroid Carcinoma and Benign Thyroid Adenoma

Papillary thyroid carcinoma (PTC) and benign thyroid adenoma (BTA) are the most common head and neck tumors. However, the metabolic differences between PTC and BTA have not been characterized. The aim of this study was to identify the metabolic profiles of these two types of tumors using a metabolomics approach. Tumors and adjacent nontumor specimens collected from 57 patients with PTC and 48 patients with BTA were profiled using gas chromatography-time-of-flight mass spectrometry and ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. A panel of 46 and 44 differentially expressed metabolites were identified in the PTC and BTA specimens, respetively, and compared with nontumor tissues. Common metabolic signatures, as characterized by increased glycolysis, amino acid metabolism, one carbon metabolism and tryptophan metabolism, were found in both types of tumors. Purine and pyrimidine metabolism was significantly elevated in the PTC specimens, and taurine and hypotaurine levels were also higher in the PTC tissues. Increased fatty acid and bile acid levels were found, especially in the BTA tissues. The metabolic profiles of the PTC and BTA tissues include both similar and remarkably different metabolites, suggesting the presence of common and unique mechanistic pathways in these types of tumors during tumorigenesis.

Allostasis and Resilience of the Human Individual Metabolic Phenotype

The urine metabotype of 12 individuals was followed over a period of 8-10 years, which provided the longest longitudinal study of metabolic phenotypes to date. More than 2000 NMR metabolic profiles were analyzed. The majority of subjects have a stable metabotype. Subjects who were exposed to important pathophysiological stressful conditions had a significant metabotype drift. When the stress conditions ceased, the original metabotypes were regained, while an irreversible stressful condition resulted in a permanent metabotype change. These results suggest that each individual occupies a well-defined region in the broad metabolic space, within which a limited degree of allostasis is permitted. The insurgence of significant stressful conditions causes a shift of the metabotype to another distinct region. The spontaneous return to the original metabolic region when the stressful conditions are removed suggests that the original metabotype has some degree of resilience. In this picture, precision medicine should aim at reinforcing the patient's metabolic resilience, that is, his or her ability to revert to his or her specific metabotype rather than to a generic healthy one.

Application of metabolomics in thyroid cancer research

Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput "omics" techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis.

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.

Proteomics, and metabolomics: magnetic resonance spectroscopy for the presurgical screening of thyroid nodules

We review the progress and state-of-the-art applications of studies in Magnetic Resonance Spectroscopy (MRS) and Imaging as an aid for diagnosis of thyroid lesions of different nature, especially focusing our attention to those lesions that are cytologically undetermined. It appears that the high-resolution of High-Resolution Magic-Angle-Spinning (HRMAS) MRS improves the overall accuracy of the analysis of thyroid lesions to a point that a significant improvement in the diagnosis of cytologically undetermined lesions can be expected. This analysis, in the meantime, allows a more precise comprehension of the alterations in the metabolic pathways induced by the development of the different tumors. Although these results are promising, at the moment, a clinical application of the method to the common workup of thyroid nodules cannot be used, due to both the limitation in the availability of this technology and the wide range of techniques, that are not uniformly used. The coming future will certainly see a wider application of these methods to the clinical practice in patients affected with thyroid nodules and various other neoplastic diseases.