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

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.

Experimental study on changes in metabolic mechanism of papillary thyroid carcinoma complicated with Hashimoto's thyroiditis

Background

Whether the mechanism of thyroid papillary carcinoma (PTC) is the same in patients with a Hashimoto's thyroiditis (HT) background as compared with patients with a normal background remains a highly debated and controversial issue. In this study, we aimed to analyze the differences and similarities of the metabolic mechanism of PTC in normal and HT background, and to explore the relationship between HT and PTC.

Methods

The ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF/MS) technology was used to analyze 61 PTC patient tissues (31 HT background and 30 normal tissue (NC) background). Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least square (OPLS) discriminant analysis. HMDB was searched to identify potential differential metabolites and final metabolic pathway analysis was performed by MetaboAnalyst 5.0. We analyzed the differential metabolites diagnostic accuracy through receiver operating characteristic (ROC) curves analysis.

Results

Seven different metabolites were screened from HT group and NC group, including arginine, glutamic acid, cysteine, citric acid, malic acid, uracil and taurine. Logistic regression model combined with ROC analysis of these 7 biomarkers had good discriminability for PTC (area under operating characteristic curve of HT group and NC group were 0.867 and 0.973, respectively). The HT group had specific metabolic pathways, including aminoacyl-tRNA biosynthesis, glycine, serine and threonine metabolism.

Conclusions

The metabolic profiles of the NC and HT groups had important similarities and differences in PTC. The correlation of PTC with HT may be related to aminoacyl-tRNA biosynthesis, serine and threonine metabolism.

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.

Radiomics Analysis of Gray-Scale Ultrasonographic Images of Papillary Thyroid Carcinoma > 1 cm: Potential Biomarker for the Prediction of Lymph Node Metastasis

Purpose

This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients.

Materials and Methods

This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set.

Results

Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set.

Conclusion

This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.

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.

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.

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.

Gut Microbiome Alterations in Patients With Thyroid Nodules

Thyroid nodules are found in nearly half of the adult population. Accumulating evidence suggests that the gut microbiota plays an important role in thyroid metabolism, yet the association between gut microbiota capacity, thyroid nodules, and thyroid function has not been studied comprehensively. We performed a gut microbiome genome-wide association study in 196 patients with thyroid nodules and 283 controls by using whole-genome shotgun sequencing. We found that participants with high-grade thyroid nodules have decreased number of gut microbial species and gene families compared with those with lower grade nodules and controls. There are also significant alterations in the overall microbial composition in participants with high-grade thyroid nodules. The gut microbiome in participants with high-grade thyroid nodules is characterized by greater amino acid degradation and lower butyrate production. The relative abundances of multiple butyrate producing microbes are reduced in patients with high-grade thyroid nodules and the relative abundances of L-histidine metabolism pathways are associated with thyrotropin-releasing hormone. Our study describes the gut microbiome characteristics in thyroid nodules and a gut-thyroid link and highlight specific gut microbiota as a potential therapeutic target to regulate thyroid metabolism.

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it greatly relies on visual examination. Hence, there is a need to develop a more sensitive and specific method for diagnosing cancer. Here, Fourier transform infrared (FTIR) spectroscopy of thyroid tumors (n = 164; 76 malignant, 88 benign) was performed and five (5) neural network (NN) models were designed to discriminate the obtained spectral data. PCA-LDA was used as classical benchmark for comparison. Each NN model was evaluated using a stratified 10-fold cross-validation method to avoid overfitting, and the performance metrics-accuracy, area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NN models were able to perform excellently as classifiers, and all were able to surpass the LDA model in terms of accuracy. Among the NN models, the RNN model performed best, having an AUC of 95.29% ± 6.08%, an accuracy of 98.06% ± 2.87%, a PPV of 98.57% ± 4.52%, a NPV of 93.18% ± 7.93%, a SR value of 98.89% ± 3.51%, and a RR value of 91.25% ± 10.29%. The RNN model outperformed the LDA model for all metrics except for the AUC, NPV, and RR. In conclusion, NN-based tools were able to predict thyroid cancer based on infrared spectroscopy of tissues with a high level of diagnostic performance in comparison to the gold standard.

Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS

This study examined metabolite profile differences between serum samples of thyroid papillary carcinoma (PTC) patients and healthy controls, aiming to identify candidate biomarkers and pathogenesis pathways in this cancer type. Serum samples were collected from PTC patients (n = 80) and healthy controls (n = 80). Using principal component analysis (PCA), partial least squares discrimination analysis(PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), t-tests, and the volcano plot, a model of abnormal metabolic pathways in PTC was constructed. PCA, PLS-DA, and OPLS-DA analysis revealed differences in serum metabolic profiles between the PTC and control group. OPLS-Loading plot analysis, combined with Variable importance in the projection (VIP)>1, Fold change (FC) > 1.5, and p < 0.05 were used to screen 64 candidate metabolites. Among them, 22 metabolites, including proline betaine, taurocholic acid, L-phenylalanine, retinyl beta-glucuronide, alpha-tocotrienol, and threonine acid were upregulated in the PTC group; meanwhile, L-tyrosine, L-tryptophan, 2-arachidonylglycerol, citric acid, and other 42 metabolites were downregulated in this group. There were eight abnormal metabolic pathways related to the differential metabolites, which may be involved in the pathophysiology of PTC. Six metabolites yielded an area under the receiver operating curve of >0.75, specifically, 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanylphenylalanine, and l-glutamic acid. The Warburg effect was observed in PTC. The levels of 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanine, and L-glutamic acid may help distinguish PTC patients from healthy controls. Aspartic acid metabolism, glutamic acid metabolism, urea cycle, and tricarboxylic acid cycle are involved in the mechanism of PTC.

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.

Plasma levels of soluble HLA-G and cytokines in papillary thyroid carcinoma before and after thyroidectomy

BACKGROUND

Increasing evidence shows that chronic inflammation plays an important role in thyroid tumorigenesis. Cytokines as central mediators in inflammatory microenvironment can present both pro-tumour and anti-tumour effects and cytokine release may be influenced by soluble HLA-G (sHLA-G), an immune checkpoint molecule whose expression can also be induced by certain cytokines.

AIM

To understand the role of these soluble factors in papillary thyroid cancer (PTC).

METHODS

We evaluated plasma levels of sHLA-G and of 13 cytokines using ELISA and flow cytometry, respectively, in PTC patients at two time points: pre- and post-thyroidectomy; and control subjects.

RESULTS

Compared with controls, IL-6 levels were increased, while IL-1β, IFN-α and TGF-β1 levels were decreased in pre-thyroidectomy PTC patients. IFN-α and TGF-β1 efficiently discriminated patients from controls and were associated with extrathyroidal extension and lymph node metastasis, respectively. In addition, TNF and IL-13 were associated with male gender, lymph node metastasis and Hashimoto thyroiditis, and sHLA-G with tumour invasion. Compared with pre-thyroidectomy, IL-4, IL-10, TNF, IFN-α and TGF-β1 levels were increased in post-thyroidectomy.

CONCLUSION

There are significant changes in the cytokine profile after surgical removal of the thyroid tumour, and IFN-α e TGF-β1 showed to be promising cytokines for discriminating PTC patients from controls. We also found that different cytokines are associated with clinicohistopathological characteristics of PTC related to poor prognosis, suggesting that cytokines seem to play an important role in PTC development and management.

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.

Metabolomics as a potential method for predicting thyroid malignancy in children and adolescents

PURPOSE

To identify potential metabolic biomarkers for distinguishing malignant and benign thyroid nodules in children and adolescents using a metabolomics approach.

METHODS

A total of 96 consecutive patients (median age 14.29 ± 2.31 years, range 9-18 years) who underwent thyroidectomy and 40 healthy controls were enrolled. Patients were assigned to the papillary thyroid carcinoma and benign thyroid adenoma groups according to postoperative pathologic biopsy. Plasma samples were preoperatively collected, and multivariate analysis was performed to identify differential metabolites.

RESULTS

Papillary thyroid carcinoma could be distinguished not only from healthy serum but also from benign thyroid adenoma according to the metabolic profiles. A total of 17 metabolites were identified. Compared with those from benign thyroid adenoma patients and healthy controls, the metabolites from papillary thyroid carcinoma patients, including leucine, lactate, alanine, glycine, acetate, lysine and choline, were increased, while glucose was decreased.

CONCLUSION

The metabolomics method based on proton nuclear magnetic resonance has great potential for identifying papillary thyroid carcinoma in children and adolescents. Lactate and glycine may be used as potential serum markers for the diagnosis of papillary thyroid carcinoma.

Distribution and malignancy risk of six categories of the pathology reporting system for thyroid core-needle biopsy in 1,216 consecutive thyroid nodules

PURPOSE

The purpose of this study was to present the distribution of lesions among the six categories of the pathology reporting system for thyroid core-needle biopsy (CNB), along with the range of malignancy risk of each category based on different diagnostic criteria for benignity in a clinical cohort.

METHODS

For 1,216 consecutive nodules (≥1 cm) of 1,125 patients who underwent CNB at two hospitals, the diagnostic results based on the six categories of thyroid CNB were analyzed. Patients were divided into three groups according to prior fine-needle aspiration (FNA) status: second-line CNB for nodules where prior FNA yielded nondiagnostic or unsatisfactory results (n=57), second-line CNB for nodules with prior FNA results of atypia/follicular lesion of undetermined significance (AUS/FLUS) (n=303), and first-line CNB (n=856).

RESULTS

The proportion of nodules in each CNB category and the range of the malignancy rate for each category was as follows, in order from category I to VI: 1.8%, 23.1%-75.0%; 57.9%, 0.7%-16.7%; 16.0%, 13.2%-46.7%; 8.8%, 53.8%-56.8%; 2.0%, 100%; and 13.5%, 100%. First-line CNB was associated with a higher rate of conclusive diagnoses (category II, IV, or VI) (725 of 856, 84.7%) than second-line CNB with prior nondiagnostic or AUS/FLUS FNA results (241 of 360, 66.9%; P<0.001).

CONCLUSION

The overall distribution of nodules across the six categories of thyroid CNB and the ranges of malignancy risk for those categories were presented in a clinical cohort. First-line CNB tended to produce a higher rate of conclusive results than second-line CNB with prior inconclusive FNA results.

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.

Diagnosis of acute pediatric appendicitis from children with inflammatory diseases by combination of metabolic markers and inflammatory response variables

BACKGROUND

The discovery of new metabolic markers may be helpful for early diagnosis of acute pediatric appendicitis (APA). However, no studies have been reported regarding identification of potential metabolic markers for the APA diagnosis by metabonomics.

METHODS

Serum samples of APA (n=32), non-appendicitis inflammation (NAI, n=32) and healthy children (HS, n=65) were analyzed by the 1H NMR-based metabonomics. A logistic regression model was established to screen the most efficient markers combinations for classification. Forty double-blind samples were further validated the model.

RESULTS

Nine blood metabolites that were different in the APA group from other groups were identified. To differentiate APA from HS, single variable of acetate, formate, white blood cell (WBC) and C-reactive protein (CRP) showed a high diagnostic value (area under the receiver operating characteristic [AUROC]<0.92), while they had a weak diagnostic value (AUROC<0.77) for identifying the APA and NAI. By contrast, the AUROC values of leucine (0.799) were higher than that of WBC and CRP. A combination of five variables, i.e. leucine, lactate, betaine, WBC and CRP, showed a high diagnostic value (AUROC=0.973) for the APA discriminating from the NAI, and the sensitivity and specificity were 93.8% and 93.7%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 85% for 40 unknown samples.

CONCLUSIONS

The current study provides useful information in our understanding of the metabolic alterations associated with APA and indicates that measurement of these metabolites in serum effectively aids in the clinical identification of APA.

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.

Discovery and Validation of Potential Serum Biomarkers for Pediatric Patients with Congenital Heart Diseases by Metabolomics

To identify and screen serum biomarkers to determine pediatric patients with congenital heart diseases (PCH) from healthy control children (NC), a total of 614 clinically diagnosed subjects from three hospitals, including 491 PCH and 234 NC, were enrolled for nontargeted proton nuclear magnetic resonance spectroscopy (¹H NMR)-based and targeted ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS)-based metabolomics studies. Nineteen serum metabolites distinguishing PCH from NC were identified by ¹H NMR-based metabolomic analysis. The amino acid and choline metabolic pathways were considered to be closely related to PCH. The serum levels of 13 metabolites in these two pathways were further determined by UPLC-MS/MS and observed to be altered significantly in PCH. Taurine, glutamine, and glutamate presented considerable diagnostic value for the diagnosis of PCH (AUROC > 0.80). Logistic regression analysis showed that a combination of four variables, namely, betaine, taurine, glutamine, and phenylalanine, yields a high diagnostic value (AUROC = 0.949) and prediction accuracy (89.1%) for differentiating PCH from the NC, and the sensitivity and specificity were 93.9 and 95.2%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 83.8% for 80 unknown samples. Our results showed that the serum amino acid and choline metabolite levels in PCH were changed considerably. The combination of four metabolites, namely, betaine, taurine, glutamine, and phenylalanine, can be used as potential serum biomarkers in PCH diagnosis, which contributes to the early PCH screening.

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.

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.

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.