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

Exploring the potential of myo-inositol in thyroid disease management: focus on thyroid cancer diagnosis and therapy

Thyroid cancer (TC) is a malignancy that is increasing in prevalence on a global scale, necessitating the development of innovative approaches for both diagnosis and treatment. Myo-inositol (MI) plays a crucial role in a wide range of physiological and pathological functions within human cells. To date, studies have investigated the function of MI in thyroid physiology as well as its potential therapeutic benefits for hypothyroidism and autoimmune thyroiditis. However, research in the field of TC is very restricted. Metabolomics studies have highlighted the promising diagnostic capabilities of MI, recognizing it as a metabolic biomarker for identifying thyroid tumors. Furthermore, MI can influence therapeutic characteristics by modulating key cellular pathways involved in TC. This review evaluates the potential application of MI as a naturally occurring compound in the management of thyroid diseases, including hypothyroidism, autoimmune thyroiditis, and especially TC. The limited number of studies conducted in the field of TC emphasizes the critical need for future research to comprehend the multifaceted role of MI in TC. A significant amount of research and clinical trials is necessary to understand the role of MI in the pathology of TC, its diagnostic and therapeutic potential, and to pave the way for personalized medicine strategies in managing this intricate disease.

Estrogen-related receptor alpha promotes thyroid tumor cell survival via a tumor subtype-specific regulation of target gene networks

Mortalin (encoded by HSPA9) is a mitochondrial chaperone often overexpressed in cancer through as-yet-unknown mechanisms. By searching different RNA-sequencing datasets, we found that ESRRA is a transcription factor highly correlated with HSPA9 in thyroid cancer, especially in follicular, but not C cell-originated, tumors. Consistent with this correlation, ESRRA depletion decreased mortalin expression only in follicular thyroid tumor cells. Further, ESRRA expression and activity were relatively high in thyroid tumors with oncocytic characteristics, wherein ESRRA and mortalin exhibited relatively high functional overlap. Mechanistically, ESRRA directly regulated HSPA9 transcription through a novel ESRRA-responsive element located upstream of the HSPA9 promoter. Physiologically, ESRRA depletion suppressed thyroid tumor cell survival via caspase-dependent apoptosis, which ectopic mortalin expression substantially abrogated. ESRRA depletion also effectively suppressed tumor growth and mortalin expression in the xenografts of oncocytic or ESRRA-overexpressing human thyroid tumor cells in mice. Notably, our Bioinformatics analyses of patient data revealed two ESRRA target gene clusters that contrast oncocytic-like and anaplastic features of follicular thyroid tumors. These findings suggest that ESRRA is a tumor-specific regulator of mortalin expression, the ESRRA-mortalin axis has higher significance in tumors with oncocytic characteristics, and ESRRA target gene networks can refine molecular classification of thyroid cancer.

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

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

Integrative Multi-omics Analysis Reveals Different Metabolic Phenotypes Based on Molecular Characteristics in Thyroid Cancer

PURPOSE

Thyroid cancer metabolic characteristics vary depending on the molecular subtype determined by mutational status. We aimed to investigate the molecular subtype-specific metabolic characteristics of thyroid cancers.

EXPERIMENTAL DESIGN

An integrative multi-omics analysis was conducted, incorporating transcriptomics, metabolomics, and proteomics data obtained from human tissues representing distinct molecular characteristics of thyroid cancers: BRAF-like (papillary thyroid cancer with BRAFV600E mutation; PTC-B), RAS-like (follicular thyroid cancer with RAS mutation; FTC-R), and ATC-like (anaplastic thyroid cancer with BRAFV600E or RAS mutation; ATC-B or ATC-R). To validate our findings, we employed tissue microarray of human thyroid cancer tissues and performed in vitro analyses of cancer cell phenotypes and metabolomic assays after inducing genetic knockdown.

RESULTS

Metabolic properties differed between differentiated thyroid cancers of PTC-B and FTC-R, but were similar in dedifferentiated thyroid cancers of ATC-B/R, regardless of their mutational status. Tricarboxylic acid (TCA) intermediates and branched-chain amino acids (BCAA) were enriched with the activation of TCA cycle only in FTC-R, whereas one-carbon metabolism and pyrimidine metabolism increased in both PTC-B and FTC-R and to a great extent in ATC-B/R. However, the protein expression levels of the BCAA transporter (SLC7A5) and a key enzyme in one-carbon metabolism (SHMT2) increased in all thyroid cancers and were particularly high in ATC-B/R. Knockdown of SLC7A5 or SHMT2 inhibited the migration and proliferation of thyroid cancer cell lines differently, depending on the mutational status.

CONCLUSIONS

These findings define the metabolic properties of each molecular subtype of thyroid cancers and identify metabolic vulnerabilities, providing a rationale for therapies targeting its altered metabolic pathways in advanced thyroid cancer.

Thyroid cancer cell metabolism: A glance into cell culture system-based metabolomics approaches

Thyroid cancer is the most common malignancy of the endocrine system and the seventh most prevalent cancer in women worldwide. It is a complex and diverse disease characterized by heterogeneity, underscoring the importance of understanding the underlying metabolic alterations within tumor cells. Metabolomics technologies offer a powerful toolset to explore and identify endogenous and exogenous biochemical reaction products, providing crucial insights into the intricate metabolic pathways and processes within living cells. Metabolism plays a central role in cell function, making metabolomics a valuable reflection of a cell's phenotype. In the OMICs era, metabolomics analysis of cells brings numerous advantages over existing methods, propelling cell metabolomics as an emerging field with vast potential for investigating metabolic pathways and their perturbation in pathophysiological conditions. This review article aims to look into recent developments in applying metabolomics for characterizing and interpreting the cellular metabolome in thyroid cancer cell lines, exploring their unique metabolic characteristics. Understanding the metabolic alterations in tumor cells can lead to the identification of critical nodes in the metabolic network that could be targeted for therapeutic intervention.

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.

Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma

Molecular subtyping of lung squamous cell carcinoma (LUSC) has been performed at the genomic, transcriptomic, and proteomic level. However, LUSC stratification based on tissue metabolomics is still lacking. Combining high-mass-resolution imaging mass spectrometry with consensus clustering, four tumor- and four stroma-specific subtypes with distinct metabolite patterns were identified in 330 LUSC patients. The first tumor subtype T1 negatively correlated with DNA damage and immunological features including CD3, CD8, and PD-L1. The same features positively correlated with the tumor subtype T2. Tumor subtype T4 was associated with high PD-L1 expression. Compared with the status of subtypes T1 and T4, patients with subtype T3 had improved prognosis, and T3 was an independent prognostic factor with regard to UICC stage. Similarly, stroma subtypes were linked to distinct immunological features and metabolic pathways. Stroma subtype S4 had a better prognosis than S2. Subsequently, analyses based on an independent LUSC cohort treated by neoadjuvant therapy revealed that the S2 stroma subtype was associated with chemotherapy resistance. Clinically relevant patient subtypes as determined by tissue-based spatial metabolomics are a valuable addition to existing molecular classification systems. Metabolic differences among the subtypes and their associations with immunological features may contribute to the improvement of personalized therapy.

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.

iDMET: network-based approach for integrating differential analysis of cancer metabolomics

BACKGROUND

Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study.

RESULTS

We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered.

CONCLUSIONS

We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.

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.

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.

The deubiquitinating enzyme ATXN3 promotes the progression of anaplastic thyroid carcinoma by stabilizing EIF5A2

Ataxin-3 (ATXN3) is a ubiquitous deubiquitinating enzyme that plays an essential role in the carcinogenesis of numerous tumors and stabilizes the expression of substrates by deubiquitination. However, the functional role of ATXN3 in anaplastic thyroid carcinoma (ATC) remains unknown. In this research, we report that ATXN3 was overexpressed in ATC compared to that in paracancerous samples. Moreover, various gain/loss functional assays were performed to indicate that ATXN3 overexpression enhanced ATC cell proliferation and metastasis. We also found that ATXN3 and eukaryotic translation initiation factor 5A2 (EIF5A2) protein levels in ATC tissues are positively correlated, and ATXN3 promotes the proliferation and metastasis of ATC cells through EIF5A2. Mechanistically, ATXN3 promotes EIF5A2 expression by directly binding to EIF5A2 to reduce its ubiquitination and degradation. Therefore, for the first time, we clarified the role of ATXN3 in the carcinogenesis of ATC cells, which provides novel insights into potential therapeutic targets for ATC progression.

Metabolic Reprogramming in Thyroid Cancer: Role of the Epithelial-Mesenchymal Transition

Thyroid cancer (TC) represents the most common endocrine malignancy, with an increasing incidence all over the world. Papillary TC (PTC), a differentiated TC subtype, is the most common and, even though it has an excellent prognosis following radioiodine (RAI) ablation, it shows an aggressive behavior in 20–30% of cases, becoming RAI-resistant and/or metastatic. On the other side, anaplastic thyroid carcinoma (ATC), the most undifferentiated TC, is a rare but devastating disease, indicating that progression of differentiated to undifferentiated forms of TC could be responsible for RAI-resistance and increased mortality. The epithelial-to-mesenchymal transition (EMT) plays a pivotal role in both tumor progression and resistance to therapy. Moreover, during tumor progression, cancer cells modify their metabolism to meet changed requirements for cellular proliferation. Through these metabolic changes, cancer cells may adopt cancer stem cell-like properties and express an EMT phenotype. EMT, in turn, can induce metabolic changes to which cancer cells become addicted. Here we review metabolic reprogramming in TC highlighting the role of EMT with the aim to explore a potential field to find out new therapeutic strategies for advanced-stage PTC. Accordingly, we discuss the identification of the metabolic enzymes and metabolites, critical to TC progression, which can be employed either as predicting biomarkers of tumor response to RAI therapy or possible targets in precision medicine.

Metabolomics of Oral/Head and Neck Cancer

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

CSN5 Promotes Carcinogenesis of Thyroid Carcinoma Cells Through ANGPTL2

COP9 signalosome subunit 5 (CSN5) plays a key role in carcinogenesis of multiple cancers and contributes to the stabilization of target proteins through deubiquitylation. However, the underlying role of CSN5 in thyroid carcinoma has not been reported. In this research, our data showed that CSN5 was overexpressed in thyroid carcinoma tissues compared with paracancerous tissues. Furthermore, a series of gain/loss functional assays were performed to demonstrate the role of CSN5 in facilitating thyroid carcinoma cell proliferation and metastasis. Additionally, we found there was a positive correlation between CSN5 and angiopoietin-like protein 2 (ANGPTL2) protein levels in thyroid carcinoma tissues and that CSN5 promoted thyroid carcinoma cell proliferation and metastasis through ANGPTL2. We also identified the underlying mechanism that CSN5 elevated ANGPTL2 protein level by directly binding it, decreasing its ubiquitination and degradation. Overall, our results highlight the significance of CSN5 in promoting thyroid carcinoma carcinogenesis and implicate CSN5 as a promising candidate for thyroid carcinoma treatment.

A Novel Nanoproteomic Approach for the Identification of Molecular Targets Associated with Thyroid Tumors

A thyroid nodule is the most common presentation of thyroid cancer; thus, it is extremely important to differentiate benign from malignant nodules. Within malignant lesions, classification of a thyroid tumor is the primary step in the assessment of the prognosis and selection of treatment. Currently, fine-needle aspiration biopsy (FNAB) is the preoperative test most commonly used for the initial thyroid nodule diagnosis. However, due to some limitations of FNAB, different high-throughput “omics” approaches have emerged that could further support diagnosis based on histopathological patterns. In the present work, formalin-fixed paraffin-embedded (FFPE) tissue specimens from normal (non-neoplastic) thyroid (normal controls (NCs)), benign tumors (follicular thyroid adenomas (FTAs)), and some common types of well-differentiated thyroid carcinoma (follicular thyroid carcinomas (FTCs), conventional or classical papillary thyroid carcinomas (CV-PTCs), and the follicular variant of papillary thyroid carcinomas (FV-PTCs)) were analyzed. For the first time, FFPE thyroid samples were deparaffinized using an easy, fast, and non-toxic method. Protein extracts from thyroid tissue samples were analyzed using a nanoparticle-assisted proteomics approach combined with shotgun LC-MS/MS. The differentially regulated proteins found to be specific for the FTA, FTC, CV-PTC, and FV-PTC subtypes were analyzed with the bioinformatic tools STRING and PANTHER showing a profile of proteins implicated in the thyroid cancer metabolic reprogramming, cancer progression, and metastasis. These proteins represent a new source of potential molecular targets related to thyroid tumors.

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.

OMICS insights into cancer histology; Metabolomics and proteomics approach

Metabolomics as a post-genomic research area comprising different analytical methods for small molecules analysis. One of the underlying applications of metabolomics technology for better disease diagnosis and prognosis is discovering the metabolic pathway differences between healthy individuals and patients. On the other hand, the other noteworthy applications of metabolomics include its effective role in biomarker screening for cancer detection, monitoring, and prediction. In other words, emerging of the metabolomics field can be hopeful to provide a suitable alternative for the common current cancer diagnostic methods especially histopathological tests. Indeed, cancer as a major global issue places a substantial burden on the health care system. Hence, proper management can be beneficial. In this respect, formalin-fixed paraffin-embedded tissue specimens (in histopathological tests) are considered as a valuable source for metabolomics investigations. Interestingly, formalin-fixed paraffin-embedded tissue specimens can provide informative data for cancer management. In general, using these specimens, determining the cancer stage, individual response to the different therapies, personalized risk prediction are possible and high-quality clinical services are the promise of OMICS technologies for cancer disease. However, considering all of these beneficial characteristics, there are still some limitations in this area that need to be addressed in order to optimize the metabolomics utilizations and advancement.

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.

The key points in the pre-analytical procedures of blood and urine samples in metabolomics studies

BACKGROUND

Metabolomics provides measurement of numerous metabolites in human samples, which can be a useful tool in clinical research. Blood and urine are regarded as preferred subjects of study because of their minimally invasive collection and simple preprocessing methods. Adhering to standard operating procedures is an essential factor in ensuring excellent sample quality and reliable results.

AIM OF REVIEW

In this review, we summarize the studies about the impacts of various preprocessing factors on metabolomics studies involving clinical blood and urine samples in order to provide guidance for sample collection and preprocessing.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical information is important for sample grouping and data analysis which deserves attention before sample collection. Plasma and serum as well as urine samples are appropriate for metabolomics analysis. Collection tubes, hemolysis, delay at room temperature, and freeze-thaw cycles may affect metabolic profiles of blood samples. Collection time, time between sampling and examination, contamination, normalization strategies, and storage conditions may alter analysis results of urine samples. Taking these collection and preprocessing factors into account, this review provides suggestions of standard sample preprocessing.

Targeting the pentose phosphate pathway increases reactive oxygen species and induces apoptosis in thyroid cancer cells

The pentose phosphate pathway (PPP) plays an important role in the biosynthesis of ribonucleotide precursor and NADPH. Cancer cells frequently increase the flux of glucose into the PPP to support the anabolic demands and regulate oxidative stress. Consistently, metabolomic analyses indicate an upregulation of the PPP in thyroid cancer. In the present study, we found that the combination of glucose-6-phosphate dehydrogenase (G6PD) and transketolase inhibitors (6-aminonicotinamide and oxythiamine) exerted an additive or synergistic effect on cell growth inhibition in thyroid cancer cells. Targeting PPP significantly increased cellular reactive oxygen species (ROS) and induced endoplasmic reticulum (ER) stress and apoptosis. Suppressed cell viability could be partially rescued with treatment with the ROS scavenger or apoptosis inhibitor but not ER-stress inhibitor. Taken together, dual PPP blockade leads to pharmacologic additivity or synergism and causes ROS-mediated apoptosis in thyroid cancer cells.

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

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

UPLC‑MS/MS‑based metabolomic characterization and comparison of pancreatic adenocarcinoma tissues using formalin‑fixed, paraffin‑embedded and optimal cutting temperature‑embedded materials

The purpose of the present study was to compare metabolites from formalin-fixed and paraffin-embedded (FFPE) pancreatic tissue blocks with those identified in optimal cutting temperature (OCT)-embedded pancreatic tissue blocks. Thus, ultra-performance liquid chromatograph-mass spectrometry/mass spectrometry-based metabolic profiling was performed in paired frozen (n=13) and FFPE (n=13) human pancreatic adenocarcinoma tissue samples, in addition to their benign counterparts. A total of 206 metabolites were identified in both OCT-embedded and FFPE tissue samples. The method feasibility was confirmed through reproducibility and a consistency assessment. Partial least-squares discriminant analysis and heatmap analysis reliably distinguished tumor and normal tissue phenotypes. The expression of 10 compounds, including N-acetylaspartate and creatinine, was significantly different in both OCT-embedded and FFPE tumor samples. These ten compounds may be viable candidate biomarkers of malignant pancreatic tissues. The super-categories to which they belonged exhibited no significant differences between FFPE and OCT-embedded samples. Furthermore, purine, arginine and proline, and pyrimidine metabolism used a shared pathway found in both OCT-embedded and FFPE tissue samples. These results supported the notion that metabolomic data acquired from FFPE pancreatic cancer specimens are reliable for use in retrospective and clinical studies.

Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies

Thyroid neoplasia is common and requires appropriate clinical workup with imaging and fine-needle aspiration (FNA) biopsy to evaluate for cancer. Yet, up to 20% of thyroid nodule FNA biopsies will be indeterminate in diagnosis based on cytological evaluation. Genomic approaches to characterize the malignant potential of nodules showed initial promise but have provided only modest improvement in diagnosis. Here, we describe a method using metabolic analysis by desorption electrospray ionization mass spectrometry (DESI-MS) imaging for direct analysis and diagnosis of follicular cell-derived neoplasia tissues and FNA biopsies. DESI-MS was used to analyze 178 tissue samples to determine the molecular signatures of normal, benign follicular adenoma (FTA), and malignant follicular carcinoma (FTC) and papillary carcinoma (PTC) thyroid tissues. Statistical classifiers, including benign thyroid versus PTC and benign thyroid versus FTC, were built and validated with 114,125 mass spectra, with accuracy assessed in correlation with clinical pathology. Clinical FNA smears were prospectively collected and analyzed using DESI-MS imaging, and the performance of the statistical classifiers was tested with 69 prospectively collected clinical FNA smears. High performance was achieved for both models when predicting on the FNA test set, which included 24 nodules with indeterminate preoperative cytology, with accuracies of 93% and 89%. Our results strongly suggest that DESI-MS imaging is a valuable technology for identification of malignant potential of thyroid nodules.

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.

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.

Formalin-Fixed Paraffin-Embedded Tissues-An Untapped Biospecimen for Biomonitoring DNA Adducts by Mass Spectrometry

The measurement of DNA adducts provides important information about human exposure to genotoxic chemicals and can be employed to elucidate mechanisms of DNA damage and repair. DNA adducts can serve as biomarkers for interspecies comparisons of the biologically effective dose of procarcinogens and permit extrapolation of genotoxicity data from animal studies for human risk assessment. One major challenge in DNA adduct biomarker research is the paucity of fresh frozen biopsy samples available for study. However, archived formalin-fixed paraffin-embedded (FFPE) tissues with clinical diagnosis of disease are often available. We have established robust methods to recover DNA free of crosslinks from FFPE tissues under mild conditions which permit quantitative measurements of DNA adducts by liquid chromatography-mass spectrometry. The technology is versatile and can be employed to screen for DNA adducts formed with a wide range of environmental and dietary carcinogens, some of which were retrieved from section-cuts of FFPE blocks stored at ambient temperature for up to nine years. The ability to retrospectively analyze FFPE tissues for DNA adducts for which there is clinical diagnosis of disease opens a previously untapped source of biospecimens for molecular epidemiology studies that seek to assess the causal role of environmental chemicals in cancer etiology.

Thyroid follicular adenomas and carcinomas: molecular profiling provides evidence for a continuous evolution

Non-autonomous thyroid nodules are common in the general population with a proportion found to be cancerous. A current challenge in the field is to be able to distinguish benign adenoma (FA) from preoperatively malignant thyroid follicular carcinoma (FTC), which are very similar both histologically and genetically. One controversial issue, which is currently not understood, is whether both tumor types represent different molecular entities or rather a biological continuum. To gain a better insight into FA and FTC tumorigenesis, we defined their molecular profiles by mRNA and miRNA microarray. Expression data were analyzed, validated by qRT-PCR and compared with previously published data sets. The majority of deregulated mRNAs were common between FA and FTC and were downregulated, however FTC showed additional deregulated mRNA. Both types of tumors share deregulated pathways, molecular functions and biological processes. The additional deregulations in FTC include the lipid transport process that may be involved in tumor progression. The strongest candidate genes which may be able to discriminate follicular adenomas and carcinomas, CRABP1, FABP4 and HMGA2, were validated in independent samples by qRT-PCR and immunohistochemistry. However, they were not able to adequately classify FA or FTC, supporting the notion of continuous evolving tumors, whereby FA and FTC appear to show quantitative rather than qualitative changes. Conversely, miRNA expression profiles showed few dysregulations in FTC, and even fewer in FA, suggesting that miRNA play a minor, if any, role in tumor progression.

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.

miR-146a and miR-146b in the diagnosis and prognosis of papillary thyroid carcinoma

The present study investigated the relationship between the expression of miR-146a and miR-146b with the occurrence and prognosis of papillary thyroid carcinoma. Experiments in vitro were also used to explore the effect of the knocked down expression of the miRNAs on growth and migration of papillary thyroid carcinoma cells. A total of 73 patients with papillary thyroid carcinoma admitted to Yidu Central Hospital of Weifang from September 2013 to September 2015 were enrolled in the study. Carcinoma samples were obtained from each patient, and adjacent tissues were used as control samples to determine expression levels of miR-146a and miR146b by semi-quantitative RT-PCR. An analysis was conducted to find possible correlations between the miRNAs expression levels and clinicopathological features in the patients followed up for one year after diagnosis. Additionally, to examine the function of miR-146a and miR-146b on TPC-1 cells, the expression of miRNAs was knocked down using specific siRNAs. MTT and Transwell assays were used to evaluate cell proliferation and migration, respectively, in the miRNA cell lines. Finally, western blot analysis was used to analyze the expression of IRAK1 in PTC cancer cells. Our results showed that the expression levels of miR-146a and miR-146b in carcinoma tissues were significantly higher than the levels in cancer-free tissues (P<0.01). The relative expression levels of miR-146a and miR-146b in cancerous tissues could be associated with the pathological type and presence or absence of lymph node metastasis (P<0.05). Compared with the siRNA-control cell, MTT and Transwell assays showed that the cell growth and migration of TPC-1 cells were decreased in miR-146a and miR-146b low expression cells (P<0.01). Western blot analysis showed that the expression of IRAK1 in papillary thyroid carcinoma was higher than in adjacent tissue (P<0.01). Based on our findings, the expression of miR-146a and miR-146b correlates with the occurrence and prognosis of papillary thyroid carcinoma, and the expression levels of miR-146a and miR-146b seem to affect the cell proliferation and migration and regulate the expression of IRAK1 protein in cancer cells. Further studies are needed to validate our results to provide new targets for prevention and treatment of papillary thyroid carcinoma.

Applications of Metabolomics in Cancer Studies

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

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Utility of the succinate: Fumarate ratio for assessing SDH dysfunction in different tumor types

Objective

Mutations of genes encoding the four subunits of succinate dehydrogenase (SDH) have been associated with pheochromocytoma and paraganglioma (PPGLs), gastrointestinal stromal tumors (GISTs) and renal cell carcinomas (RCCs). These tumors have not been characterized in a way that reflects severity of SDH dysfunction. Mass spectrometric analysis now allows measurement of metabolites extracted from formalin fixed paraffin embedded (FFPE) specimens. We assess whether SDH deficiency in various tumor types characterized by loss of SDHB protein expression correlates with SDH dysfunction as assessed by the ratio of succinate:fumarate in FFPE specimens.

Patients and methods

Sections of FFPE tumor specimens from 18 PPGL, 10 GIST and 11 RCC patients with known SDHx mutation status for SDH deficiency were collected for mass spectrometric analysis of succinate and fumarate.

Results

FFPE samples showed higher succinate:fumarate ratios in SDH-deficient PPGLs compared to SDH-sufficient PPGLs. Similarly, a higher succinate:fumarate ratio was able to distinguish SDH-deficient GISTs and RCCs from their SDH-sufficient counterparts with great selectivity. Interestingly, the cut-off value of the succinate:fumarate ratio was two-folds greater in RCCs than GISTs.

Conclusion

Analyzing biochemical imbalances preserved in FFPE specimens with mass spectrometry expands the method and sample type repertoire available for characterisation of multiple neoplasias associated with SDH deficiency.

In Situ Metabolomics in Cancer by Mass Spectrometry Imaging

Metabolomics is a rapidly evolving and a promising research field with the expectation to improve diagnosis, therapeutic treatment prediction, and prognosis of particular diseases. Among all techniques used to assess the metabolome in biological systems, mass spectrometry imaging is the method of choice to qualitatively and quantitatively analyze metabolite distribution in tissues with a high spatial resolution, thus providing molecular data in relation to cancer histopathology. The technique is ideally suited to study tissues molecular content and is able to provide molecular biomarkers or specific mass signatures which can be used in classification or the prognostic evaluation of tumors. Recently, it was shown that FFPE tissue samples are also suitable for metabolic analyses. This progress in methodology allows access to a highly valuable resource of tissues believed to widen and strengthen metabolic discovery-driven studies.

Molecular profiles of thyroid cancer subtypes: Classification based on features of tissue revealed by mass spectrometry imaging

Determination of the specific type of thyroid cancer is crucial for the prognosis and selection of treatment of this malignancy. However, in some cases appropriate classification is not possible based on histopathological features only, and it might be supported by molecular biomarkers. Here we aimed to characterize molecular profiles of different thyroid malignancies using mass spectrometry imaging (MSI) which enables the direct annotation of molecular features with morphological pictures of an analyzed tissue. Fifteen formalin-fixed paraffin-embedded tissue specimens corresponding to five major types of thyroid cancer were analyzed by MALDI-MSI after in-situ trypsin digestion, and the possibility of classification based on the results of unsupervised segmentation of MALDI images was tested. Novel method of semi-supervised detection of the cancer region of interest (ROI) was implemented. We found strong separation of medullary cancer from malignancies derived from thyroid epithelium, and separation of anaplastic cancer from differentiated cancers. Reliable classification of medullary and anaplastic cancers using an approach based on automated detection of cancer ROI was validated with independent samples. Moreover, extraction of spectra from tumor areas allowed the detection of molecular components that differentiated follicular cancer and two variants of papillary cancer (classical and follicular). We concluded that MALDI-MSI approach is a promising strategy in the search for biomarkers supporting classification of thyroid malignant tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.

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.