Oncometabolites as biomarkers in thyroid cancer: a systematic review

Deciphering the multifaceted roles and clinical implications of 2-hydroxyglutarate in cancer

Increasing evidence indicates that 2-hydroxyglutarate (2HG) is an oncometabolite that drives tumour formation and progression. Due to mutations in isocitrate dehydrogenase (IDH) and the dysregulation of other enzymes, 2HG accumulates significantly in tumour cells. Due to its structural similarity to α-ketoglutarate (αKG), accumulated 2HG leads to the competitive inhibition of αKG-dependent dioxygenases (αKGDs), such as KDMs, TETs, and EGLNs. This inhibition results in epigenetic alterations in both tumour cells and the tumour microenvironment. This review comprehensively discusses the metabolic pathways of 2HG and the subsequent pathways influenced by elevated 2HG levels. We will delve into the molecular mechanisms by which 2HG exerts its oncogenic effects, particularly focusing on epigenetic modifications. This review will also explore the various methods available for the detection of 2HG, emphasising both current techniques and emerging technologies. Furthermore, 2HG shows promise as a biomarker for clinical diagnosis and treatment. By integrating these perspectives, this review aims to provide a comprehensive overview of the current understanding of 2HG in cancer biology, highlight the importance of ongoing research, and discuss future directions for translating these findings into clinical applications.

Glycosylation in cancer as a source of biomarkers

INTRODUCTION

Glycosylation, the process of glycan synthesis and attachment to target molecules, is a crucial and common post-translational modification (PTM) in mammalian cells. It affects the protein's hydrophilicity, charge, solubility, structure, localization, function, and protection from proteolysis. Aberrant glycosylation in proteins can reveal new detection and therapeutic Glyco-biomarkers, which help to improve accurate early diagnosis and personalized treatment. This review underscores the pivotal role of glycans and glycoproteins as a source of biomarkers in human diseases, particularly cancer.

AREAS COVERED

This review delves into the implications of glycosylation, shedding light on its intricate roles in cancer-related cellular processes influencing biomarkers. It is underpinned by a thorough examination of literature up to June 2024 in PubMed, Scopus, and Google Scholar; concentrating on the terms: (Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).

EXPERT OPINION

Glyco-biomarkers enhance early cancer detection, allow early intervention, and improve patient prognoses. However, the abundance and complex dynamic glycan structure may make their scientific and clinical application difficult. This exploration of glycosylation signatures in cancer biomarkers can provide a detailed view of cancer etiology and instill hope in the potential of glycosylation to revolutionize cancer research.

The Role of Inositols in Endocrine and Neuroendocrine Tumors

Inositols have demonstrated a role in cancer prevention and treatment in many kinds of neoplasms. Their molecular mechanisms vary from the regulation of survival and proliferative pathways to the modulation of immunity and oxidative stress. The dysregulation of many pathways and mechanisms regulated by inositols has been demonstrated in endocrine and neuroendocrine tumors but the role of inositol supplementation in this context has not been clarified. The aim of this review is to summarize the molecular basis of the possible role of inositols in endocrine and neuroendocrine tumors, proposing it as an adjuvant therapy.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics

INTRODUCTION

Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population.

OBJECTIVES

This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs).

METHODS

We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers.

RESULTS

The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

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.

TiO2@COF-based solid-phase microextraction combined with UHPLC-MS/MS for the rapid determination of potential biomarkers of phosphatidylcholines and lysophosphatidylcholines in head and neck cancers

Phosphatidylcholine (PC) and lysophosphatidylcholine (LPC), two types of phospholipids (PLs), have been reported to be closely correlated with head and neck cancers of laryngeal cancer (LC) and thyroid cancer (TC), which make their analysis crucial. TiO2@COF-based solid-phase microextraction (SPME) coupled to UHPLC-MS/MS was developed for the rapid and accurate detection of seven potential PL biomarkers from small amounts of serum in this work. The combination of TiO2 and COF proves to be effective for the extraction of the target analytes. Under optimal conditions, the developed TiO2@COF-based SPME-UHPLC-MS/MS method revealed good linearity (R2 ≥ 0.997) with LODs ranging from 0.05 to 0.38 ng/mL for PLs, the extraction recoveries and matrix effects ranging from 83.09-112.03% and 85.38-113.67%, respectively. As a high-throughput pretreatment method, satisfactory probe-to-probe reproducibility rates of 2.7-10.1% were obtained. Finally, the TiO2@COF-based SPME-UHPLC-MS/MS method was applied to analyze LPC 14:0, LPC 16:0, LPC 18:0, LPC 18:1, LPC 19:0, PC 16:0/18:1, and PC 18:0 in serum samples from early LC patients (n = 15), early TC patients (n = 15), and healthy volunteers (n = 15). The results indicated that cancer patients could be effectively differentiated from healthy controls using orthogonal partial least squares discriminant analysis (OPLS-DA). In conclusion, the established TiO2@COF-based SPME-UHPLC-MS/MS method is reliable for the rapid determination of the seven PLs in serum samples, which is promising for early diagnosis of head and neck cancers.

The potential role of reprogrammed glucose metabolism: an emerging actionable codependent target in thyroid cancer

Although the incidence of thyroid cancer is increasing year by year, most patients, especially those with differentiated thyroid cancer, can usually be cured with surgery, radioactive iodine, and thyroid-stimulating hormone suppression. However, treatment options for patients with poorly differentiated thyroid cancers or radioiodine-refractory thyroid cancer have historically been limited. Altered energy metabolism is one of the hallmarks of cancer and a well-documented feature in thyroid cancer. In a hypoxic environment with extreme nutrient deficiencies resulting from uncontrolled growth, thyroid cancer cells utilize "metabolic reprogramming" to satisfy their energy demand and support malignant behaviors such as metastasis. This review summarizes past and recent advances in our understanding of the reprogramming of glucose metabolism in thyroid cancer cells, which we expect will yield new therapeutic approaches for patients with special pathological types of thyroid cancer by targeting reprogrammed glucose metabolism.

Integrated metabolic and genetic analysis reveals distinct features of human differentiated thyroid cancer

BACKGROUND

Differentiated thyroid cancer (DTC) affects thousands of lives worldwide each year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) quality of life and might be unnecessary in indolent DTC cases. On the other hand, the lack of biomarkers indicating a potential metastatic thyroid cancer imposes an additional challenge to managing and treating patients with this disease.

AIM

The presented clinical setting highlights the unmet need for a precise molecular diagnosis of DTC and potential metastatic disease, which should dictate appropriate therapy.

MATERIALS AND METHODS

In this article, we present a differential multi-omics model approach, including metabolomics, genomics, and bioinformatic models, to distinguish normal glands from thyroid tumours. Additionally, we are proposing biomarkers that could indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC.

RESULTS

Normal and tumour thyroid tissue from DTC patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumour cells. The consistency of the DTC metabolic profile allowed us to build a bioinformatic classification model capable of clearly distinguishing normal from tumor thyroid tissues, which might help diagnose thyroid cancer. Moreover, based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intra-tumour heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease.

DISCUSSION

Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy.

CONCLUSIONS

Well-designed, prospective translational clinical trials will ultimately show the value of this integrated multi-omics approach and early diagnosis of DTC and potential metastatic PTC.

Wnt/BMP Mediated Metabolic Reprogramming Preserves Multipotency of Neural Crest-Like Stem Cells

Neural crest-like stem cells resembling embryonic neural crest cells (NCs) can be derived from adult human tissues such as the epidermis. However, these cells lose their multipotency rapidly in culture limiting their expansion for clinical use. Here, we show that the multipotency of keratinocyte-derived NCs (KC-NCs) can be preserved by activating the Wnt and BMP signaling axis, promoting expression of key NC-specifier genes and ultimately enhancing their differentiation potential. We also show that transcriptional changes leading to multipotency are linked to metabolic reprogramming of KC-NCs to a highly glycolytic state. Specifically, KC-NCs treated with CHIR and BMP2 rely almost exclusively on glycolysis for their energy needs, as seen by increased lactate production, glucose uptake, and glycolytic enzyme activities. This was accompanied by mitochondrial depolarization and decreased mitochondrial ATP production. Interestingly, the glycolytic end-product lactate stabilized β-catenin and further augmented NC-gene expression. Taken together, our study shows that activation of the Wnt/BMP signaling coordinates the metabolic demands of neural crest-like stem cells governing decisions regarding multipotency and differentiation, with possible implications for regenerative medicine.

Integrated metabolic and genetic analysis reveals distinct features of primary differentiated thyroid cancer and its metastatic potential in humans

Differentiated thyroid cancer (DTC) affects thousands of lives worldwide every year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) the quality of life and might be unnecessary in indolent DTC cases. This clinical setting highlights the unmet need for a precise molecular diagnosis of DTC, which should dictate appropriate therapy. Here we propose a differential multi-omics model approach to distinguish normal gland from thyroid tumor and to indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC. Based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intratumor heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease. Specifically, normal and tumor thyroid tissues from these patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumor cells. Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy. Well-designed, prospective translational clinical trials will ultimately show the value of this targeted molecular approach.

TRANSLATIONAL RELEVANCE

In this article, we propose a new integrated metabolic, genomic, and cytopathologic methods to diagnose Differentiated Thyroid Cancer when the conventional methods failed. Moreover, we suggest metabolic and genomic markers to help predict high-risk Papillary Thyroid Cancer. Both might be important tools to avoid unnecessary surgery and/or radioiodine therapy that can worsen the quality of life of the patients more than living with an indolent Thyroid nodule.

Direct mass spectrometry analysis of biological tissue for diagnosis of thyroid cancer using wooden-tip electrospray ionization

Direct mass spectrometry (MS) analysis of human tissue at the molecular level could gain insight into biomarker discovery and disease diagnosis. Detecting metabolite profiles of tissue sample play an important role in understanding the pathological properties of disease development. Because the complex matrices in tissue samples, complicated and time-consuming sample preparation processes are usually required by conventional biological and clinical MS methods. Direct MS with ambient ionization technique is a new analytical strategy for direct sample analysis with little sample preparation, and has been proven to be a simple, rapid, and effective analytical tools for direct analysis of biological tissues. In this work, we applied a simple, low-cost, disposable wooden tip (WT) for loading tiny thyroid tissue, and then loading organic solvents to extract biomarkers under electrospray ionization (ESI) condition. Under such WT-ESI, the extract of thyroid was directly sprayed out from wooden tip to MS inlet. In this work, thyroid tissue from normal and cancer parts were analyzed by the established WT-ESI-MS, showing lipids were mainly detectable compounds in thyroid tissue. The MS data of lipids obtained from thyroid tissues were further analyzed with MS/MS experiment and multivariate variable analysis, and the biomarkers of thyroid cancer were also investigated.

Brown adipose tissue and alzheimer's disease

Alzheimer's disease (AD), the most common type of senile dementia, is a chronic neurodegenerative disease characterized by cognitive dysfunction and behavioral disability. The two histopathological hallmarks in this disease are the extraneuronal accumulation of amyloid-β (Aβ) and the intraneuronal deposition of neurofibrillary tangles (NFTs). Despite this, central and peripheral metabolic dysfunction, such as abnormal brain signaling, insulin resistance, inflammation, and impaired glucose utilization, have been indicated to be correlated with AD. There is solid evidence that the age-associated thermoregulatory deficit induces diverse metabolic changes associated with AD development. Brown adipose tissue (BAT) has been known as a thermoregulatory organ particularly vital during infancy. However, in recent years, BAT has been accepted as an endocrine organ, being involved in various functions that prevent AD, such as regulating energy metabolism, secreting hormones, improving insulin sensitivity, and increasing glucose utilization in adult humans. This review focuses on the mechanisms of BAT activation and the effect of aging on BAT production and signaling. Specifically, the evidence demonstrating the effect of BAT on pathological mechanisms influencing the development of AD, including insulin pathway, thermoregulation, and other hormonal pathways, are reviewed in this article.

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.

Evolutionary View on Lactate-Dependent Mechanisms of Maintaining Cancer Cell Stemness and Reprimitivization

The role of lactic acid (lactate) in cell metabolism has been significantly revised in recent decades. Initially, lactic acid was attributed to the role of a toxic end-product of metabolism, with its accumulation in the cell and extracellular space leading to acidosis, muscle pain, and other adverse effects. However, it has now become obvious that lactate is not only a universal fuel molecule and the main substrate for gluconeogenesis but also one of the most ancient metabolites, with a signaling function that has a wide range of regulatory activity. The Warburg effect, described 100 years ago (the intensification of glycolysis associated with high lactate production), which is characteristic of many malignant tumors, confirms the key role of lactate not only in physiological conditions but also in pathologies. The study of lactate’s role in the malignant transformation becomes more relevant in the light of the “atavistic theory of carcinogenesis,” which suggests that tumor cells return to a more primitive hereditary phenotype during microevolution. In this review, we attempt to summarize the accumulated knowledge about the functions of lactate in cell metabolism and its role in the process of carcinogenesis and to consider the possible evolutionary significance of the Warburg effect.

Characterization of Lactate Metabolism Score in Breast and Thyroid Cancers to Assist Immunotherapy via Large-Scale Transcriptomic Data Analysis

Breast cancer (BC) and thyroid cancer (TC) have the highest rate of incidence, especially in women. Previous studies have revealed that lactate provides energetic and anabolic support to cancer cells, thus serving as an important oncometabolite with both extracellular and intracellular signaling functions. However, the correlation of lactate metabolism scores with thyroid and breast cancer immune characteristics remains to be systematically analyzed. To investigate the role of lactate at the transcriptome level and its correlation with the clinical outcome of BC and TC, transcriptome data of 1,217 patients with breast cancer (BC) and 568 patients with thyroid cancer (TC) were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets with their corresponding clinical and somatic mutation data. The lactate metabolism score was calculated based on a single-sample gene set enrichment analysis (ssGSEA). The results showed that lactate metabolism-related genes and lactate metabolism scores was significantly associated with the survival of patients with BRCA and THCA. Notably, the lactate metabolism scores were strongly correlated with human leukocyte antigen (HLA) expression, tumor-infiltrating lymphocyte (TIL) infiltration, and interferon (IFN) response in BC and TC. Furthermore, the lactate metabolism score was an independent prognostic factor and could serve as a reliable predictor of overall survival, clinical characteristics, and immune cell infiltration, with the potential to be applied in immunotherapy or precise chemotherapy of BC and TC.

Rapid diagnosis of papillary thyroid carcinoma with machine learning and probe electrospray ionization mass spectrometry

Frozen section examination could provide pathological diagnosis for surgery of thyroid nodules, which is time-consuming, skill- and experience-dependent. This study developed a rapid classification method for thyroid nodules and machine learning. Total 69 tissues were collected including 43 nodules and 26 nodule-adjacent tissues. Intraoperative frozen section was first performed to give accurate diagnosis, and the rest frozen specimen were pretreated for probe electrospray ionization mass measurement. By multivariate analysis of mass scan data, a series compounds were found downregulated in the extraction solution of papillary thyroid carcinoma (PTC), but some were found upregulated by mass spectrometry imaging. m/z 758.5713 ([PC[34:2] + H]⁺ ), m/z 772.5845 ([PC[32:0] + K]⁺ ), and m/z 786.6037 ([PC[36:2] + H]⁺ ) were firstly identified as potential biomarkers for nodular goiter (NG). Machine learning was employed by means of support vector machine (SVM) and random forest (RF) algorithms. For classification of PTC from NG, SVM and RF algorithms exhibited the same performance and the concordance was 94.2% and 94.4% between prediction and pathological diagnosis with positive and negative mass dataset, respectively. For the classification of PTC from PTC adjacent tissues, SVM was better than RF and the concordance was 93.8% and 83.3% with positive and negative mass dataset, respectively. With the identified compounds as training features, the sensitivity and specificity are 87.5% and 88.9% for the test set. The developed method could also correctly predict the malignancy of one medullary thyroid carcinoma and one adenomatous goiter (benign). The diagnosis time is about 10 min for one specimen, and it is very promising for the intraoperative diagnosis of papillary thyroid carcinoma.

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Thyroid Cancer Diagnostics Related to Occupational and Environmental Risk Factors: An Integrated Risk Assessment Approach

There are still many questions remaining about the etiopathogenesis of thyroid cancer, the most common type of endocrine neoplasia. Numerous occupational and environmental exposures have been shown to represent important risk factors that increase its incidence. Updated information about thyroid cancer diagnostics related to occupational and environmental risk factors is reviewed here, considering an integrated risk assessment approach; new data concerning thyroid cancer etiology and pathogenesis mechanisms, diagnostic biomarkers and methodologies, and risk factors involved in its pathogenesis are presented. A special emphasis is dedicated to specific occupational risk factors and to the association between environmental risk agents and thyroid cancer development. The occupational environment is taken into consideration, i.e., the current workplace and previous jobs, as well as data regarding risk factors, e.g., age, gender, family history, lifestyle, use of chemicals, or radiation exposure outside the workplace. Finally, an integrative approach is presented, underlying the need for an accurate Risk Assessment Matrix based on a systematic questionnaire. We propose a complex experimental design that contains different inclusion and exclusion criteria for patient groups, detailed working protocols for achieving coherent and sustainable, well-defined research stages from sample collection to the identification of biomarkers, with correlations between specific oncometabolites integrated into the Risk Assessment Matrix.

Systems biomarkers for papillary thyroid cancer prognosis and treatment through multi-omics networks

The identification of biomolecules associated with papillary thyroid cancer (PTC) has upmost importance for the elucidation of the disease mechanism and the development of effective diagnostic and treatment strategies. Despite particular findings in this regard, a holistic analysis encompassing molecular data from different biological levels has been lacking. In the present study, a meta-analysis of four transcriptome datasets was performed to identify gene expression signatures in PTC, and reporter molecules were determined by mapping gene expression data onto three major cellular networks, i.e., transcriptional regulatory, protein-protein interaction, and metabolic networks. We identified 282 common genes that were differentially expressed in all PTC datasets. In addition, six proteins (FYN, JUN, LYN, PML, SIN3A, and RARA), two Erb-B2 receptors (ERBB2 and ERBB4), two cyclin-dependent receptors (CDK1 and CDK2), and three histone deacetylase receptors (HDAC1, HDAC2, and HDAC3) came into prominence as proteomic signatures in addition to several metabolites including lactaldehyde and proline at the metabolome level. Significant associations with calcium and MAPK signaling pathways and transcriptional and post-transcriptional activities of 12 TFs and 110 miRNAs were also observed at the regulatory level. Among them, six miRNAs (miR-30b-3p, miR-15b-5p, let-7a-5p, miR-130b-3p, miR-424-5p, and miR-193b-3p) were associated with PTC for the first time in the literature, and the expression levels of miR-30b-3p, miR-15b-5p, and let-7a-5p were found to be predictive of disease prognosis. Drug repositioning and molecular docking simulations revealed that 5 drugs (prochlorperazine, meclizine, rottlerin, cephaeline, and tretinoin) may be useful in the treatment of PTC. Consequently, we report here biomolecule candidates that may be considered as prognostic biomarkers or potential therapeutic targets for further experimental and clinical trials for PTC.

Unraveling Autocrine Signaling Pathways through Metabolic Fingerprinting in Serous Ovarian Cancer Cells

Focusing on defining metabolite-based inter-tumoral heterogeneity in ovarian cancer, we investigated the metabolic diversity of a panel of high-grade serous ovarian carcinoma (HGSOC) cell-lines using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis established the heterogeneity of the HGSOC cells by clustering them into five distinct metabolic groups compared to the fallopian tube epithelial cell line control. An overall increase in the metabolites associated with aerobic glycolysis and phospholipid metabolism were observed in the majority of the cancer cells. A preponderant increase in the levels of metabolites involved in trans-sulphuration and glutathione synthesis was also observed. More significantly, subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, γ-aminobutyrate, or glutamate. Additionally, 5-hydroxytryptamin synthesis inhibitor as well as antagonists of γ-aminobutyrate and glutamate receptors prohibited the proliferation of HGSOC cells, pointing to their potential roles as oncometabolites and ligands for receptor-mediated autocrine signaling in cancer cells. Consistent with this role, 5-Hydroxytryptamine synthesis inhibitor as well as receptor antagonists of γ-aminobutyrate and Glutamate-receptors inhibited the proliferation of HGSOC cells. These antagonists also inhibited the three-dimensional spheroid growth of TYKNU cells, a representative HGSOC cell-line. These results identify 5-HT, GABA, and Glutamate as putative oncometabolites in ovarian cancer metabolic sub-type and point to them as therapeutic targets in a metabolomic fingerprinting-based therapeutic strategy.

Role of GATA3 in tumor diagnosis: A review

GATA binding protein 3 (GATA3) belongs to a family of transcription factors comprising six members. These proteins identify G-A-T-A containing sequences in the target gene and bind to DNA target via two zinc-finger domains. The aim of this study was to evaluate the role of GATA3 in the diagnosis of tumors and its value as a prognostic marker. To perform this review, a comprehensive search was conducted through PubMed, Embase, Scopus, Cochrane and Google Scholar databases from 1985 to 2020. Articles were considered thoroughly by independent reviewers and data were extracted in predefined forms. Final synthesis was conducted by using appropriate data from included articles in each topic. Studies have shown that GATA3 has a critical role in the development of epithelial structures in both embryonic and adult tissues. The majority of studies regarding GATA3 expression in tumor evaluation focused on breast and urothelial neoplasms, whether primary or metastatic. Its sensitivity in these neoplasms has been reported to be high and made this marker more valuable than other available immunohistochemistry markers. However, GATA3 expression was not restricted to these tumors. Studies have shown that GATA3 immunostaining could be a useful tool in various tumors in kidney, salivary gland, endocrine system, hematopoietic system, and skin. GATA3 can also be used as a useful prognostic tool. Although GATA3 is a multi-specific immunohistochemical stain, it is a valuable marker in the panel for confirming many epithelial or mesenchymal neoplasms as both a diagnostic and prognostic tool.

Metabolic Rewiring and the Characterization of Oncometabolites

Simple Summary

Oncometabolites are produced by cancer cells and assist the cancer to proliferate and progress. Oncometabolites occur as a result of mutated enzymes in the tumor tissue or due to hypoxia. These processes result in either the abnormal buildup of a normal metabolite or the accumulation of an unusual metabolite. Definition of the metabolic changes that occur due to these processes has been accomplished using metabolomics, which mainly uses mass spectrometry platforms to define the content of small metabolites that occur in cells, tissues, organs and organisms. The four classical oncometabolites are fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate, which operate by inhibiting 2-oxoglutarate-dependent enzyme reactions that principally regulate gene expression and response to hypoxia. Metabolomics has also revealed several putative oncometabolites that include lactate, kynurenine, methylglyoxal, sarcosine, glycine, hypotaurine and (2R,3S)-dihydroxybutanoate. Metabolomics will continue to be critical for understanding the metabolic rewiring involving oncometabolite production that underpins many cancer phenotypes.

Abstract

The study of low-molecular-weight metabolites that exist in cells and organisms is known as metabolomics and is often conducted using mass spectrometry laboratory platforms. Definition of oncometabolites in the context of the metabolic phenotype of cancer cells has been accomplished through metabolomics. Oncometabolites result from mutations in cancer cell genes or from hypoxia-driven enzyme promiscuity. As a result, normal metabolites accumulate in cancer cells to unusually high concentrations or, alternatively, unusual metabolites are produced. The typical oncometabolites fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate inhibit 2-oxoglutarate-dependent dioxygenases, such as histone demethylases and HIF prolyl-4-hydroxylases, together with DNA cytosine demethylases. As a result of the cancer cell acquiring this new metabolic phenotype, major changes in gene transcription occur and the modification of the epigenetic landscape of the cell promotes proliferation and progression of cancers. Stabilization of HIF1α through inhibition of HIF prolyl-4-hydroxylases by oncometabolites such as fumarate and succinate leads to a pseudohypoxic state that promotes inflammation, angiogenesis and metastasis. Metabolomics has additionally been employed to define the metabolic phenotype of cancer cells and patient biofluids in the search for cancer biomarkers. These efforts have led to the uncovering of the putative oncometabolites sarcosine, glycine, lactate, kynurenine, methylglyoxal, hypotaurine and (2R,3S)-dihydroxybutanoate, for which further research is required.

Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy

The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.

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.

L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for Precision Medicine

Biomarker discovery and implementation are at the forefront of the precision medicine movement. Modern advances in the field of metabolomics afford the opportunity to readily identify new metabolite biomarkers across a wide array of disciplines. Many of the metabolites are derived from or directly reflective of mitochondrial metabolism. L-carnitine and acylcarnitines are established mitochondrial biomarkers used to screen neonates for a series of genetic disorders affecting fatty acid oxidation, known as the inborn errors of metabolism. However, L-carnitine and acylcarnitines are not routinely measured beyond this screening, despite the growing evidence that shows their clinical utility outside of these disorders. Measurements of the carnitine pool have been used to identify the disease and prognosticate mortality among disorders such as diabetes, sepsis, cancer, and heart failure, as well as identify subjects experiencing adverse drug reactions from various medications like valproic acid, clofazimine, zidovudine, cisplatin, propofol, and cyclosporine. The aim of this review is to collect and interpret the literature evidence supporting the clinical biomarker application of L-carnitine and acylcarnitines. Further study of these metabolites could ultimately provide mechanistic insights that guide therapeutic decisions and elucidate new pharmacologic targets.

Immunohistochemical basigin expression level in thyroid cancer tissues

BACKGROUND

Thyroid cancer (TC) is the most common endocrine malignancy; basigin (also known as BSG) plays a crucial role in tumor cell invasion, metastasis, and angiogenesis. This study was designed to identify the change of BSG expression in TC and its possible potential mechanism.

METHODS

The BSG expression levels in TC were demonstrated using data collected from in-house immunohistochemical (IHC), RNA-sequencing (RNA-seq), microarrays, and literatures. Integrated analysis was performed to determined BSG expression levels in TC comprehensively. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the integration of BSG co-expressed genes and differentially expressed genes (DEGs) in TC tissues to explore the potential mechanisms of BSG in TC.

RESULTS

The protein expression level of BSG was significantly higher in TC cases based on the IHC experiments. In addition, the combined SMD for BSG expression was 0.39 (p < 0.0001), the diagnostic odds ratio was 3.69, and the AUC of the sROC curve was 0.6986 using 1182 TC cases and 437 non-cancerous cases from 17 independent datasets. Furthermore, BSG co-expressed genes tended to be enriched in gene terms of the extracellular matrix (ECM), cell adhesion, and cell-cell interactions. The expression levels of nine hub BSG co-expressed genes were markedly upregulated in TC cases.

CONCLUSION

BSG expression levels were closely correlated with the progression of TC and may affect the signals of the ECM, cell adhesion, and cell-cell interactions.

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.

The Blood Biomarkers of Thyroid Cancer

Introduction

With the gradual increase in the incidence of thyroid cancer, people’s attention to thyroid cancer has also gradually increased. Although the prognosis of thyroid cancer is rather mild compared to other cancers, it will still bring a heavy psychological burden on people who have been diagnosed. At present, the diagnosis of thyroid cancer mainly depends on ultrasound and percutaneous fine needle aspiration (pFNA). Due to the unsatisfactory accuracy of the diagnosis methods we use now, there are still some thyroid nodules that cannot be clearly diagnosed before surgery.

Methods

In this article, we have searched for relevant research on blood markers of thyroid cancer in the past five years and categoried them into four groups.

Discussion

Though we have not found a biomarker which can diagnose thyroid cancer both sensitively and specifically, we do found many substances that are related to it, and have the potential to recognize it and help the diagnosis. And perhaps combined models can do it better.

Simultaneous Analysis of Fatty Alcohols, Fatty Aldehydes, and Sterols in Thyroid Tissues by Electrospray Ionization-Ion Mobility-Mass Spectrometry Based on Charge Derivatization

In this work, we developed a rapid and high-sensitivity method for simultaneous analyses of fatty alcohols, fatty aldehydes, and sterols by combining the optimized derivatization reaction with electrospray ionization-ion mobility-mass spectrometry (ESI-IM-MS). Pyridine and thionyl chloride were used as derivatization reagents as they were easily removed after the derivatization reaction and could generate permanently charged tags on different functional groups including hydroxyls and aldehydes. Through this one-step derivatization reaction, the sensitivity of detection for fatty alcohols, fatty aldehydes, and sterols was significantly increased. Moreover, the introduction of ion mobility spectrometry (IMS), offering an additional resolution power, ensured more sensitive and accurate detection of derivative products without increasing analytical time. Being connected with high-performance liquid chromatography, more than 15 kinds of compounds were analyzed within 4 min. Relative quantification using peak intensity ratios between d₀-/d₅-labeled ions were subsequently applied for analyzing these 15 kinds of compounds in human thyroid carcinoma and para-carcinoma tissues. The results showed significant differences in content of some analytes between these two kinds of tissues (p < 0.05). The correlations between most of the analytes in thyroid carcinoma tissues are better than the correlations in para-carcinoma tissues.

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.

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.

Nutraceuticals in Thyroidology: A Review of in Vitro, and in Vivo Animal Studies

Nutraceuticals are defined as a food, or parts of a food, that provide medical or health benefits, including the prevention of different pathological conditions, and thyroid diseases, or the treatment of them. Nutraceuticals have a place in complementary medicines, being positioned in an area among food, food supplements, and pharmaceuticals. The market of certain nutraceuticals such as thyroid supplements has been growing in the last years. In addition, iodine is a fundamental micronutrient for thyroid function, but also other dietary components can have a key role in clinical thyroidology. Here, we have summarized the in vitro, and in vivo animal studies present in literature, focusing on the commonest nutraceuticals generally encountered in the clinical practice (such as carnitine, flavonoids, melatonin, omega-3, resveratrol, selenium, vitamins, zinc, and inositol), highlighting conflicting results. These experimental studies are expected to improve clinicians’ knowledge about the main supplements being used, in order to clarify the potential risks or side effects and support patients in their use.

Promoter Methylation of Four Tumor Suppressor Genes in Human Papillary Thyroid Carcinoma

BACKGROUND & OBJECTIVE

Papillary thyroid cancer (PTC) is considered to be the most common type of thyroid malignancies. Epigenetic alteration, in which the chromatin conformation and gene expression change without changing the sequence of DNA, can occur in some tumor suppressor genes and oncogenes. Methylation is the most common type of epigenetic alterations that can be an excellent indicator of PTC invasive behavior.

METHODS

In this research, we determined the promoter methylation status of four tumor suppressor genes (SLC5A8, RASSF1, MGMT, and DNMT1) and compared the results of 55 PTC cases with 40 goiter patients. For methylation, we used the methylation-sensitive high resolution melting (MS-HRM) assay technique. The resulting graphs of each run were compared with those of 0%, 50%, and 100% methylated controls.

RESULTS

Our data showed that the promoter methylation of SLC5A8, Ras association domain family member 1(RASSF1), and MGMT were significantly different between PTC tissue and goiter with P-value less than 0.05. The most significant differences were observed in RASSF1; 77.2% of hyper-methylated PTC patients versus 15.6% hyper-methylated goiter samples (P<0.001).

CONCLUSION

RASSF1 promoter methylation can be a PTC genetic marker. RASSF1 promoter methylation is under the impact of the methyltransferase genes (DNMT1 and MGMT), protein expression, and promoter methylation.

LC/MS-Based Polar Metabolite Profiling Identified Unique Biomarker Signatures for Cervical Cancer and Cervical Intraepithelial Neoplasia Using Global and Targeted Metabolomics

Cervical cancer remains one of the most prevalent cancers among females worldwide. Therefore, it is important to discover new biomarkers for early diagnosis of cervical intraepithelial neoplasia (CIN) and cervical cancer, preferably non-invasive ones. In the present study, we aimed to identify unique metabolic signatures for CINs and cervical cancers using global and targeted metabolomic profiling. Plasma samples (69 normal, 55 CIN1, 42 CIN2/3, and 60 cervical cancer) were examined by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with multivariate statistical analysis. Metabolic pathways were analyzed using the integrated web-based tool MetaboAnalyst. A multivariate logistic regression analysis was conducted to evaluate the combined association of metabolites and human papillomavirus (HPV) status with the risk of cervical carcinogenesis. A total of 28 metabolites exhibiting discriminating levels among normal, CIN, and cervical cancer patients (Kruskal–Wallis test p < 0.05) were identified in the global profiling analysis. The pathway analysis showed significantly altered alanine, aspartate, and glutamate metabolic pathways (FDR p-value < 0.05) in both the discovery and validation phases. Seven metabolites (AMP, aspartate, glutamate, hypoxanthine, lactate, proline, and pyroglutamate) were discriminated between CINs and cervical cancer versus normal (area under the curve (AUC) value > 0.8). The levels of these metabolites were significantly high in patients versus normal (p < 0.0001) and were associated with increased risk of developing CIN2/3 and cervical cancer. Additionally, elevated levels of the seven metabolites combined with positive HPV status were correlated with substantial risk of cancer progression. These results demonstrated that metabolomics profiling is capable of distinguishing CINs and cervical cancers from normal and highlighted potential biomarkers for the early detection of cervical carcinogenesis.