A distinct serum metabolic signature of distant metastatic papillary thyroid carcinoma

Exosome-mediated Delivery of miR-519e-5p Promotes Malignant Tumor Phenotype and CD8+ T-Cell Exhaustion in Metastatic PTC

CONTEXT

Distant metastases are the primary cause of therapy failure and mortality in patients with papillary thyroid carcinomas (PTCs). However, the underlying mechanism responsible for the initiation of tumor cell dissemination and metastasis in PTCs has rarely been investigated.

OBJECTIVE

The aim of this study was to investigate effects and underlying molecular mechanisms of circulating exosomal microRNAs (miRNAs) in distant metastatic PTCs.

METHODS

The most relevant circulating exosomal miRNA to distant metastatic PTCs were verified between distant metastatic PTCs and nondistant metastatic PTCs by miRNA microarray, quantitative real-time polymerase chain reaction (qRT-PCR) assays and receiver operating characteristic (ROC) curves. The parental and recipient cells of that circulating exosomal miRNA were then explored. In vitro and in vivo experiments were further performed to elucidate the function and potential mechanisms of circulating exosomal miRNAs that contribute to the development of distant metastases.

RESULTS

We determined that PTC-derived exosomal miR-519e-5p was significantly upregulated in the circulatory system in distant metastatic PTCs. Further tests demonstrated that PTC cells can acquire a more malignant phenotype via hnRNPA2B1-mediated sorting of tumor suppressor miR-519e-5p into exosomes to activate Wnt signaling pathway via upregulating PLAGL2. Furthermore, miR-519e-5p included in PTC-derived exosomes can be transferred to recipient CD8+ T cells and aid in tumor immune escape in distant organs through inhibiting Notch signaling pathway by downregulating NOTCH2.

CONCLUSION

Our findings highlight the dual role of PTC-derived exosomal miR-519e-5p in distant metastasis, which may improve our understanding of exosome-mediated distant metastatic mechanisms.

Microbiota Implications in Endocrine-Related Diseases: From Development to Novel Therapeutic Approaches

This comprehensive review article delves into the critical role of the human microbiota in the development and management of endocrine-related diseases. We explore the complex interactions between the microbiota and the endocrine system, emphasizing the implications of microbiota dysbiosis for the onset and progression of various endocrine disorders. The review aims to synthesize current knowledge, highlighting recent advancements and the potential of novel therapeutic approaches targeting microbiota-endocrine interactions. Key topics include the impact of microbiota on hormone regulation, its role in endocrine pathologies, and the promising avenues of microbiota modulation through diet, probiotics, prebiotics, and fecal microbiota transplantation. We underscore the importance of this research in advancing personalized medicine, offering insights for more tailored and effective treatments for endocrine-related diseases.

Causal analysis of the gut microbiota in differentiated thyroid carcinoma: a two-sample Mendelian randomization study

Objective: Numerous studies have highlighted an association between the gut microbiota (GM) and thyroid tumors. Employing Mendelian randomization methodology, we seek to elucidate the causal link between the gut microbiota and thyroid neoplasms. Methods: We procured data from the Mibiogen database encompassing 211 distinct gut microbiota taxa, alongside extensive genome-wide association studies (GWAS) summary data for differentiated thyroid carcinoma (DTC). Our principal analytical approach involved the application of the Inverse-Variance Weighted method (IVW) within the framework of Mendelian randomization. Simultaneously, we conducted sensitivity analyses to assess result heterogeneity, horizontal pleiotropy, and outcome stability. Results: IVW analysis revealed a dual role of the GM in thyroid carcinoma. The phylum Actinobacteria (OR, 0.249 [95% CI, 0.121-0.515]; p < 0.001) was associated with a decreased risk of DTC. Conversely, the genus Ruminiclostridium9 (OR, 11.276 [95% CI, 4.406-28.860]; p < 0.001), class Mollicutes (OR, 5.902 [95% CI, 1.768-19.699]; p = 0.004), genus RuminococcaceaeUCG004 (OR, 3.831 [95% CI, 1.516-9.683]; p = 0.005), genus Paraprevotella (OR, 3.536 [95% CI, 1.330-9.401]; p = 0.011), and phylum Tenericutes (OR, 5.902 [95% CI, 1.768-19.699]; p = 0.004) were associated with an increased risk of DTC. Conclusion: Our findings underscore that the presence of genus Ruminiclostridium9, class Mollicutes, genus RuminococcaceaeUCG004, genus Paraprevotella, and phylum Tenericutes is associated with an elevated risk of DTC, whereas the presence of the phylum Actinobacteria is linked to a decreased risk. These discoveries enhance our comprehension of the relationship between the GM and DTC.

Interactive Association Between Gut Microbiota and Thyroid Cancer

CONTEXT

The association between the gut microbiota and thyroid cancer remains controversial.

OBJECTIVE

We aimed to systematically investigate the interactive causal relationships between the abundance and metabolism pathways of gut microbiota and thyroid cancer.

METHODS

We leveraged genome-wide association studies for the abundance of 211 microbiota taxa from the MiBioGen study (N = 18 340), 205 microbiota metabolism pathways from the Dutch Microbiome Project (N = 7738), and thyroid cancer from the Global Biobank Meta-analysis Initiative (N cases = 6699 and N participants = 1 620 354). We performed a bidirectional Mendelian randomization (MR) to investigate the causality from microbiota taxa and metabolism pathways to thyroid cancer and vice versa. We performed a systematic review of previous observational studies and compared MR results with observational findings.

RESULTS

Eight taxa and 12 metabolism pathways had causal effects on thyroid cancer, where RuminococcaceaeUCG004 genus (P = .001), Streptococcaceae family (P = .016), Olsenella genus (P = .029), ketogluconate metabolism pathway (P = .003), pentose phosphate pathway (P = .016), and L-arginine degradation II in the AST pathway (P = .0007) were supported by sensitivity analyses. Conversely, thyroid cancer had causal effects on 3 taxa and 2 metabolism pathways, where the Holdemanella genus (P = .015) was supported by sensitivity analyses. The Proteobacteria phylum, Streptococcaceae family, Ruminococcus2 genus, and Holdemanella genus were significantly associated with thyroid cancer in both the systematic review and MR, whereas the other 121 significant taxa in observational results were not supported by MR.

DISCUSSIONS

These findings implicated the potential role of host-microbiota crosstalk in thyroid cancer, while the discrepancy among observational studies calls for further investigations.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

Construction and validation of a metabolism-related prognostic model for thyroid cancer

Metabolic reprogramming is a common pathological process of cancer. Expression of metabolism-related genes differs in thyroid cancer (TC) patients with different prognoses. This work committed to constructing a prognostic model for TC through identifying metabolism-related signatures. Expression profiles of mRNAs and clinical data of TC, were acquired from The Cancer Genome Atlas. Differential analysis was performed on mRNA expression profiles. The obtained differentially expressed genes (DEGs) were overlapped with metabolism-related genes from MSigDB database to acquire metabolism-related DEGs. Cox regression and Least Absolute Shrinkage and Selection Operator analyses were performed to ascertain feature genes and to build a prognostic model for TC. The model was evaluated comprehensively through survival curve, time-dependent receiver operating characteristic (ROC) curve, gene set enrichment analysis (GSEA), and Cox regression analyses combining varying clinical information. 7 key genes related to metabolism, including AWAT2, GGT6, ENTPD1, PAPSS2, CYP26A, ACY3 and PLA2G10, were identified, based on which a prognostic model was constructed. The survival analysis indicated that high-risk group presented shorter survival time than low-risk group. ROC curve results exhibited that AUC values of 3-year and 5-year survival of TC patients were both >0.70. Besides, GSEA on high/low-risk groups revealed that DEGs were mainly gathered in biological functions and signaling pathways linked with keratan sulfate catabolism and triglyceride catabolism. Combined with clinical information, Cox regression analyses unveiled that the 7-gene prognostic model can be an independent predictor. In conclusion, this model can effectively predict prognoses of TC patients, and also offer guidance for clinical treatment of TC.

The potential value of LC-MS non-targeted metabonomics in the diagnosis of follicular thyroid carcinoma

Background

To explore the metabolic differences of follicular thyroid carcinoma (FTC) by metabonomics, to find potential biomarkers for the diagnosis of FTC, and to explore the pathogenesis and diagnosis and treatment strategies of FTC.

Method

The metabonomics of 15 patients with FTC and 15 patients with follicular thyroid nodules(FTN) treated in Henan Cancer Hospital were analyzed by liquid chromatography-mass spectrometry (LC-MS).

Results

The analysis showed that the metabolite profiles of FTC tissues could be well distinguished from those of control tissues, and 6 kinds of lipids were identified respectively, including lysophosphatidic acid(LysoPA) [LysoPA(0:0/18:0),LysoPA(0:0/18:2(9Z,12Z)],LysoPA[20:4(8Z,11Z,14Z,17Z)/0:0)]; phosphatidic acid(PA) [PA(20:3(8Z,11Z,14Z)/0:0),PA(20:4(5Z,8Z,11Z,14Z)/0:0),PA(20:5(5Z,8Z,11Z,14Z,17Z)/0:0)]; lysophosphatidylcholine(LPC) [LPC(18:1),LPC(16:0),LPC[16:1(9Z)/0:0],LPC(17:0),LPC[22:4(7Z,10Z,13Z,16Z),LPC(20:2(11Z,14Z); phosphatidylcholine(PC)(PC(14:0/0:0),PC(16:0/0:0); sphingomyelin(SM) (d18:0/12:0); fatty acid(FA)(18:1(OH3)]. There are 2 kinds of amino acids, including L-glutamate,L-glutamine.There are 3 other metabolites, including retinol,flavin adenine dinucleotide,androsterone glucuronide.Lipid metabolites are the main metabolites in these metabolites.The metabolic pathways related to FTC were analyzed by KEGG and HMDB, and 9 metabolic pathways were found, including 4 amino acid related metabolic pathways, 1 lipid metabolic pathways and 4 other related pathways.

Conclusion

There are significant differences in many metabonomic characteristics between FTC and FTN, suggesting that these metabolites can be used as potential biomarkers. Further study found that LysoPA and its analogues can be used as biomarkers in the early diagnosis of FTC.It may be related to the abnormal metabolism of phospholipase D (PLD), the key enzyme of LysoPA synthesis caused by RAS pathway. At the same time, it was found that the metabolic pathway of amino acids and lipids was the main metabolic pathway of FTC. The abnormality of LysoPA may be the cause of follicular tumor carcinogenesis caused by lipid metabolic pathway.

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.

Carbon Ion Radiotherapy Induce Metabolic Inhibition After Functional Imaging-Guided Simultaneous Integrated Boost for Prostate Cancer

Purpose

As local recurrence remains a challenge and the advantages of the simultaneous integrated boost (SIB) technique have been validated in photon radiotherapy, we applied the SIB technique to CIRT. The aim was to investigate the metabolomic changes of the CIRT with concurrent androgen deprivation therapy (ADT) in localized prostate cancer (PCa) and the unique metabolic effect of the SIB technique.

Material and Methods

This study enrolled 24 pathologically confirmed PCa patients. All patients went through CIRT with concurrent ADT. The gross target volume (GTV) boost was defined as positive lesions on both 68Ga-PSMA PET/CT and mpMRI images. Urine samples collected before and after CIRT were analyzed by the Q-TOF UPLC-MS/MS system. R platform and MetDNA were used for peak detection and identification. Statistical analysis and metabolic pathway analysis were performed on Metaboanalyst.

Results

The metabolite profiles were significantly altered after CIRT. The most significantly altered metabolic pathway is PSMA participated alanine, aspartate and glutamate metabolism. Metabolites in this pathway showed a trend to be better suppressed in the SIB group. A total of 11 identified metabolites were significantly discriminative between two groups and all of them were better down-regulated in the SIB group. Meanwhile, among these metabolites, three metabolites in DNA damage and repair related purine metabolism were down-regulated to a greater extent in the SIB group.

Conclusion

Metabolic dysfunction was one of the typical characteristics of PCa. CIRT with ADT showed a powerful inhibition of PCa metabolism, especially in PSMA participated metabolic pathway. The SIB CIRT showed even better performance on down-regulation of most metabolism than uniform-dose-distribution CIRT. Meanwhile, the SIB CIRT also showed its unique superiority to inhibit purine metabolism. PSMA PET/CT guided SIB CIRT showed its potentials to further benefit PCa patients.

Interaction of Gut Microbiota with Endocrine Homeostasis and Thyroid Cancer

The gut microbiota plays a crucial role in healthy individuals as well as in patients with thyroid diseases, including thyroid cancer. Although the prognosis of differentiated thyroid cancer is predictable, that of some poorly differentiated, medullary, and anaplastic thyroid cancers remains unpromising. As the interaction between the gut microbiota and thyroid cancer has been gradually revealed in recent years, the thyroid gland, a crucial endocrine organ, is shown to have a complex connection with the body's metabolism and is involved in inflammation, autoimmunity, or cancer progression. Dysbiosis of the gut microbiota and its metabolites can influence changes in hormone levels and susceptibility to thyroid cancer through multiple pathways. In this review, we focus on the interactions of the gut microbiota with thyroid function diseases and thyroid cancer. In addition, we also discuss some potential new strategies for the prevention and treatment of thyroid disease and thyroid cancer. Our aim is to provide some possible clinical applications of gut microbiota markers for early diagnosis, treatment, and postoperative management of thyroid cancer. These findings were used to establish a better multi-disciplinary treatment and prevention management strategy and to individualize the treatment of patients in relation to their gut microbiota composition and pathological characteristics.

Ubiquitin conjugating enzyme E2 C (UBE2C) may play a dual role involved in the progression of thyroid carcinoma

The present study aimed to explore the role of ubiquitin-conjugating enzyme E2 C (UBE2C) in the progress of thyroid carcinoma (THCA). We firstly explored the prognostic impact and expression level of UBE2C in THCA. Then, we performed the UBE2C knockdown and evaluated the effects on the proliferation, cell cycle distribution, apoptosis, migration, and invasion of THCA cells, as well as resistance to sorafenib. Finally, we predicted the possible pathways and explored the correlation between UBE2C with immune infiltrates. The results showed that high expression of UBE2C independently predicted a shorter disease-free survival time of THCA patients. And UBE2C also presented a better prognostic performance on the survival probability of patients. Expression analysis showed that UBE2C was statistically upregulated in THCA tissue compared with normal tissue. After UBE2C knockdown, the proliferation of THCA cells was inhibited and apoptosis was increased. These results indicated that UBE2C acted as an oncogene in THCA. However, the migration and invasion of THCA cells with UBE2C knockdown were enhanced, and the expressions of migration-related proteins were upregulated. In addition, UBE2C knockdown increased the resistance of THCA cells to sorafenib. These results implied the potential of UBE2C as a suppressor gene in THCA. The pathway analysis further predicted that metabolism-related pathways were activated in the UBE2C low expression class, and cell growth and immune-related pathways were focused on the UBE2C high expression class. Finally, we observed a significant positive relationship between UBE2C and several immune infiltrates in THCA. It followed that UBE2C high expression might play a vital role in THCA to some extent. This study revealed that UBE2C participated in the progression of THCA and may play the dual role of both oncogene and tumor suppressor gene. The detailed mechanism needed to be further investigated.

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.

Gut microbiota changes and its potential relations with thyroid carcinoma

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Thyroid cancer patients have reduced richness and diversity of gut microbiota.

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A predictive model of 10 genera could distinguish thyroid cancer patients from healthy controls.

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The loss of the short-chain fatty acid-producing bacteria may promote thyroid carcinoma.

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The functional changes that occur in thyroid cancer patients affect the processing of genetic information.

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A four-genus microbial signature may be able to distinguish thyroid carcinoma patients with metastatic lymphadenopathy from those without metastatic lymphadenopathy.

Introduction

Emerging evidence suggests that the essence of life is the ecological balance of the neural, endocrine, metabolic, microbial, and immune systems. Gut microbiota have been implicated as an important factor affecting thyroid homeostasis.

Objectives

This study aims to explore the relationship between gut microbiota and the development of thyroid carcinoma.

Methods

Stool samples were collected from 90 thyroid carcinoma patients (TCs) and 90 healthy controls (HCs). Microbiota were analyzed using 16S ribosomal RNA gene sequencing. A cross-sectional study of an exploratory cohort of 60 TCs and 60 HCs was conducted. The gut microbiota signature of TCs was established by LEfSe, stepwise logistic regression, lasso regression, and random forest model analysis. An independent cohort of 30 TCs and 30 HCs was used to validate the findings. Functional prediction was achieved using Tax4Fun and PICRUSt2. TC patients were subsequently divided into subgroups to analyze the relationship between microbiota and metastatic lymphadenopathy.

Results

In the exploratory cohorts, TCs had reduced richness and diversity of gut microbiota compared to HCs. No significant difference was found between TCs and HCs on the phylum level, though 70% of TCs had increased levels of Proteobacteria-types based on dominant microbiota typing. A prediction model of 10 genera generated with LEfSe analysis and lasso regression distinguished TCs from HCs with areas under the curves of 0.809 and 0.746 in the exploration and validation cohorts respectively. Functional prediction suggested that the microbial changes observed in TCs resulted in a decline in aminoacyl-tRNA biosynthesis, homologous recombination, mismatch repair, DNA replication, and nucleotide excision repair. A four-genus microbial signature was able to distinguish TC patients with metastatic lymphadenopathy from those without metastatic lymphadenopathy.

Conclusion

Our study shows that thyroid carcinoma patients demonstrate significant changes in gut microbiota, which will help delineate the relationship between gut microbiota and TC pathogenesis.

The relationships between the gut microbiota and its metabolites with thyroid diseases

Emerging studies have provided a preliminary understanding of the thyroid-gut axis, indicating that intestinal microbiota and its metabolites may act directly or indirectly on the thyroid by influencing intestinal microelements uptake, iodothyronine conversion and storage, and immune regulation, providing new insights into the pathogenesis of thyroid disorders and clinical management strategies. However, the research on gut microbiota and thyroid has only presented the tip of the iceberg. More robust clinical data and basic experiments are still required to elucidate the specific relationships and mechanisms in the future. Here we will characterize the associations between the microbiota and thyroid diseases to evaluate their potential implications in the pathophysiology and open up scientific avenues for future precision studies of the thyroid-gut axis.

Alterations of Gut Microbiome and Metabolite Profiles Associated With Anabatic Lipid Dysmetabolism in Thyroid Cancer

BACKGROUND

Currently, the high morbidity of individuals with thyroid cancer (TC) is an increasing health care burden worldwide. The aim of our study was to investigate the relationship among the gut microbiota community, metabolites, and the development of differentiated thyroid cancer.

METHODS

16S rRNA gene sequencing and an integrated LC-MS-based metabolomics approach were performed to obtain the components and characteristics of fecal microbiota and metabolites from 50 patients with TC and 58 healthy controls (HCs).

RESULTS

The diversity and richness of the gut microbiota in the TC patients were markedly decreased. The composition of the gut microbiota was significantly altered, and the Bacteroides enterotype was the dominant enterotype in TC patients. Additionally, the diagnostic validity of the combined model (three genera and eight metabolites) and the metabolite model (six metabolites) were markedly higher than that of the microbial model (seven genera) for distinguishing TC patients from HCs. LEfSe analysis demonstrated that genera (g_Christensenellaceae_R-7_group, g_Eubacterium_coprostanoligenes_group) and metabolites [27-hydroxycholesterol (27HC), cholesterol] closely related to lipid metabolism were greatly reduced in the TC group. In addition, a clinical serum indicator (total cholesterol) and metabolites (27HC and cholesterol) had the strongest influence on the sample distribution. Furthermore, functional pathways related to steroid biosynthesis and lipid digestion were inhibited in the TC group. In the microbiota-metabolite network, 27HC was significantly related to metabolism-related microorganisms (g_Christensenellaceae_R-7_group).

CONCLUSIONS

Our research explored the characteristics of the gut microecology of patients with TC. The findings of this study will help to discover risk factors that affect the occurrence and development of TC in the intestinal microecology.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Diagnostic approach to thyroid cancer based on amino acid metabolomics in saliva by ultra-performance liquid chromatography with high resolution mass spectrometry

Thyroid cancer is a malignant disease with dramatically low advanced-stage 10-year survival. Meanwhile, the metabolites in saliva are becoming a wealthy source of disease biomarkers. However, there is a lack of non-invasive analytical methods for the identification of biomarkers in saliva for the preoperative diagnosis of thyroid cancer. Therefore, we developed an ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) method to simultaneously determine the metabolic levels of 10 amino acids in saliva, aiming to study the amino acid metabolism profile to promote early diagnosis of thyroid cancer. We tested unstimulated whole saliva from patients with papillary thyroid carcinoma (PTC; n = 61) and healthy controls (HC; n = 61), and used receiver operating characteristic (ROC) curves to establish the diagnostic value of potential markers. The method validation results showed good precision, linearity (R² > 0.99), recovery (92.2 %-110.3 %), intra- and inter-day precision (RSD < 7 % and RSD < 9 %, respectively). The concentration of 10 amino acids was significantly different between PTC and HC in human salivary analysis (P < 0.05), the area under the curve (AUC) values of a single marker for the diagnosis of PTC were ranging from 0.678 to 0.833. A panel of alanine, valine, proline, phenylalanine was selected in combination yielded the AUC of 0.936, which will improve the accuracy of early diagnosis of thyroid cancer (sensitivity: 91.2 %; specificity: 85.2 %). This study proved the possibility of salivary amino acid biomarkers for PTC early diagnosis, providing a simple auxiliary way for the non-invasive diagnosis of thyroid cancer.

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.

Our Little Friends with Big Roles: Alterations of the Gut Microbiota in Thyroid Disorders

BACKGROUND

The thyroid gland influences the metabolic processes in our body by producing thyroid hormones, and thyroid disorders can range from a harmless goiter to life-threatening cancer. A growing number of evidence support the link between gut microbiota composition and thyroid homeostasis. Gut dysbiosis can disrupt the normal gut barrier function, leading to immunologic and metabolic disorders.

OBJECTIVE

The aim of this review was to discuss the main features of gut dysbiosis associated with different thyroid disorders.

RESULTS

Gut microbiota contributes to thyroid hormone synthesis and hydrolysis of thyroid hormones conjugates. It has been shown that microbial metabolites may play a role in autoimmune thyroid diseases via modulating the immune system. Intestinal microbiota can contribute to the thyroid malignancies via controlling DNA damage and apoptosis and influencing inflammatory reactions by the microbiota- derived metabolites. However, the pathogenic role of altered gut microbiota in different thyroid disorders has not yet fully elucidated.

CONCLUSION

Further research is needed to assess the role of alterations of the gut microbiota in disease onset and development in order to achieve novel strategies for the prevention and treatment of these diseases.

The Potential of Metabolomics in the Diagnosis of Thyroid Cancer

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

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

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Studies on the Mechanism of Glutamate Metabolism in NTG-Induced Migraine Rats Treated with DCXF

Objective

To explore the mechanism of the antimigraine effect by active components extracted from the Dachuanxiong prescription (DCXF), nitroglycerin- (NTG-) induced migraine rats were used to detect the change of glutamate metabolism and the overall metabolic profile at different time points in the serum and Trigeminocervical complex(TCC) samples.

Method

The biological samples that were obtained at 30 minutes, 60 minutes, and 90 minutes after model establishment or drug administration were tested by GC-TOF-MS. Then, real-time PCR and western blot were applied to detect changes in the expression of some substances involved in glutamate metabolism.

Result

DCXF could improve the metabolic profile of serum and TCC in migraine rats and showed the time trend of treatment, mainly involved by amino acid metabolism (glutamate, aspartic acid, and alanine metabolism). In addition, DCXF could increase the expressions of GS at 60 min and 90 min and EAAT1 at 90 min. The results of GS protein were similar to that of mRNA.

Conclusion

The antimigraine effect of DCXF could be achieved by improving the metabolic profile and increasing the expressions of GS and EAAT1 to promote the glutamate cycle of TCC and serum samples in NTG-induced migraine rats to a certain extent.

Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Dedifferentiation of Papillary Thyroid Cancer

CONTEXT

Metabolic reprogramming is a common feature of tumorigenesis. It remains unknown concerning the expression pattern of metabolism-associated genes in dedifferentiated thyroid cancer (DDTC).

OBJECTIVE

This study aimed to identify a useful signature to indicate dedifferentiation of papillary thyroid cancer (PTC).

DESIGN AND SETTING

We used one discovery and two validation cohorts to screen out aberrant metabolic genes in DDTC, and further used The Cancer Genome Atlas (TCGA) cohort to search for independent risk factors for the low-differentiated phenotype of PTC as a signature of dedifferentiation. The prediction of the signature for DDTC was validated in the TCGA cohort and the combined Gene Expression Omnibus cohort. We also analyzed the correlations of the signature risk score with clinicopathological features of PTC. Gene set enrichment analyses were performed in the TCGA cohort.

RESULTS

Significant enrichment of metabolic pathways correlated with differentiation status of PTC. A signature of metabolic genes including LPCAT2, ACOT7, HSD17B8, PDE8B, and ST3GAL1 was discovered and validated across three cohorts. The signature was not only predictive of DDTC but also significantly associated with BRAFV600E mutation (P < 0.001), T3/T4 stage (P < 0.001), extrathyroidal extension (P < 0.001), lymph node metastasis (P < 0.001), and tumor/lymph node/metastasis III/IV stage (P < 0.001) in PTC. Downregulations of LPCAT2 expression (P = 0.009) and ST3GAL1 expression (P = 0.005) increased risks of decreased disease-free survival for patients. Furthermore, the signature was implicated in a number of oncogenic biological pathways.

CONCLUSIONS

Our findings suggest that metabolic deregulations mediate dedifferentiation of PTC, and that the metabolic gene signature can be used as a biomarker for DDTC.

Oncometabolites as biomarkers in thyroid cancer: a systematic review

Introduction

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

Methods

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

Results

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

Conclusion

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

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.