Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation

Emerging Potential of Metabolomics in Thyroid Cancer-A Comprehensive Review

Thyroid cancer is a very common endocrine system malignancy. Nevertheless, a dearth of precise markers makes it challenging to apply precision medicine to thyroid cancer. The limitations of standard diagnosis techniques (fine-needle aspiration biopsy), such as indeterminate cases and inaccuracies in distinguishing between different types of cancers, lead to unnecessary surgeries and thus warrant the development of more discriminatory biomarkers to improve the accuracy of existing diagnostic and prognostic techniques. Moreover, individualized therapies for thyroid cancer are necessary to avoid overtreatment of indolent lesions and undertreatment of high-risk progressive disease. As thyroid cancer metabolic signatures are associated with disease aggressiveness and responsiveness to therapy, metabolomics has been recently used for diagnostic and prognostic biomarker discovery. This strategy has enabled the detection of several metabolites from tissue samples or biofluids to facilitate the classification of disease aggressiveness and to potentially assist in individualized therapies. In this review, we summarize the utilization and potential of metabolomics in thyroid cancer.

The Predictive Value of a Nomogram Based on Ultrasound Radiomics, Clinical Factors, and Enhanced Ultrasound Features for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma

This study aims to establish and validate an ultrasound radiomics nomogram for preoperative prediction of central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) before operation. A retrospective analysis conducted on ultrasonic images and clinical features derived from 288 PTMC patients, who were divided into training cohorts (n = 201) and validating cohorts (n = 87) in a ratio of 7:3 base on the principle of random allocation. Radiomics features were extracted from the PTMC patients after ultrasonic examination, followed by dimension reduction and characteristic selection to construct the radiomics score (Radscore) using LASSO regression analysis. Subsequently, the models, ultrasound features plus clinical features (US-Clin), radiomics score model, and combined model of clinical features plus ultrasound features and Radscore (Combined-model) were built through multi-factor logistic regression analysis. After that, the nomograms were developed for visualization and presentation of these models. The discriminative power, calibration and clinical utility of the nomogram models were evaluated in the training and validating cohorts. The Radscore model comprised 12 carefully selected features. The independent risk factors for conventional ultrasound features and clinical features of PTMC in predicting CLNM included age <45 years, tumor envelope invasion, male gender and presence of microcalcifications, while the enhanced ultrasound features risk factor was extrathyroidal expansion. The combined model showed good performance in predicting PTMC CLNM, with AUCs of 0.921 and 0.889 in the training and validating cohorts, respectively. And DCA based on the prediction model showed good clinical utility. The nomogram developed based on preoperative clinical data, ultrasound features, and Radscore of PTMC patients can more accurately predict central lymph node metastasis (CLNM) in PTMC patients. However, it needs to be validated for clinical applicability in multicenter studies with larger sample sizes and combined with genomic mutation analyses of the tumors.

Serum metabolome associated with novel and legacy per- and polyfluoroalkyl substances exposure and thyroid cancer risk: A multi-module integrated analysis based on machine learning

BACKGROUND

Exposure to per- and polyfluoroalkyl substances (PFAS) may linked to thyroid cancer (TC) risk, but inconsistent findings and a lack of studies on mixed exposures exist, especially regarding novel PFAS compounds. Additionally, little is known about the potential mechanisms underlying the association.

OBJECTIVES

Explore the effects of PFAS exposure on the serum metabolome and its correlation with TC.

METHODS

A 1:1 age- and sex-matched case-control study was administered with 746 TC cases and healthy controls. Liquid chromatography-high resolution mass spectrometry determined serum 11 PFAS and untargeted metabolome profile. ENET and LightGBM models were used to explore the exposure patterns and perform variable selection. The mixed exposure effects were assessed using Weighted quantile sum regression and Bayesian kernel machine regression. Metabolome-wide association analyses were performed to assess metabolic dysregulation associated with PFAS, and a structural synthesis analysis was used to detect latent groups of individuals with TC based on PFAS levels and metabolite patterns.

RESULTS

Ten of the 11 PFAS were detected in > 80 % of the population. PFHxA and PFDoA exposure associated with increased TC risk, while PFHxS and PFOA associated with decreased TC risk in single compound models (all P < 0.05). Machine learning algorithms identified PFHxA, PFDoA, PFHxS, PFOA, and PFHpA as the key PFAS influencing the development of TC, and mixed exposures have an overall positive effect on TC risk, with PFHxA making the primary contribution. A novel integrative analysis identified a cluster of TC patients characterized by increased PFHxA, PFDoA, PFHpA and decreased PFOA, PFHxS levels, and altered metabolite patterns highlighted by the upregulation of free fatty acids.

CONCLUSIONS

PFAS exposure is linked to a higher risk of TC, possibly through changes in fatty acid metabolism. Larger, prospective studies are needed to confirm these findings, and the role of short-chain PFAS requires more attention.

Omics experiments in Iran, a review in endocrine and metabolism disorders studies

Objective

The aim of this study was to evaluate the status of scientific research output of omics in regards to human diseases with more attention shifted toward endocrine and metabolism disorders in Iran, in order to find scientific gaps and also to design future plans for further investigations in this field.

Methods

Extensive search was performed in the electronic databases of Scopus and PubMed, and documents published by Iranian authors up to 27 December 2020 were extracted. Articles related to human diseases were included and categorized based on their types and topics.

Results

A total of 904 publications were found. Followed by checking their titles and abstracts, 327 studies were included. The trend of publication has been increasing during the past years. Regarding this subject, the highest number of publications was in the field of malignant disorders with 82 publications followed by reproductive system diseases and infectious diseases with 33 publications in each subject. Only 12 articles were found in the field of endocrinology and metabolism. The most popular techniques used in those reports were two-dimensional electrophoresis coupled with mass spectrometry (34.4 %) followed by NMR (22.6 %), LC/MS/MS (15 %).

Conclusions

Omics studies in Iran are a relatively new approach and the number of original articles regarding endocrine disorders in humans is limited. Providing appropriate infrastructures including lab facilities with high technology instruments can improve the quality and quantity of basic and clinical researches in this field.

In Search of Relevant Urinary Biomarkers for Thyroid Papillary Carcinoma and Benign Thyroid Nodule Differentiation, Targeting Metabolic Profiles and Pathways via UHPLC-QTOF-ESI+-MS Analysis

BACKGROUND

Identification of specific urine metabolic profiles for patients diagnosed with papillary thyroid carcinoma (TC) vs. benign nodules (B) to identify specific biomarkers and altered pathways compared to those of healthy controls (C).

METHODS

Patient urine samples were collected, before surgery and after a histological confirmation of TC (n = 30) and B (n = 30), in parallel with sample collection from healthy controls (n = 20). The untargeted and semi-targeted metabolomic protocols were applied using UPLC-QTOF-ESI+-MS analysis, and the statistical analysis was performed using the Metaboanalyst 6.0 platform. The results for the blood biomarkers, previously published, were compared with the data obtained from urine sampling using the Venny algorithm and multivariate statistics.

RESULTS

Partial least squares discrimination, including VIP values, random forest graphs, and heatmaps (p < 0.05), together with biomarker analysis (AUROC ranking) and pathway analysis, suggested a specific model for the urinary metabolic profile and pathway alterations in TC and B vs. C, based on 190 identified metabolites in urine that were compared with the serum metabolites. By semi-targeted metabolomics, 10 classes of metabolites, considered putative biomarkers, were found to be responsible for specific alterations in the metabolic pathways, from polar molecules to lipids. Specific biomarkers for discrimination were identified in each class of metabolites that were either upregulated or downregulated when compared to those of the controls.

CONCLUSIONS

The lipidomic window was the most relevant for identifying biomarkers related to thyroid cancer and benign conditions, since this study detected a stronger involvement of lipids and selenium-related molecules for metabolic discrimination.

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.

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

PURPOSE

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

EXPERIMENTAL DESIGN

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

RESULTS

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

CONCLUSIONS

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

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

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

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer

Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing's syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing's syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

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.

Elevated Plasma Levels of MT4-MMP and MT6-MMP; A New Observation in Patients with Thyroid Nodules

BACKGROUND

Based on the critical role of MT4-MMP and MT6-MMP in carcinogenesis, we focused on MT4-MMP and MT6-MMP circulating levels in patients with thyroid nodules.

METHODS

Plasma samples were collected from three groups, including papillary thyroid cancer (PTC; n=30), multinodular goiter (MNG; n=30), and healthy subjects (n=22). Enzyme-linked immunosorbent assay (ELISA) was used to obtain the concentration of MT4-MMP and MT6-MMP in the three groups.

RESULTS

Analysis of data demonstrated increased levels of MT4-MMP (PTC: 4.90±1.35, MNG: 4.89±1.37, and healthy: 3.13±1.42) and MT6-MMP (PTC: 8.29±2.50, MNG: 7.34±2.09, and healthy:5.01±2.13) in thyroid nodules by comparison with healthy subjects (P<0.05). There were no significant differences in the levels of the two MT-MMPs between PTC and MNG (P>0.05). Increased plasma levels of MT4-MMP (odds ratio=2.48; 95% CI: 1.46-4.19; P=0.001) or MT6-MMP (odds ratio=1.81; 95% CI: 1.29-2.53; P=0.001) were associated with increased risk of PTC tumorigenesis. Interestingly, a strong positive association was observed between MT4-MMP and MT6-MMP in the three groups (PTC: r=0.766**, P=0.000; MNG: r=0.856**, P=0.000; healthy r=0.947**, P=0.000). Areas under the ROC curve for MT4-MMP and MT6-MMP were 0.82 and 0.96, respectively. At the cutoff value>4.7 (ng/mL), MT4-MMP and MT6-MMP showed a sensitivity of 63.3% and 90.0%, respectively, with 100% specificity.

CONCLUSION

Our work has led us to imply that the higher levels of MT4-MMP and MT6-MMP are closely linked with both PTC and MNG tumorigenesis. They may probably promote the development of thyroid lesions; however, more research is needed to further clarify the current findings.

Multi-Omics and Management of Follicular Carcinoma of the Thyroid

Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland, accounting for up to 20% of all primary malignant tumors in iodine-replete areas. The diagnostic work-up, staging, risk stratification, management, and follow-up strategies in patients who have FTC are modeled after those of papillary thyroid carcinoma (PTC), even though FTC is more aggressive. FTC has a greater propensity for haematogenous metastasis than PTC. Furthermore, FTC is a phenotypically and genotypically heterogeneous disease. The diagnosis and identification of markers of an aggressive FTC depend on the expertise and thoroughness of pathologists during histopathological analysis. An untreated or metastatic FTC is likely to de-differentiate and become poorly differentiated or undifferentiated and resistant to standard treatment. While thyroid lobectomy is adequate for the treatment of selected patients who have low-risk FTC, it is not advisable for patients whose tumor is larger than 4 cm in diameter or has extensive extra-thyroidal extension. Lobectomy is also not adequate for tumors that have aggressive mutations. Although the prognosis for over 80% of PTC and FTC is good, nearly 20% of the tumors behave aggressively. The introduction of radiomics, pathomics, genomics, transcriptomics, metabolomics, and liquid biopsy have led to improvements in the understanding of tumorigenesis, progression, treatment response, and prognostication of thyroid cancer. The article reviews the challenges that are encountered during the diagnostic work-up, staging, risk stratification, management, and follow-up of patients who have FTC. How the application of multi-omics can strengthen decision-making during the management of follicular carcinoma is also discussed.

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.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.

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.

Uncontrolled Thyroid during Pregnancy Alters the Circulative and Exerted Metabolome

Normal levels of thyroid hormones (THs) are essential for a normal pregnancy outcome, fetal growth and the normal function of the central nervous system. Hypothyroidism, a common endocrine disorder during pregnancy, is a significant metabolic factor leading to cognitive impairments. It is essential to investigate whether patients with thyroid dysfunction may present an altered circulative and excreted metabolic profile, even after receiving treatment with thyroxine supplements. NMR metabolomics was employed to analyze 90 serum and corresponding colostrum samples. Parallel analyses of the two biological specimens provided a snapshot of the maternal metabolism through the excretive and circulating characteristics of mothers. The metabolomics data were analyzed by performing multivariate statistical, biomarker and pathway analyses. Our results highlight the impact of hypothyroidism on metabolites’ composition during pregnancy and lactation. Thyroid disorder causing metabolite fluctuations may lead to impaired lipid and glucose metabolic pathways as well as aberrant prenatal neurodevelopment, thus posing a background for the occurrence of metabolic syndrome or neurogenerative diseases later in life. This risk applies to not only untreated but also hypothyroid women under replacement therapy since our findings in both biofluids framed a different metabolic phenotype for the latter group, thus emphasizing the need to monitor women adequately after treatment initiation.

Untargeted metabolomics analysis of esophageal squamous cell cancer progression

90% of esophageal cancer are esophageal squamous cell carcinoma (ESCC) and ESCC has a very poor prognosis and high mortality. Nevertheless, the key metabolic pathways associated with ESCC progression haven’t been revealed yet. Metabolomics has become a new platform for biomarker discovery over recent years. We aim to elucidate dominantly metabolic pathway in all ESCC tumor/node/metastasis (TNM) stages and adjacent cancerous tissues. We collected 60 postoperative esophageal tissues and 15 normal tissues adjacent to the tumor, then performed Liquid Chromatography with tandem mass spectrometry (LC–MS/MS) analyses. The metabolites data was analyzed with metabolites differential and correlational expression heatmap according to stage I vs. con., stage I vs. stage II, stage II vs. stage III, and stage III vs. stage IV respectively. Metabolic pathways were acquired by Kyoto Encyclopedia of Genes and Genomes. (KEGG) pathway database. The metabolic pathway related genes were obtained via Gene Set Enrichment Analysis (GSEA). mRNA expression of ESCC metabolic pathway genes was detected by two public datasets: gene expression data series (GSE)23400 and The Cancer Genome Atlas (TCGA). Receiver operating characteristic curve (ROC) analysis is applied to metabolic pathway genes. 712 metabolites were identified in total. Glycerophospholipid metabolism was significantly distinct in ESCC progression. 16 genes of 77 genes of glycerophospholipid metabolism mRNA expression has differential significance between ESCC and normal controls. Phosphatidylserine synthase 1 (PTDSS1) and Lysophosphatidylcholine Acyltransferase1 (LPCAT1) had a good diagnostic value with Area under the ROC Curve (AUC) > 0.9 using ROC analysis. In this study, we identified glycerophospholipid metabolism was associated with the ESCC tumorigenesis and progression. Glycerophospholipid metabolism could be a potential therapeutic target of ESCC progression.

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.

Metabolic Profile Characterization of Different Thyroid Nodules Using FTIR Spectroscopy: A Review

Thyroid cancer's incidence has increased in the last decades, and its diagnosis can be a challenge. Further and complementary testing based in biochemical alterations may be important to correctly identify thyroid cancer and prevent unnecessary surgery. Fourier-transform infrared (FTIR) spectroscopy is a metabolomic technique that has already shown promising results in cancer metabolome analysis of neoplastic thyroid tissue, in the identification and classification of prostate tumor tissues and of breast carcinoma, among others. This work aims to gather and discuss published information on the ability of FTIR spectroscopy to be used in metabolomic studies of the thyroid, including discriminating between benign and malignant thyroid samples and grading and classifying different types of thyroid tumors.

Screening and validation of lymph node metastasis risk-factor genes in papillary thyroid carcinoma

BACKGROUND

Although most papillary thyroid carcinoma (PTC) cases have a good prognosis, some PTCs are more aggressive and are often accompanied by lymph node (LN) metastasis, a high recurrence rate, and poor prognosis. Distinguishing highly invasive metastatic PTC is an urgent problem that needs to be addressed clinically. We analyzed a microarray of metastasized PTC and validated it using quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry to identify biomarkers that can be used to assess the risk of PTC metastasis.

METHODS

The microarray of metastasized PTC was screened using the Gene Expression Omnibus (GEO) database. The differences between cancer and normal tissues were analyzed using the official GEO tool: GEO2R. Gene expression profile data (GEPIA) were used to verify the expression of differential genes in large samples and to analyze their correlation. The Kaplan-Meier plotter (KM-plotter) database was used for the analysis of genes potentially related to survival. RT-qPCR was used to check the expression of risk factor genes in pathological sections from PTC patients with clinical LN metastasis. Immunohistochemistry was used to verify the expression of core risk-associated genes.

RESULTS

Fourteen PTC metastasis-associated genes were identified. In metastasized PTC, CLDN1, LRP4, LRRK2, and TENM1 were highly expressed, whereas DIO1, DPP6, HGD, IPCEF1, MT1F, SLC26A4, SLC26A7, SPX, TFF3, and TPO were expressed at low levels, compared to expression in normal tissues. DIO1, HGD, SLC26A4, and TPO were found to be the core risk genes in the PTC metastatic risk set. Results based on clinical samples showed that the expression differences for metastasis risk-associated genes were consistent with the bioinformatics analysis results.

CONCLUSIONS

Fourteen differentially expressed genes (CLDN1, LRP4, LRRK2, TENM1, DIO1, DPP6, HGD, IPCEF1, MT1F, SLC26A4, SLC26A7, SPX, TFF3, TPO) are associated with an increased risk of PTC metastasis, and DIO1, HGD, SLC26A4, and TPO are the key risk-associated genes in this set that might affect the occurrence and development of PTC through iodine metabolism. These genes could provide a reference for clinical metastatic PTC risk evaluation and treatment.

Predictive model for recurrence of renal cell carcinoma by comparing pre- and postoperative urinary metabolite concentrations

To improve treatment outcomes in real practice, useful biomarkers are desired when predicting postoperative recurrence for renal cell carcinoma (RCC). We collected data from patients who underwent definitive surgery for RCC and for benign urological tumor at our department between November 2016 and December 2019. We evaluated the differences in pre‐ and postoperative urinary metabolites with our precise quantitative method and identified predictive factors for RCC recurrence. Additionally, to clarify the significance of metabolites, we measured the intracellular metabolite concentration of three RCC cell lines. Among the 56 patients with RCC, nine had a recurrence (16.0%). When comparing 27 patients with T1a RCC and 10 with benign tumor, a significant difference was observed between pre‐ and postoperative concentrations among 10 urinary metabolites. In these 10 metabolites, multiple logistic regression analysis identified five metabolites (lactic acid, glycine, 2‐hydroxyglutarate, succinic acid, and kynurenic acid) as factors to build our recurrence prediction model. The values of area under the receiver operating characteristic curve, sensitivity, and specificity in this predictive model were 0.894%, 88.9%, and 88.0%, respectively. When stratified into low and high risk groups of recurrence based on this model, we found a significant drop of recurrence‐free survival rates among the high risk group. In in vitro studies, intracellular metabolite concentrations of metastatic tumor cell lines were much higher than those of primary tumor cell lines. By using our quantitative evaluation of urinary metabolites, we could predict postoperative recurrence with high sensitivity and specificity. Urinary metabolites could be noninvasive biomarkers to improve patient outcome.

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

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

Soft Tissue Sarcoma: An Insight on Biomarkers at Molecular, Metabolic and Cellular Level

Simple Summary

Soft tissue sarcoma is a rare mesenchymal malignancy. Despite the advancements in the fields of radiology, pathology and surgery, these tumors often recur locally and/or with metastatic disease. STS is considered to be a diagnostic challenge due to the large variety of histological subtypes with clinical and histopathological characteristics which are not always distinct. One of the important clinical problems is a lack of useful biomarkers. Therefore, the discovery of biomarkers that can be used to detect tumors or predict tumor response to chemotherapy or radiotherapy could help clinicians provide more effective clinical management.

Abstract

Soft tissue sarcomas (STSs) are a heterogeneous group of rare tumors. Although constituting only 1% of all human malignancies, STSs represent the second most common type of solid tumors in children and adolescents and comprise an important group of secondary malignancies. Over 100 histologic subtypes have been characterized to date (occurring predominantly in the trunk, extremity, and retroperitoneum), and many more are being discovered due to molecular profiling. STS mortality remains high, despite adjuvant chemotherapy. New prognostic stratification markers are needed to help identify patients at risk of recurrence and possibly apply more intensive or novel treatments. Recent scientific advancements have enabled a more precise molecular characterization of sarcoma subtypes and revealed novel therapeutic targets and prognostic/predictive biomarkers. This review aims at providing a comprehensive overview of the most relevant cellular, molecular and metabolic biomarkers for STS, and highlight advances in STS-related biomarker research.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

Molecular Chaperones and Thyroid Cancer

Thyroid cancers are the most common of the endocrine system malignancies and progress must be made in the areas of differential diagnosis and treatment to improve patient management. Advances in the understanding of carcinogenic mechanisms have occurred in various fronts, including studies of the chaperone system (CS). Components of the CS are found to be quantitatively increased or decreased, and some correlations have been established between the quantitative changes and tumor type, prognosis, and response to treatment. These correlations provide the basis for identifying distinctive patterns useful in differential diagnosis and for planning experiments aiming at elucidating the role of the CS in tumorigenesis. Here, we discuss studies of the CS components in various thyroid cancers (TC). The chaperones belonging to the families of the small heat-shock proteins Hsp70 and Hsp90 and the chaperonin of Group I, Hsp60, have been quantified mostly by immunohistochemistry and Western blot in tumor and normal control tissues and in extracellular vesicles. Distinctive differences were revealed between the various thyroid tumor types. The most frequent finding was an increase in the chaperones, which can be attributed to the augmented need for chaperones the tumor cells have because of their accelerated metabolism, growth, and division rate. Thus, chaperones help the tumor cell rather than protect the patient, exemplifying chaperonopathies by mistake or collaborationism. This highlights the need for research on chaperonotherapy, namely the development of means to eliminate/inhibit pathogenic chaperones.

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.

Low level of plasma fibulin-1 in patients with thyroid lesions: a case-control study and literature review

Investigating novel biomarkers discriminating thyroid nodules is a matter of great importance for differential diagnosis. The current study was planned to investigate the diagnostic value of fibulin-1 in plasma specimens of patients with thyroid nodules. A literature review was also performed to gain an understanding of the existing research relevant to the main role of fibulin-1 in carcinogenesis. In this case-control study, the levels of plasma fibulin-1 were compared in 82 subjects including papillary thyroid cancer (PTC; n = 30), multinodular goiter (MNG; n = 30), and healthy subjects (n = 22) using enzyme-linked immunosorbent assay (ELISA). Fibulin-1 levels of patients with PTC and MNG were documented to be significantly lower than those of healthy subjects (PTC vs. Healthy; P = 0.000, MNG vs. Healthy; P = 0.000). No statistically significant differences were found between PTC and MNG groups when fibulin-1 levels were compared (P > 0.05). Low level of plasma fibulin-1 was associated with an increased risk of PTC tumorigenesis (odds ratio = 0.810; 95% CI: 0.704-0.933; P = 0.003). Further, fibulin-1 had an appropriate diagnostic value for detecting PTC patients with a sensitivity of 73.33%, and specificity of 100% at the cutoff value > 4.9 (ng/ml). According to the results of the present research which are tied well with previous studies, the abnormal downregulation of fibulin-1 may play a role in the PTC and MNG tumorigenesis. In addition, fibulin-1 probably promotes the development and progression of other human cancer; however, further studies are needed to improve current understandings.

Integration of Serum Metabolomics into Clinical Assessment to Improve Outcome Prediction of Metastatic Soft Tissue Sarcoma Patients Treated with Trabectedin

Soft tissue sarcomas (STS) are a group of rare and heterogeneous cancers with few diagnostic or prognostic biomarkers. This metabolomics study aimed to identify new serum prognostic biomarkers to improve the prediction of overall survival in patients with metastatic STS. The study enrolled 24 patients treated with the same trabectedin regimen. The baseline serum metabolomics profile, targeted to 68 metabolites encompassing amino acids and bile acids pathways, was quantified by liquid chromatography-tandem mass spectrometry. Correlations between individual metabolomics profiles and overall survival were examined and a risk model to predict survival was built by Cox multivariate regression. The median overall survival of the studied patients was 13.0 months (95% CI, 5.6–23.5). Among all the metabolites investigated, only citrulline and histidine correlated significantly with overall survival. The best Cox risk prediction model obtained integrating metabolomics and clinical data, included citrulline, hemoglobin and patients’ performance status score. It allowed to distinguish patients into a high-risk group with a low median overall survival of 2.1 months and a low- to moderate-risk group with a median overall survival of 19.1 months (p < 0.0001). The results of this metabolomics translation study indicate that citrulline, an amino acid belonging to the arginine metabolism, represents an important metabolic signature that may contribute to explain the high inter-patients overall survival variability of STS patients. The risk prediction model based on baseline serum citrulline, hemoglobin and performance status may represent a new prognostic tool for the early classification of patients with metastatic STS, according to their overall survival expectancy.

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.

Heat shock proteins in thyroid malignancies: Potential therapeutic targets for poorly-differentiated and anaplastic tumours?

Thyroid cancer is the most common endocrine malignancy, with well-differentiated subtypes characterized by an excellent prognosis due to their optimal sensitivity to standard therapies whereas poorly differentiated and anaplastic tumours by chemo/radio-resistance and unfavourable outcome. Heat Shock Proteins (HSPs) are molecular chaperones overexpressed in thyroid malignancies and involved in crucial functions responsible for thyroid carcinogenesis, as protection from apoptosis, drug resistance and cell migration. Thus, HSPs inhibitors have been proposed as novel therapeutic agents in thyroid cancer to revert molecular mechanisms of tumour progression. In this review, we report an overview on the biological role of HSPs, and specifically HSP90s, in thyroid cancer and their potential involvement as biomarkers. We discuss the rationale to evaluate HSPs inhibitors as innovative anticancer agents in specific subtypes of thyroid cancer characterized by poor response to therapies with the objective to target single family chaperones to reduce, simultaneously, the expression/stability of multiple client proteins.