The metabolomics and lipidomics window into thyroid cancer research

Discrimination of Benign and Malignant Thyroid Nodules through Comparative Analyses of Human Saliva Samples via Metabolomics and Deep-Learning-Guided Label-free SERS

Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65-68%, among which a small portion (less than 5%) is malignant (cancerous). Therefore, it is important to discriminate benign thyroid nodules from malignant thyroid nodules. In this study, an equal number of participants with benign and malignant thyroid nodules (N = 10/group) were recruited. Saliva samples were collected from each participant, and SERS spectra were acquired, followed by validation using a metabolomics approach. An additional equal number of patients (N = 40/group) were recruited to construct diagnostic models. The performance of various machine learning (ML) algorithms was assessed using multiple evaluation metrics. Finally, the reliability of the optimal model was tested using blind test data (N = 10/group for benign and malignant thyroid nodules). The results showed a consistent trend between the SERS metabolic profile and the metabolites identified through MS analysis. The Multi-ResNet algorithm was optimal, achieving a 95% accuracy in sample discrimination. Additionally, blind test data sets yielded an overall accuracy of 83%. In summary, the deep-learning-guided SERS technique holds great potential in the accurate discrimination of benign and malignant thyroid nodules via human saliva samples, which facilitates the noninvasive diagnosis of malignant thyroid nodules in clinical settings.

The influence of cancer on the reprogramming of lipid metabolism in healthy thyroid tissues of patients with papillary thyroid carcinoma

BACKGROUND AND OBJECTIVES

Over the years we observed changes in the metabolism of glucose, amino acids, fatty acids (FA) and nucleotides in cancer cells in order to maintain their viability and proliferate. Moreover, as the latest data show, cancer also forces a complete change in the behavior of other tissues. For instance, fat-filled adipocytes are often found in the vicinity of invasive solid human tumors. We investigated the effects of papillary thyroid carcinoma (PTC) on the lipid metabolism of healthy tissue distant from the tumor.

METHOD

Thyroid tissue was collected from female patients immediately after surgical removal of the entire thyroid gland. Blood samples were collected from PTC patients and healthy volunteers. Real-time PCR assays were performed to analyze the expression of lipogenic genes and a broad panel of FA was determined using the gas chromatography-mass spectrometry method.

RESULTS

The concentration of lipids was higher in paratumor tissue than in healthy thyroid tissue (p = 0.005). The lipogenic genes tested were significantly increased in paratumor tissue compared to healthy tissue, especially enzymes related to the synthesis of very long-chain saturated and polyunsaturated FAs (VLCSFAs and PUFAs, respectively) (p < 0.001). The FA profile also showed increased levels of C22-C26, VLCSFAs and almost all PUFAs in paratumor tissue (p < 0.05).

CONCLUSION

Our study suggests that a restructuring of lipid metabolism occurs in the adjacent healthy thyroid gland and that the metabolism of VLCSFAs and PUFAs is higher in the paratumor tissue than in the distant tissue of the healthy thyroid gland.

Thyroid Cancer research at endocrinology and metabolism research institute (EMRI): a report of scientific activities between 2005 and 2020

Purpose

Endocrinology and Metabolism Research Institute (EMRI), Tehran University of Medical Sciences, has achieved many advances in the understanding of epidemiology, pathology, and molecular biology of thyroid cancer (TC). This paper will focus on published literature in the field of TC research at EMRI since its inception.

Methods

A comprehensive literature search of "Scopus", "Web of Science (ISI)", "PubMed", and "SID" databases was done to identify TC-based studies in EMRI since 2005, using the predefined keywords.

Results

All the records were reviewed. Eventually, 46 articles that met the inclusion and exclusion criteria were considered. Timeline, characteristics, and summarized findings of the included most studies have been briefly described.

Conclusion

The main focus of the TC research at EMRI is to investigate genetic and epigenetic changes as well as the cellular and molecular aspects seen more frequently in TC patients, and find out how exactly these alterations lead to more aggressive TC behavior and resistance to the conventional treatment approaches in those patients.

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.

Combined metabolomic and lipidomic analysis uncovers metabolic profile and biomarkers for papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy with a rapidly increasing incidence. The pathogenesis of PTC is unclear, but metabolic and lipidomic reprogramming may play a role in tumor growth. We applied ultra-performance liquid chromatography-tandem mass spectrometry to perform widely targeted metabolomics and lipidomics on plasma samples from 94 patients with PTC and 100 healthy controls. We identified 113 differential metabolites and 236 differential lipids, mainly involved in branched-chain amino acid metabolism, glutamate and glutamine metabolism, tricarboxylic acid cycle, and lipid metabolism. We also screened three potential metabolite biomarkers: sebacic acid, L-glutamine, and indole-3-carboxaldehyde. These biomarkers showed excellent diagnostic performance for PTC in both discovery and validation cohorts, with areas under the receiver operating characteristic curves of 0.994 and 0.925, respectively. Our findings reveal distinct metabolic and lipidomic features of PTC and provide novel targets for diagnosis and treatment.

Exploring blood metabolites and thyroid disorders: a bidirectional mendelian randomization study

Background

Human blood metabolites have demonstrated close associations with thyroid disorders in observational studies. However, it's essential to determine whether these correlations imply causation. Mendelian Randomization (MR) offers a promising approach to investigate these patterns.

Aims

The primary aim of our investigation is to establish causality between blood metabolites and three thyroid disorders: TC, GD, and HT.

Methods

We employed a two-sample bidirectional MR analysis approach to assess the relationships between 452 blood metabolites and the three aforementioned thyroid disorders. Causal links were estimated using the IVW method, with sensitivity analyses conducted via MR-Egger, Weighted Median, and MR-PRESSO. We assessed potential heterogeneity and pleiotropy using MR-Egger intercept and Cochran's Q statistic. Additionally, we conducted pathway analysis to identify potential metabolic pathways.

Results

We found 46 metabolites that showed suggestive associations with thyroid disease risk, especially Aspartate (ORIVW=7.41; 95%CI: 1.51-36.27; PIVW=0.013) and C-glycosyltryptophan (ORIVW=0.04; 95%CI: 0.00-0.29; PIVW=0.001) impacted TC, Kynurenine (ORIVW=2.69; 95%CI: 1.08-6.66; PIVW=0.032) and 4-androsten-3beta,17beta-diol disulfate 2 (ORIVW=0.78; 95%CI: 0.48-0.91; PIVW=0.024) significantly impacted GD, and Alpha-ketoglutarate (ORIVW=46.89; 95%CI: 4.65-473.28; PIVW=0.001) and X-14189-leucylalanine (ORIVW=0.31; 95%CI: 0.15-0.64 PIVW=0.001) significantly impacted HT. We also detected 23 metabolites influenced by TC and GD. Multiple metabolic pathways have been found to be involved in thyroid disease.

Conclusion

Our MR findings suggest that the identified metabolites and pathways can serve as biomarkers for clinical thyroid disorder screening and prevention, while also providing new insights for future mechanistic exploration and drug target selection.

Cross-sectional relationships between general and central adiposity and plasma amino acids in Chinese adults

Adiposity is an important determinant of blood metabolites, but little is known about the variations of blood amino acids according to general and central adiposity status among Chinese population. This study included 187 females and 322 males who were cancer-free subjects randomly selected from two cohorts in Shanghai, China. Participants' plasma concentrations of amino acids were measured by ultra-performance liquid chromatography coupled to tandem mass spectrometry. Linear regression models were used to examine the cross-sectional correlations between general and central adiposity and amino acid levels. A total of 35 amino acids in plasma were measured in this study. In females, alanine, aspartic acid and pyroglutamic acid were positively correlated with general adiposity. In males, glutamic acid, aspartic acid, valine and pyroglutamic acid showed positive correlations, and glutamine, serine and glycine showed negative correlations with both general and central adiposity; phenylalanine, isoleucine and leucine were positively correlated and N-phenylacetylglutamine was negatively correlated with general adiposity; asparagine was negatively correlated with central adiposity. In summary, general adiposity and central adiposity were correlated with the concentrations of specific plasma amino acids among cancer-free female and male adults in China. Adiposity-metabolite characteristics and relationships should be considered when studying blood biomarkers for adiposity-related health outcomes.

Point-of-Care Diagnosis of Endometrial Cancer Using the Surgical Intelligent Knife (iKnife)-A Prospective Pilot Study of Diagnostic Accuracy

Introduction: Delays in the diagnosis and treatment of endometrial cancer negatively impact patient survival. The aim of this study was to establish whether rapid evaporative ionisation mass spectrometry using the iKnife can accurately distinguish between normal and malignant endometrial biopsy tissue samples in real time, enabling point-of-care (POC) diagnoses. Methods: Pipelle biopsy samples were obtained from consecutive women needing biopsies for clinical reasons. A Waters G2-XS Xevo Q-Tof mass spectrometer was used in conjunction with a modified handheld diathermy (collectively called the 'iKnife'). Each tissue sample was processed with diathermy, and the resultant surgical aerosol containing ionic lipid species was then analysed, producing spectra. Principal component analyses and linear discriminant analyses were performed to determine variance in spectral signatures. Leave-one-patient-out cross-validation was used to test the diagnostic accuracy. Results: One hundred and fifty patients provided Pipelle biopsy samples (85 normal, 59 malignant, 4 hyperplasia and 2 insufficient), yielding 453 spectra. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differential phospholipid spectra. Cross-validation revealed a diagnostic accuracy of 89% with sensitivity, specificity, positive predictive value and negative predictive value of 85%, 93%, 94% and 85%, respectively. Conclusions: This study is the first to use the iKnife to identify cancer in endometrial Pipelle biopsy samples. These results are highly encouraging and suggest that the iKnife could be used in the clinic to provide a POC diagnosis.

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.

Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis

The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)—anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC.

Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research

Shotgun lipidomics is an analytical approach for large-scale and systematic analysis of the composition, structure, and quantity of cellular lipids directly from lipid extracts of biological samples by mass spectrometry. This approach possesses advantages of high throughput and quantitative accuracy, especially in absolute quantification. As cancer research deepens at the level of quantitative biology and metabolomics, the demand for lipidomics approaches such as shotgun lipidomics is becoming greater. In this chapter, the principles, approaches, and some applications of shotgun lipidomics for cancer research are overviewed.

Active surveillance as a management strategy for papillary thyroid microcarcinoma

Active surveillance (AS) can be considered as a treatment strategy for low risk papillary thyroid microcarcinoma (PTMC), with the absence of clinically apparent lymph nodes, extrathyroidal extensions, and distant metastasis. After reviewing the reports on AS of low risk PTMCs worldwide, we introduced AS, and discussed the selection criteria for active surveillance candidates based on different guidelines and the follow-up schedules. Moreover, the requirement of cytological diagnosis, progression evaluation methods, necessity of thyrotropin suppression, and medical costs were issues that both clinicians and patients considered. The usefulness of AS for low risk PTMC patients depended on accurate and confidential evaluation of patient risk. Clinicians may adopt measures like dynamic monitoring, risk stratification, and making personal follow-up schedules to minimize these potential risks. By appropriately selecting PTMC patients, AS can be an effective alternative treatment to immediate surgery.

Chronic kidney disease: a review of proteomic and metabolomic approaches to membranous glomerulonephritis, focal segmental glomerulosclerosis, and IgA nephropathy biomarkers

Chronic Kidney Disease (CKD) is a global health problem annually affecting millions of people around the world. It is a comprehensive syndrome, and various factors may contribute to its occurrence. In this study, it was attempted to provide an accurate definition of chronic kidney disease; followed by focusing and discussing on molecular pathogenesis, novel diagnosis approaches based on biomarkers, recent effective antigens and new therapeutic procedures related to high-risk chronic kidney disease such as membranous glomerulonephritis, focal segmental glomerulosclerosis, and IgA nephropathy, which may lead to end-stage renal diseases. Additionally, a considerable number of metabolites and proteins that have previously been discovered and recommended as potential biomarkers of various CKD_s using ‘-omics-’ technologies, proteomics, and metabolomics were reviewed.

Tutorial on lipidomics

The mainstream of lipidomics involves mass spectrometry-based, systematic, and large-scale studies of the structure, composition, and quantity of lipids in biological systems such as organs, cells, and body fluids. As increasingly more researchers in broad fields are beginning to pay attention to and actively learn about the lipidomic technology, some introduction on the topic is needed to help the newcomers to better understand the field. This tutorial seeks to introduce the basic knowledge about lipidomics and to provide readers with some core ideas and the most important approaches for studying the field.

Dataset of differential gene expression between total normal human thyroid and histologically normal thyroid adjacent to papillary thyroid carcinoma

This article contains further data and information from our published manuscript [1]. We aim to identify significant transcriptome alterations of total normal human thyroid vs. histologically normal thyroid adjacent to papillary thyroid carcinoma. We performed a systematic meta-analysis of all the available gene expression profiles for the whole organ also collecting gene expression data for the normal thyroid adjacent to papillary thyroid carcinoma. A differential quantitative transcriptome reference map was generated by using TRAM (Transcriptome Mapper) software able to combine, normalize and integrate a total of 35 datasets from total normal thyroid and 40 datasets from histologically normal thyroid adjacent to papillary thyroid carcinoma from different sources. This analysis identified genes and genome segments that significantly discriminated the two groups of samples. Differentially expressed genes were grouped and enrichment function analyses were performed identifying the main features of the differentially expressed genes between total normal thyroid and histologically normal thyroid adjacent to papillary thyroid carcinoma. The search for housekeeping genes retrieved 414 loci.

A distinct serum metabolic signature of distant metastatic papillary thyroid carcinoma

BACKGROUND

Although the incidence rate for thyroid cancer seems to have begun stabilizing in recent years, an increased rate of advanced stage of this disease has been reported. Additionally, distant metastasis is one of the most important prognostic factors of patients with papillary thyroid carcinoma (PTC). Unfortunately, the underlying mechanisms of distant metastasis, as well as cell status like metabolism changes in distant metastatic tumours have not been clearly elucidated.

OBJECTIVE

To identify serum metabolic signature of distant metastatic PTC.

DESIGN, PATIENTS AND MEASUREMENTS

In this study, gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) was used to analyse the serum from 77 patients diagnosed with PTC (37 in distant metastasis group and 40 in ablation group). Principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) scores plots were used to analyse the data.

RESULTS

Principal component analysis and OPLS-DA analyses demonstrated an evident trend of separation between 40 serum samples from the ablation group and 37 samples from distant metastasis group. A total of 31 metabolites were identified, which are related to amino acid, lipid, glucose, vitamin metabolism and diet/gut microbiota interaction. Pathway analysis showed "alanine, aspartate and glutamate metabolism" and "inositol phosphate metabolism" were the most relevant pathways.

CONCLUSION

Serum metabolomics profiling could significantly discriminate papillary thyroid cancer patients according to distant metastasis. Potential metabolic aberration in distant metastatic PTC could be involved in different biological behaviours of tumour cells including proliferation, invasion/migration and immune escape. Diet/gut microbiota-produced metabolites could play an important role in these effects. This work may provide new clues to find the underlying mechanisms regarding the distant metastasis of PTC as well as potential adjuvant therapy targets.