Diagnosis of papillary thyroid carcinoma by 1H NMR spectroscopy-based metabolomic analysis of whole blood

Plasma metabolites analysis of patients with papillary thyroid cancer: A preliminary untargeted 1H NMR-based metabolomics

Metabolomics plays a crucial role in identifying molecular biomarkers that can differentiate pathological conditions. In the case of thyroid cancer, it is essential to accurately diagnose malignancy from benignity to avoid unnecessary surgeries. The objective of this research was to apply untargeted NMR-based metabolomics in order to identify metabolic biomarkers that can distinguish between plasma samples of patients with papillary thyroid cancer (PTC) and multinodular goiter (MNG), as well as PTC and healthy individuals. The study included a cohort of 55 patients who were divided into three groups: PTC (n=20), MNG (n=16), and healthy (n=19). Plasma samples were collected from all participants and subjected to 1H NMR spectroscopy. Differential metabolites were identified using chemometric pattern recognition algorithms. The obtained metabolic profile had the potential to differentiate PTC from healthy plasma, but not from MNG. In patients diagnosed with PTC, a total of 18 compounds were discovered, revealing elevated levels of leucine, lysine, and 4-acetamidobutyric acid, while acetate, proline, acetoacetate, 3-hydroxybutyrate, glutamate, pyruvate, cystine, glutathione, asparagine, ethanolamine, histidine, tyrosine, myo-inositol, and glycerol along with a lipid compound were found to be lower in comparison to those of healthy individuals. According to the area under the curve (AUC) of the receiver operating characteristic curve, this particular profile exhibited an impressive capability of 85% to discern PTC from healthy subjects (AUC=0.853, sensitivity=78.95, specificity=84.21). The utilization of the 1H NMR-based metabolomics approach revealed considerable promise in the identification of PTC from healthy plasma specimens. The modifications noticed in the plasma metabolites have the potential to act as practical biomarkers that are non-invasive and could suggest transformations in the metabolic profile of thyroid tumors.

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics

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

Alterations in the amino acid profile in patients with papillary thyroid carcinoma with and without Hashimoto's thyroiditis

Purpose

Amino acids (AAs) play important physiological roles in living cells. Some amino acid changes in blood are specific for autoimmune disorders, and some are specific for thyroid cancer. The aims of this study were to profile AA metabolites in the serum of patients with papillary thyroid carcinoma (PTC0) without Hashimoto's thyroiditis (HT) and patients with PTC with HT (PTC1) and predict whether AA metabolites are associated with thyroid disease, thyroid hormone and thyroid autoantibodies.

Methods

A total of 95 serum samples were collected, including 28 healthy controls (HCs), 28 PTC0 patients and 39 PTC1 patients. Serum samples were analyzed by high-performance liquid chromatography-triple stage quadrupole-mass spectrometry (HPLC-TSQ-MS), and twenty-one amino acids (AAs) were detected.

Results

The serum concentration of glutamic acid was significantly elevated in PTC1 patients compared with PTC0 patients. Lysine was the second amino acid that differentiated these two groups of PTC patients. In addition, the serum concentrations of glycine, alanine and tyrosine were significantly reduced in both PTC patient groups compared to the HC group. These AAs were also correlated with thyroid hormones and antibodies. Five amino acid markers, namely, glycine, tyrosine, glutamic acid, glutamine and arginine, separated/distinguished PTC0 patients from healthy subjects, and eight AA markers, the same AAs as above without arginine but with alanine, leucine, valine and histidine, separated/distinguished PTC1 patients from healthy subjects based on ROC analysis.

Conclusion

Compared with the HCs, changes in AAs in PTC0 and PTC1 patients showed similar patterns, suggesting the possibility of a common pathophysiological basis, which confirms preliminary research that PTC is significantly associated with pathologically confirmed HT. We found two AAs, lysine and alanine, that can perform diagnostic functions in distinguishing PTC1 from PTC0.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Pilot Study on the Use of Untargeted Metabolomic Fingerprinting of Liquid-Cytology Fluids as a Diagnostic Tool of Malignancy for Thyroid Nodules

Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20-30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich^®) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich^® FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842-1), an AUC of 0.945 (0.833-1), and an F1 score of 0.947 (0.842-1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures.

Cobalt exposure and dyslipidemia in elderly population: the mediating role of systemic inflammation and lipid peroxidation

Heavy metal exposure has been reported to be correlated with lipid profile alteration and dyslipidemia. While the associations between serum cobalt (Co) with lipid profile levels and risk of dyslipidemia have not been explored in elderly population, and the underlying mechanisms remain unclear. All eligible 420 elderly people were recruited in three communities of Hefei City in this cross-sectional study. Peripheral blood samples and clinical information were collected. The level of serum Co was detected through ICP-MS. The biomarkers for systemic inflammation (TNF-α) and lipid peroxidation (8-iso-PGF2α) were measured with ELISA. Each 1-unit increase of serum Co was related with 0.513 mmol/L, 0.196 mmol/L, 0.571 mmol/L, and 0.303 g/L in TC, TG, LDL-C, and ApoB, respectively. Multivariate linear and logistic regression analyses indicated that the prevalence of elevated TC, elevated LDL-C, and elevated ApoB were gradually increased according to tertiles of serum Co concentration (all P trend < 0.001). The risk of dyslipidemia was positively correlated with serum Co (OR = 3.500; 95% CI 1.630 ~ 7.517). Moreover, the levels of TNF-α and 8-iso-PGF2α were gradually risen in parallel with elevating serum Co. The elevation of TNF-α and 8-iso-PGF2α partially mediated Co-caused elevation of TC and LDL-C. Environmental Co exposure is associated with elevated lipid profile levels and dyslipidemia risk among elderly population. Systemic inflammation and lipid peroxidation partially mediate the associations of serum Co with dyslipidemia.

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.

Associations among S100A4, Sphingosine-1-Phosphate, and Pulmonary Function in Patients with Chronic Obstructive Pulmonary Disease

Background. S100A4 is a member of the S100 calcium-binding protein family and is increased in patients with chronic obstructive pulmonary disease (COPD). Sphingosine-1-phosphate (S1P) is a naturally occurring bioactive sphingolipid, which regulates the adhesion between the cells and the extracellular matrix and affects cell migration and differentiation. The goal of this study was to analyze the correlations among S100A4, S1P, and pulmonary function among COPD patients. Methods. All 139 serum samples and 15 lung specimens were collected in COPD patients and control subjects. S100A4 and S1P were detected in two groups. The markers of fibrosis and epithelial-mesenchymal transition (EMT) were measured in the lungs of COPD patients and control subjects. Results. The protein expression of S100A4 was higher in the lungs and serum of COPD patients than control cases. Additionally, serum S100A4 was inversely associated with pulmonary function among COPD patients. Meanwhile, collagen deposition and EMT nuclear transcription factors were elevated in the lungs of COPD patients. Moreover, the protein expression of S1P was increased in the serum of COPD patients. Serum S1P was gradually increased along with pulmonary function decline in COPD patients. Further correlation analysis revealed that serum S1P was negatively associated with pulmonary function in COPD patients. Furthermore, there was a positive correlation between S1P and S100A4 in COPD patients. Conclusions. These results provide evidence that the elevation of S100A4 and S1P may be involved in the onset and progression of COPD.

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.

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.