Alteration of Trace Elements in Multinodular Goiter, Thyroid Adenoma, and Thyroid Cancer

Serum Trace Element Levels in Cancer Patients Undergoing Chemotherapy: a Before-After Analysis

Trace elements (TEs) play a crucial role in metabolism through their biochemical and catalytic effects, and alterations in their levels have been observed in various malignancies. Given that chemotherapy is a common treatment for cancer, it is important to understand how it may affect the levels of TEs in the body. By investigating changes in TEs levels before and after chemotherapy, this study aims to provide insights into the potential impact of chemotherapy on TEs levels in cancer patients. In the present study, analyses were performed on the serum level of some elements including Zn, Cu, Cd, and Se in 69 patients with leukemia, lymphoma, prostate and breast cancers before and after three courses of chemotherapy. The serum TEs were measured by atomic absorption spectroscopy. The serum Zn levels in patients with leukemia, lymphoma, and breast cancer significantly decreased after chemotherapy (P < 0.05). Significant reductions were also observed in the post-chemotherapy serum level of Cd in patients with prostate (P = 0.020) and breast cancer (P = 0.013). Moreover, the Se serum level significantly decreased after chemotherapy compared to before it in the breast cancer patients (P < 0.001). In contrast, the serum level of Cu was higher before than after chemotherapy in all the patients, but no significant difference was found (P > 0.05). The results show that chemotherapy can alter the level of TEs. The assessment of TEs in cancer patients may provide information about the side effects of chemotherapy as well as the use of appropriate strategies to better manage the clinical conditions of patients.

Zinc and thyroid cancer: A systematic review and meta-analysis protocol

INTRODUCTION

The thyroid cancer has the ninth larger incidence of cancer in the world. Investigations related to the exposure to metals have become important due to the sensibility of the thyroid gland to them. Studies reveal that carcinogenic progressions are associated to the deficiency of the essential trace elements. In this context, the zinc is highlighted, essential for the metabolism of the thyroidal hormone and has a potential relation with the pathogenesis of the thyroid cancer. The objective of this systematic review and meta-analysis is to evaluate the low serum zinc as a risk factor for thyroid cancer in adults.

METHODS AND ANALYSIS

PubMed/MEDLINE, Scopus, Embase and LILACS databases will be searched for observational studies investigating the low serum zinc as a risk factor for thyroid cancer in adults. No language or publication period restrictions will be imposed. The primary outcome will be that the low serum zinc is a risk factor for thyroid cancer. Three independent reviewers will select the studies and extract data from the original publications. The risk-of-bias will be assessed by using the Newcastle-Ottawa Quality Assessment Scale (NOS). Data synthesis will be performed using the R software (V.4.3.1) and to assess heterogeneity, we will compute the I2 statistic and the results will be based on either random-effects or fixed-effects models, depending on the heterogeneity. The Grading of Recommendations, Development, and Evaluation (GRADE) system will be used to evaluate the reliability and quality of evidence.

PROSPERO REGISTRATION NUMBER

International Prospective Register of Systematic Reviews (PROSPERO) CRD42023463747.

Multi-element analysis of metals in human pathological and unchanged thyroid glands - pilot study

Disturbances in the homeostasis of the elemental composition of thyroid tissue may have serious metabolic and health consequences. It is believed that the accumulation of some metals or the deficiency of others may even cause lethal tumours. Due to the fact that metallomics most often uses human serum to analyse macro and microelements as well as trace elements, it was decided to use material that is more difficult to obtain, but also adds credibility to the research - thyroid tissue samples biopsy. The experiments were conducted on 17 patients diagnosed with: nodular (10) and colloidal goitre (2), chronic thyroiditis (2), follicular adenoma (2) and papillary carcinoma (1). They were recruited by collecting a tumour fragment, control fragment and serum from each of them. The content of Ca, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Zn was examined using ICP-OES (Inductively Coupled Plasma - Optical Emission Spectrometers). Simultaneously, biochemical methods were used to determine the markers of inflammation, glycation and peroxidation: malondialdehyde, pentosidine, reactive free amine content, compounds with thiol groups and galectin 3 in the sera of the examined patients. Three statistically significant correlations were identified: Ca-Mg and Cu-Zn in control tissues (p < 0.05) and Cr-Mn in pathological tissues (p < 0.05). A comparison of individual groups of patients shows that there are some potentail tendencies to increase or decrease in the concentration of certain elements or markers of inflammation and glycation, therefore we discuss potential relationships between a given parameter and a thyroid disorder. The pilot study is an introduction to a deeper analysis aimed at tracing the pathomechanism of the development of thyroid diseases, so that the risk of developing these diseases can be effectively minimized.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

The value of deep neural networks in the pathological classification of thyroid tumors

BACKGROUND

To explore the distinguishing diagnostic value and clinical application potential of deep neural networks (DNN) for pathological images of thyroid tumors.

METHODS

A total of 799 pathological thyroid images of 559 patients with thyroid tumors were retrospectively analyzed. The pathological types included papillary thyroid carcinoma (PTC), medullary thyroid carcinoma (MTC), follicular thyroid carcinoma (FTC), adenomatous goiter, adenoma, and normal thyroid gland. The dataset was divided into a training set and a test set. Resnet50, Resnext50, EfficientNet, and Densenet121 were trained using the training set data and tested with the test set data to determine the diagnostic efficiency of different pathology types and to further analyze the causes of misdiagnosis.

RESULTS

The recall, precision, negative predictive value (NPV), accuracy, specificity, and F1 scores of the four models ranged from 33.33% to 100.00%. The area under curve (AUC) ranged from 0.822 to 0.994, and the Kappa coefficient ranged from 0.7508 to 0.7713. However, the performance of diagnosing FTC, adenoma, and adenomatous goiter was slightly inferior to other types of pathological tissues.

CONCLUSION

The DNN model achieved satisfactory results in the task of classifying thyroid tumors by learning thyroid pathology images. These results indicate the potential of the DNN model for the efficient diagnosis of thyroid tumor histopathology.

The Role of Selected Trace Elements in Oxidoreductive Homeostasis in Patients with Thyroid Diseases

Impaired levels of selenium (Se), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and iodine (I) in the organism may adversely affect the thyroid endocrine system. These trace elements play a role in the fight against oxidative stress as components of enzymes. Oxidative-antioxidant imbalance is considered a possible factor in many pathological conditions, including various thyroid diseases. In the available literature, there are few scientific studies showing a direct correlation of the effect of supplementation of trace elements on slowing down or preventing the occurrence of thyroid diseases in combination with the improvement of the antioxidant profile, or through the action of these elements as antioxidants. Among the available studies, it has been shown that an increase in lipid peroxidation levels and a decrease in the overall antioxidant defense status occur during such thyroid diseases as thyroid cancer, Hashimoto's thyroiditis and dysthyroidism. In studies in which trace elements were supplemented, the following were observed: a decrease in the level of malondialdehyde after supplementation with Zn during hypothyroidism and reduction in the malondialdehyde level after Se supplementation with a simultaneous increase in the total activity status and activity of antioxidant defense enzymes in the course of autoimmune thyroiditis. This systematic review aimed to present the current state of knowledge about the relationship between trace elements and thyroid diseases in terms of oxidoreductive homeostasis.

Associations of trace element levels in paired serum, whole blood, and tissue: an example of esophageal squamous cell carcinoma

Previous studies have extensively explored impacts of trace elements on human beings and complex relationships with cancers. However, contradictory conclusions may be more challenging to explain due to biological specimen differences. To investigate the distribution of trace elements inside body, we collected serum, whole blood and tissues from 77 patients with esophageal squamous cell carcinoma (ESCC), as well as serum and whole blood from 100 healthy individuals, and determined the concentrations of 13 elements (Al, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Sr, Cd, and Pb) with inductively coupled plasma-mass spectrometry (ICP-MS). Al, Ni, Cu, Sr, and Cd variations between patients and controls were found to be inconsistent in serum and blood. Concentrations of Cu, As, Se, and Sr in serum were positively correlated with that in whole blood in both case and control group (r_s >0.450, P <0.01). Elements in serum had a higher accuracy (87.0%) than whole blood (74.0%) in classifying ESCC patients and healthy individuals with discriminant analysis. As, Cd, and Pb concentrations in cancerous tissues were positively correlated with those in normal epithelium (r_s =0.397, 0.571, and 0.542, respectively), while Mn, Cu, and Se accumulated in malignant tissues, with V, Cr, Co, Ni, Sr, and Cd partitioning in normal epithelium (all P <0.05). Thus, certain elements in blood, such as Cu, As, Se, and Sr, were useful in assessing element exposure burdens and accumulation tendency of some elements (Mn, Cu and Se, etc.) was uncovered in tumors. Our investigation demonstrated the variations in trace element distribution for frequently used specimens and further evidence of etiological mechanism is necessary.

Relationship between Serum Levels of Selenium and Thyroid Cancer: A Systematic Review and Meta-Analysis

Thyroid cancer is one of the most malignant tumors and a serious threat to human health. Selenium (Se) is an essential trace element that is critical for thyroid function. Since the relationship between Se and thyroid cancer remains unclear, a meta-analysis was performed to clarify the relationship. A total of five databases (PubMed, Web of Science, Scopus, Embase and Cochrane library) were searched for case-control studies and cohort studies on serum levels of Se and thyroid cancer published up to 13 July 2022. Seven articles consisting of 10 case-control studies and comprised of 2,205 subjects met the inclusion criteria for meta-analysis. From the 10 selected studies, pooled analysis indicated that thyroid cancer patients had lower serum levels of Se than healthy controls [standardized mean difference = -1.25, 95% confidence interval = (-2.07, -0.44), P = 0.003]. Our meta-analysis supports a significant relationship between serum levels of Se and thyroid cancer.

[Elimination of iodine deficiency is a concern for the health of the nation. An excursion into the history, scientific aspects and the current state of the legal regulation of the problem in Russia]

The article presents current data on the prevalence of thyroid diseases associated with iodine deficiency in Russia, focuses on the features of the comorbidity of iodine deficiency and autoimmune thyroid pathologies, methods for assessing the iodine supply of the population. Information about the study and prevention of iodine deficiency diseases (IDD) in the USSR and the Russian Federation is given. The history of legislative initiatives aimed at eliminating dietary iodine deficiency and preventing IDD is illustrated in detail. The ways of solving the problem of iodine deficiency at the present stage, both at the federal and regional levels, are proposed.

Recent advances in the application of metallomics in diagnosis and prognosis of human cancer

Metals play a critical role in human health and diseases. In recent years, metallomics has been introduced and extensively applied to investigate the distribution, regulation, function, and crosstalk of metal(loid) ions in various physiological and pathological processes. Based on high-throughput multielemental analytical techniques and bioinformatics methods, it is possible to elucidate the correlation between the metabolism and homeostasis of diverse metals and complex diseases, in particular for cancer. This review aims to provide an overview of recent progress made in the application of metallomics in cancer research. We mainly focuses on the studies about metallomic profiling of different human biological samples for several major types of cancer, which reveal distinct and dynamic patterns of metal ion contents and the potential benefits of using such information in the detection and prognosis of these malignancies. Elevated levels of copper appear to be a significant risk factor for various cancers, and each type of cancer has a unique distribution of metals in biofluids, hair/nails, and tumor-affected tissues. Furthermore, associations between genetic variations in representative metalloprotein genes and cancer susceptibility have also been demonstrated. Overall, metallomics not only offers a better understanding of the relationship between metal dyshomeostasis and the development of cancer but also facilitates the discovery of new diagnostic and prognostic markers for cancer translational medicine.

The role of heavy metals in thyroid cancer: A meta-analysis

INTRODUCTION

Metal ions are known to accumulate in the thyroid and some play an important role in the function and homeostatic mechanisms of the thyroid gland. Certain metal ions are known endocrine disruptors while others are classified to be carcinogenic. Although higher thyroid cancer incidence rates have been reported in regions with high metal levels in soil and drinking water, including volcanic regions, the effect of heavy metals on the thyroid is still poorly understood. To investigate the association between heavy metals and thyroid cancer, a comprehensive meta-analysis was performed to draw a more evidence-based conclusion for individual metal ions.

MATERIAL AND METHODS

Nineteen studies were included in this meta-analysis, of which 9 studies reported blood metal ion levels, 8 studies reported tissue metal ion levels and 2 studies reported blood and tissue metal ion levels. The standardized mean difference (SMD) was calculated between thyroid cancer patients group and a control group (benign thyroid patients group or healthy controls group) per study.

RESULTS

A significant positive SMD in manganese tissue levels between thyroid cancer patients and benign thyroid patients (SMD: 0.56 (95 % CI: 0.16, 0.95)) and a significant negative SMD in cobalt blood levels between thyroid cancer patients and healthy controls (SMD: -2.03 (95 % CI: -3.95, -0.10)) was found. No difference in levels of other metals in blood or thyroid tissue between thyroid cancer patients and non-thyroid cancer patients was noted.

DISCUSSION AND CONCLUSION

The present meta-analysis therefore demonstrates the urgent need for future studies, especially given the increasing exposure of the general population to various environmental pollutants, including metal ions, and the thyroid cancer burden worldwide.

Associations of exposure to multiple trace elements with the risk of goiter: A case-control study

Goiter is one of common endocrine diseases, and its etiology has not been fully elucidated. The changes in trace elements' levels have an important impact on the thyroid. We designed a case-control study, which involved 383 goiter cases and 383 matched controls. We measured these elements in the urine of participants by inductively coupled plasma optical emission spectrometry (ICP-OES), graphite furnace atomic absorption spectrometry (GFAAS) and As³⁺-Ce⁴⁺ catalytic spectrophotometry. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the elements into multi-element models, conditional logistic regression models were applied to analyze the association between elements and goiter risk. Bayesian kernel machine regression (BKMR) model was used to depict elements' mixtures and evaluate their joint effects. Finally, 7 elements were included in the multi-element model. We found that the concentrations of lithium (Li), strontium (Sr) and barium (Ba) had a negative effect with goiter risk, and lead (Pb) and iodine (I) showed an extreme positive effect. Additionally, compared with the lowest levels, patients with highest quartiles of I and Pb were 6.49 and 1.94 times more likely to have goiter, respectively. On the contrary, in its second and third quartiles, arsenic (As) showed a negative effect (both OR<1). BKMR model showed a certain interaction among Pb, As, Sr and Li on goiter risk. Further large sample studies are needed to confirm these findings in the future.