Study of Essential and Toxic Metal Imbalances in the Scalp Hair of Thyroid Cancer Patients in Comparison with Healthy Donors

The association between trace metals in both cancerous and non-cancerous tissues with the risk of liver and gastric cancer progression in northwest China

Liver cancer and gastric cancer have extremely high morbidity and mortality rates worldwide. It is well known that an increase or decrease in trace metals may be associated with the formation and development of a variety of diseases, including cancer. Therefore, this study aimed to evaluate the contents of aluminium (Al), arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), selenium (Se), and zinc (Zn) in cancerous liver and gastric tissues, compared to adjacent healthy tissues, and to investigate the relationship between trace metals and cancer progression. During surgery, multiple samples were taken from the cancerous and adjacent healthy tissues of patients with liver and gastric cancer, and trace metal levels within these samples were analysed using inductively coupled plasma mass spectrometry (ICP-MS). We found that concentrations of As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, and Zn in tissues from patients with liver cancer were significantly lower than those in healthy controls (P < 0.05). Similarly, patients with gastric cancer also showed lower levels of Cd, Co, Cr, Mn, Ni, and Zn-but higher levels of Cu and Se-compared to the controls (P < 0.05). In addition, patients with liver and gastric cancers who had poorly differentiated tumours and positive lymph node metastases showed lower levels of trace metals (P < 0.05), although no significant changes in their concentrations were observed to correlate with sex, age, or body mass index (BMI). Logistic regression, principal component analysis (PCA), Bayesian kernel regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g computing (qgcomp) models were used to analyse the relationships between trace metal concentrations in liver and gastric cancer tissues and the progression of these cancers. We found that single or mixed trace metal levels were negatively associated with poor differentiation and lymph node metastasis in both liver and gastric cancer, and the posterior inclusion probability (PIP) of each metal showed that Cd contributed the most to poor differentiation and lymph node metastasis in both liver and gastric cancer (all PIP = 1.000). These data help to clarify the relationship between changes in trace metal levels in cancerous liver and gastric tissues and the progression of these cancers. Further research is warranted, however, to fully elucidate the mechanisms and causations underlying these findings.

Association between Heavy Metals and Trace Elements in Cancerous and Non-cancerous Tissues with the Risk of Colorectal Cancer Progression in Northwest China

Alterations in heavy metals and trace element levels may be associated with various cancers. However, the role of this interaction in colorectal cancer (CRC) progression is unclear. In recent years, Principal Component Analysis (PCA) and Bayesian Kernel Machine Regression (BKMR) models have provided new ideas for analyzing the effects of metal mixtures on CRC progression. Herein, we assessed the differences in the levels of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), selenium (Se), and zinc (Zn) in tumors and adjacent healthy tissues, to investigate the relationship between heavy metals/trace elements and CRC progression. Surgical samples of CRC and noncancerous tissues were collected, and trace metal levels were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Logistic regression, PCA, and BKMR models were used to investigate the relationship between heavy metals and trace elements and the degree of tumor differentiation and lymph node metastasis in CRC. Cancer tissues showed lower As, Cd, Co, and Cr concentrations, and higher Se concentrations than healthy tissues (P < 0.05). In addition, CRC patients with poorly differentiated tumors and/or positive lymph node metastases had lower levels of Cd, Zn, Cu, and Se (P < 0.05). Logistic regression showed that single metal concentration was negatively correlated with CRC progression. PCA and BKMR models also showed that the metal mixture concentration was negatively correlated with CRC progression, with Cd contributing the most. Overall, changes in heavy metal and trace element levels may be related to the development of CRC; however, further mechanistic studies are required.

The Diagnostic Value of Trace Metal Concentrations in Hair in Carotid Artery Disease

Several studies showed the role of trace elements in the increase in human susceptibility to cardiovascular diseases. Carotid artery stenosis is a leading cause of ischemic neurological events. We aimed to analyze the potential role of trace elements in hair as biomarkers of atherosclerotic carotid artery disease. Materials and Methods: Fifty-seven (n = 31 (54%) men and n = 26 (46%) women) individuals with a mean age of 67.7 ± 7.7 years who were white, European, non-Hispanic, and non-Latino were diagnosed and treated in hypertensiology/internal medicine and surgical departments over three consecutive months. Of these patients, forty were diagnosed with advanced carotid artery disease, and seventeen comprised a group of healthy controls. Inflammatory and oncological diseases were exclusion criteria. Hair samples were collected, and 14 trace elements were analyzed. Clinical and laboratory data were compared and revealed differences in the co-existence of diabetes (p = 0.036) and smoking history (p = 0.041). In the multivariable analysis, zinc, chrome, and copper revealed predictive value for the occurrence of carotid artery disease, and their combined receiver operating curve showed area under the curve of 0.935, with a sensitivity of 95% and a specificity of 82.4%. Conclusion: Our report shows the significance of trace elements analyses in patients with advanced carotid artery disease. We revealed that zinc, copper, and chrome concentrations are of particular importance in differentiating atherosclerotic disease and may serve as biomarkers of carotid atherosclerosis. Hair samples represent an easily obtained and beneficial biomatrix for the assessment of biomarkers.

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.

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.