Search Results (625)

Organoid and spheroid technologies have rapidly become pivotal in thyroid cancer research, offering models that are more physiologically relevant than traditional two-dimensional culture. In the study of papillary and anaplastic thyroid carcinomas, two subtypes that differ both histologically and clinically, three-dimensional (3D) models offer unparalleled insights into tumor biology, therapeutic vulnerabilities, and resistance mechanisms. These models maintain essential tumor characteristics such as cellular diversity, spatial structure, and interactions with the microenvironment, making them extremely valuable for disease modeling and drug testing. This review emphasizes recent progress in the development and use of thyroid cancer organoids and spheroids, focusing on their role in replicating disease features, evaluating targeted therapies, and investigating epithelial–mesenchymal transition (EMT), cancer stem cell behavior, and treatment resistance. Patient-derived organoids have shown potential in capturing individualized drug responses, supporting precision oncology strategies for both differentiated and aggressive subtypes. Additionally, new platforms, such as thyroid organoid-on-a-chip systems, provide dynamic, high-fidelity models for functional studies and assessments of endocrine disruption. Despite ongoing challenges, such as standardization, limited inclusion of immune and stromal components, and culture reproducibility, advancements in microfluidics, biomaterials, and machine learning have enhanced the clinical and translational potential of these systems. Organoids and spheroids are expected to become essential in the future of thyroid cancer research, particularly in bridging the gap between laboratory discoveries and patient-focused therapies. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background: Although semaglutide (Ozempic^®) is being prescribed off-label to individuals with obesity, some concerns have arisen regarding its use, particularly regarding the risk of thyroid and pancreatic disorders. Therefore, it is crucial to screen patients’ medical and family disease histories, as well as certain clinical parameters, before initiating this treatment for obesity or weight management. However, there is limited research investigating whether pretreatment assessment is adopted in clinical practice. Method: This is a single-center retrospective study involving adults who were prescribed semaglutide for obesity or weight management. Demographic data, comorbid conditions, semaglutide-related lab work, and disease history assessments, including pancreatitis, thyroid abnormalities, oculopathy, neuropathy, and any family history of thyroid cancer, were evaluated and documented prior to treatment initiation. Results: In total, 715 patients were included in the study, with an average age of 40.2 ± 12.0 years, and 49.5% of participants were male. The average weight and BMI prior to using semaglutide were 99.8 ± 18.1 kg and 36.3 ± 8.3 kg/m², respectively, with predominantly overweight and obese individuals (collectively 91.3%). Approximately 69% of patients had 3–5 complications, with a high prevalence of cardiovascular and metabolic diseases before using semaglutide. Although HbA1c, serum creatinine, TSH, T3, T4, triglycerides, HDL, LDL, total cholesterol, and total bilirubin were monitored prior to semaglutide use, none of the patients’ pancreatic lipase, amylase, or calcitonin levels were measured. Although it is important to investigate all personal and family disease histories, including thyroid abnormalities, thyroid cancer, pancreatitis, retinopathy, eye problems, and neuropathy prior to semaglutide initiation, checks were only conducted in 1.8% of patients, despite 98.6% having at least one of the diseases assessed pretreatment. Conclusions: The current pretreatment assessment approach for patients prescribed semaglutide for weight reduction is underdeveloped, particularly with regard to assessing the influence of disease history on semaglutide use. This predisposes patients to a risk of severe clinical outcomes, including thyroid cancer, pancreatitis, and retinopathy. Full article

Background: Thyroid neoplasms exhibit a diverse molecular landscape, and the 2022 WHO classification emphasizes the critical role of molecular profiling in thyroid cancer management; however, comprehensive mutational data from fine-needle aspiration cytology (FNAC) samples using targeted next-generation sequencing (NGS) are still limited, necessitating further investigation to guide clinical practice. Purpose: To characterize the mutational landscape of thyroid neoplasms using targeted NGS of FNAC samples and to assess the clinical implications of molecular profiling. Materials and Methods: This retrospective study included 952 patients with thyroid carcinomaneoplasms who underwent surgery at Sun Yat-sen Memorial Hospital from 2021 to 2023. Preoperative ultrasound, FNAC, and targeted NGS were performed. NGS panels covering 18, 88, and pan-cancer genes were used to analyze FNAC samples. Molecular alterations were correlated with clinical and pathological features. Results: The most frequent mutation was BRAF^V600E (84.45%), followed by RET (6.41%), BRCA1/2 (4.41%) and RAS (4.41%). Patients were categorized into BRAF-like (830 cases), RAS-like (36 cases), high-risk mutations (25 cases), and other mutations (28 cases). High-risk mutations were associated with older age and larger tumor size. BRAF-like tumors had a higher lymph node metastasis rate (58.77%) compared to RAS-like tumors (33.33%). Tumor mutation burden varied significantly among different thyroid neoplasm subtypes. Conclusions: Molecular profiling using targeted NGS of FNAC samples provides valuable insights into the genetic landscape of thyroid neoplasms and has significant clinical implications for diagnosis and personalized treatment strategies. Further validation with paired tumor and plasma samples is warranted. Full article

Background: The prognosis management of thyroid cancer remains a significant challenge. This study highlights the critical role of T cells in the tumor microenvironment and aims to improve prognostic precision by integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data, providing a more comprehensive view of tumor biology at the single-cell level. Method: 15 thyroid cancer scRNA-seq samples were analyzed from GEO and 489 patients from TCGA. A multi-level attention graph neural network (MLA-GNN) model was applied to integrate T-cell-related differentially expressed genes (DEGs) for predicting disease-free survival (DFS). Patients were divided into training and validation cohorts in an 8:2 ratio. Result: We systematically characterized the immune microenvironment of metastatic thyroid cancer by using single-cell transcriptomics and identified the important role of T-cell subtypes in the development of thyroid cancer. T-cell-based DEGS between tumor tissues and normal tissues were also identified. Subsequently, T-cell-based risk signatures were selected for establishing a risk model using MLA-GNN. Finally, our MLA-GNN-based model demonstrated an excellent ability to predict the DFS of thyroid cancer patients (1-year AUC: 0.965, 3-years AUC: 0.979, and 5-years AUC: 0.949 in training groups, and 1-year AUC: 0.879, 3-years AUC: 0.804, and 5-years AUC: 0.804 in validation groups). Conclusions: Risk features based on T-cell genes have demonstrated the effectiveness in predicting the prognosis of thyroid cancer. By conducting a comprehensive characterization of T-cell features, we aim to enhance our understanding of the tumor’s response to immunotherapy and uncover new strategies for the treatment of cancer. Full article

Gain-of-function gene alterations in rearranged during transfection (RET), a receptor tyrosine kinase, are observed in both sporadic and hereditary medullary thyroid cancers (MTCs) and pheochromocytomas and paragangliomas (PPGLs). Several tyrosine kinase inhibitors (TKIs) that target RET have been proven to be effective on MTCs and PCCs. Recently, TKIs, namely, sunitinib and selpercatinib, which were clinically used to target PPGLs, have been reported to decrease catecholamine levels without reducing tumor size. Our clinical case of metastatic medullary thyroid cancer, which is associated with RET mutations undergoing treatment with vandetanib, also suggests that vandetanib can decrease catecholamine levels. Therefore, we investigated the effect of vandetanib, a representative multi-targeted TKI for RET-related MTC, on cell proliferation and catecholamine synthesis in rat pheochromocytoma PC12 cells. Vandetanib reduced viable cells in a concentration-dependent manner. The dopamine and noradrenaline levels of the cell lysate were reduced in a concentration-dependent manner. They also decreased more prominently at lower concentrations of vandetanib compared to the inhibition of cell proliferation. The RNA knockdown study of Ret revealed that this inhibitory effect on catecholamine synthesis is mainly mediated by the suppression of RET signaling. Next, we focused on two signaling pathways downstream of RET, namely, ERK and AKT signaling. Treatment with vandetanib reduced both ERK and AKT phosphorylation in PC12 cells. Moreover, both an MEK inhibitor U0126 and a PI3K/AKT inhibitor LY294002 suppressed catecholamine synthesis without decreasing viable cells. This study in rat pheochromocytoma PC12 cells reveals the direct inhibitory effects of vandetanib on catecholamine synthesis via the suppression of RET-ERK and RET-AKT signaling. Full article

Background: Lenvatinib is a receptor tyrosine kinase inhibitor indicated for advanced radioiodine-refractory thyroid cancer (RAI-RTC). It is recommended to start at 24 mg per day; however, in patients who are at risk of severe adverse events, it may be reasonable to start at lower doses. Patients and Methods: We included 15 patients with RAI-RTC who started lenvatinib at a very low/low dose and evaluated the efficacy and safety. Results: Eight patients (53.3%) did not show progression of the disease, and about half of the patients (53.3%) were alive at the last follow-up visit. Up to 26.6% of patients achieved a partial response to therapy, with a notable volume reduction in the local and metastatic lesions. However, 80% of patients experienced adverse events, mainly of a moderate grade. Conclusions: Although these findings are based on a small sample size and a single-center study, treatment with lenvatinib at very low/low doses in fragile patients seems to be a promising strategy for the management of RAI-RTC, balancing effective disease control with a favorable safety profile. Full article

Thyroid cancer, the most frequently occurring endocrine neoplasm, comprises a heterogeneous group of histological subtypes, spanning from the indolent papillary thyroid carcinoma (PTC) to the rapidly progressive and lethal anaplastic thyroid carcinoma (ATC). Although conventional therapies, such as surgery and radioactive iodine (RAI), are effective for differentiated thyroid cancers, treatment resistance and poor prognosis remain major challenges in advanced and undifferentiated forms. In current times, growing attention has been directed toward the potential of Natural Killer (NK) cells as a promising immunotherapeutic avenue. These innate immune cells are capable of direct cytotoxicity against tumor cells, but their efficiency is frequently compromised by the immunosuppressive tumor microenvironment (TME), which inhibits NK cell activation, infiltration, and persistence. This review explores the dynamic interaction between NK cells and the TME in thyroid cancer, detailing key mechanisms of immune evasion, including the impact of suppressive cytokines, altered chemokine landscapes, and inhibitory ligand expression. We further discuss latest advancements in NK cell-based immunotherapies, including strategies for ex vivo expansion, genetic modification, and combinatorial approaches with checkpoint inhibitors or cytokines. Additionally, emerging modalities, such as NK cell-derived extracellular vesicles, are addressed. By combining mechanistic insights with advancing therapeutic techniques, this review provides a comprehensive perspective on NK cell-based interventions and their future potential in improving outcomes for patients with thyroid cancer. Full article

Background and Objectives: Thyroid nodules are a common endocrine disorder, with 10–15% exhibiting malignancy. Accurate differentiation of malignant and benign nodules is crucial for optimizing treatment outcomes. Current diagnostic tools, such as the Bethesda classification and fine-needle aspiration biopsy (FNAB), are limited in sensitivity and specificity, particularly in indeterminate cases. Tumor-infiltrating immune cells (TIICs) in the tumor microenvironment (TME) play a significant role in thyroid cancer progression. CD66b⁺ neutrophil-like monocytes constitute a novel subset of myeloid cells that are implicated in the modulation of anti-tumor immune responses, but their role in thyroid cancer remains unclear. Materials and Methods: Peripheral blood and thyroid nodule tissue samples were obtained from 24 patients with papillary thyroid carcinoma, and from 10 patients who underwent surgery for symptoms of tracheal compression due to benign thyroid nodules. Myeloid cell populations were assayed by flow cytometric immunophenotyping with CD45, HLA-DR, CD14, and CD66b. The data were statistically analyzed with the clinical properties of the patients. Results: The neutrophil-like monocytes, which were determined as HLA-DR⁺CD14⁺CD66b⁺ cells, found in the circulation (11.9 ± 2.4% of total mononuclear immune cells) of the patients with papillary thyroid carcinoma, were significantly elevated (p < 0.001). Accordingly, these cells were more frequently detected in tumor tissues (21.1 ± 2.1% of total tumor-infiltrating immune cells) compared to non-tumor thyroid tissues (p = 0.0231). The infiltration levels of neutrophil-like monocytes were significantly higher in malignant nodules as well as in the peripheral blood of the papillary thyroid carcinoma patients compared to the samples obtained from the patients with benign nodules. The tumor tissues exhibited increased immune cell infiltration and harbored CD66b-expressing neutrophil-like HLA-DR⁺CD14⁺ monocytic cells, which indicates an inflammatory milieu in malignant thyroid cancer. Conclusions: This study identifies neutrophil-like monocytes as a potential biomarker for differentiating malignant and benign thyroid nodules. Elevated levels of this novel subtype of immune cells in malignant tissues suggest their role in tumor progression and their utility in enhancing diagnostic accuracy. Incorporating these findings into clinical practice may refine surgical decision-making and improve outcomes through personalized diagnostic and therapeutic strategies, particularly for radioiodine-refractory thyroid cancer. Full article

Anaplastic thyroid carcinoma (ATC) is a highly aggressive malignancy with poor prognosis and limited treatment options. Precision oncology seeks personalized therapies that selectively modulate tumor behavior, which is critical for improving patient outcomes. In this study, we evaluated the therapeutic potential of human dental pulp stem cell-derived extracellular vesicles (hDPSC-EVs) in three ATC cell lines (8505C, HTH83, KTC-2). Fluorescence and confocal microscopy confirmed the efficient, time-dependent internalization of hDPSC-EVs by ATC cells, with increased fluorescence intensity over 48 h. Functional assays revealed the selective inhibition of migration and invasion in a cell line-dependent manner, without affecting cell proliferation, viability, or tumorigenic traits, indicating a non-cytotoxic, context-specific modulation of tumor behavior. After 72 h of EV treatment, targeted qPCR of 92 cancer-related genes showed the strongest response in 8505C cells (24 genes; 16 up, 8 down), moderate changes in KTC-2 (16 genes; 14 up, 2 down), and few alterations in HTH83 (6 genes; 4 up, 2 down). Across all lines, FN1 emerged as a context-dependent target, downregulated in 8505C but upregulated in the other two. No broad pathway enrichment was observed, indicating the fine-tuning of key networks rather than wholesale reprogramming. Despite variations across cell lines, hDPSC-EVs consistently demonstrated no impact on cell proliferation and no evidence of cytotoxicity or tumorigenic behavior, with no adverse outcomes. These findings provide preclinical evidence for hDPSC-EVs as a promising, safe, and targeted therapeutic platform in precision oncology, particularly for aggressive cancers, like ATC, warranting further exploration in preclinical and clinical studies. Full article

Background/Objectives: Differentiated Thyroid Cancer (DTC), comprising papillary and follicular carcinomas, is the most common type of thyroid cancer. This is highly infectious and increasing at a higher rate. Some patients experience recurrence even after undergoing successful treatment. Early signs of recurrence can be hard to identify, and the existing health care system cannot always identify it on time. Therefore, predicting its recurrence accurately and in its early stage is a significant clinical challenge. Numerous advanced technologies, such as machine learning, are being used to overcome this clinical challenge. Thus, this study presents a novel approach for predicting the recurrence of DTC. The key objective is to improve the prediction accuracy through hyperparameter optimization. Methods: In order to achieve this, we have used a metaheuristic algorithm, the whale optimization algorithm (WOA) and its modified version. The modifications that we introduced in the original WOA algorithm are a piecewise linear chaotic map for population initialization and inertia weight. Both of our algorithms optimize the hyperparameters of the Extreme Gradient Boosting (XGBoost) model to increase the overall performance. The proposed algorithms were applied to the dataset collected from the University of California, Irvine (UCI), Machine Learning Repository to predict the chances of recurrence for DTC. This dataset consists of 383 samples with a total of 16 features. Each feature captures the critical medical and demographic information. Results: The model has shown an accuracy of 99% when optimized with WOA and 97% accuracy when optimized with the modified WOA. Conclusions: Furthermore, we have compared our work with other innovative works and validated the performance of our model for the prediction of DTC recurrence. Full article

Spatial transcriptomics (ST) is emerging as a powerful technology that transforms our understanding of thyroid cancer by offering a spatial context of gene expression within the tumor tissue. In this review, we synthesize the recent applications of ST in thyroid cancer research, with a particular focus on the heterogeneity of the tumor microenvironment, tumor evolution, and cellular interactions. Studies have leveraged the spatial information provided by ST to map distinct cell types and expression patterns of genes and pathways across the different regions of thyroid cancer samples. The spatial context also allows a closer examination of invasion and metastasis, especially through the dysregulation at the tumor leading edge. Additionally, signaling pathways are inferred at a more accurate level through the spatial proximity of ligands and receptors. We also discuss the limitations that need to be overcome, including technical limitations like low resolution and sequencing depth, the need for high-quality samples, and complex data handling processes, and suggest future directions for a wider and more efficient application of ST in advancing personalized treatment of thyroid cancer. Full article

Background/Objectives: Given the high incidence and generally favorable prognosis of papillary thyroid microcarcinomas (PTMs), the Porto Proposal aims to refine the management of these tumors. It designates tumors lacking certain risk factors as papillary microtumors (PMTs) to avoid overtreatment and reduce patient stress. The updated Porto Proposal (uPp) suggests criteria for reclassifying incidental PTMs as PMTs. This study seeks to validate these criteria using data from a university hospital in Catalonia, Spain, and assess the clinical and pathological characteristics of PTMs. Methods: This retrospective study analyzed patients diagnosed with PTM (≤1 cm) at a university hospital from 2000 to 2024. The study examined variables, including lymph node positivity, incidental diagnosis, tumor location, histological type, treatment, multifocality, age at diagnosis, tumor size, and survival. The uPp criteria were applied to reclassify PTMs into PMTs or PMCs (true papillary microcarcinomas). Student’s t-test and chi-square tests were used to evaluate the associations between these variables and the uPp classification. Results: The cohort comprised 107 patients, with 77 (72%) women and 30 men. The mean age at diagnosis was 54.5 years. Out of the total, 77 (72%) cases were reclassified as PMTs and 30 (28%) as PMCs according to the uPp criteria. PMC tumors were larger (mean size 4.5 mm vs. 3.3 mm for PMT, p = 0.014) and were significantly associated with multifocality (52.2%; p = 0.004). Most lymph node-positive cases were classified as PMCs (69.2%; p < 0.001) and were multifocal and bilateral more commonly. However, no significant differences in outcomes between PMCs and PMTs were found (p = 0.188). Follicular histology was significantly more common in PMTs (87.0%, p < 0.001) and rarely had lymph node metastases (4.6%; p = 0.047). Conclusions: The updated Porto Proposal (uPp) effectively identifies PTMs with minimal malignant potential, distinguishing between PMT and PMC. The findings support the protocol’s use in reducing unnecessary treatments and psychological stress for patients. The study highlights significant clinical and pathological differences between PTM subtypes, reinforcing the protocol’s applicability in daily pathological practice. Full article

Background/Objective: Radiotherapy for leukemia, the most common childhood malignancy, often exposes patients to radiation, increasing the risk of second malignancies, including thyroid cancer. To assess the incidence and survival outcomes of thyroid cancer after childhood acute lymphoblastic leukemia (ALL). Methods: We systematically reviewed articles reporting the incidence of thyroid cancer in childhood leukemia survivors (age at diagnosis < 18 years) published between 2000–2024 in Science Direct, PubMed, Google Scholar, CENTRAL, and EMBASE. The Newcastle Ottawa Scale was utilized to appraise the methodological quality of the included studies. Descriptive statistics and calculations of incidence were performed using Microsoft Excel. Results: The literature search yielded 1265 articles, of which 18 met the inclusion criteria. Data from 135,861 childhood cancer survivors, among whom 102,070 had a confirmed diagnosis of childhood leukemia, including ALL. The crude incidence of secondary malignancies after childhood leukemia was 10.1 per 1000 patients. Among these, 1.5 per 1000 patients developed second thyroid carcinomas. Overall, 14.6% of the second malignancies in childhood leukemia survivors were thyroid carcinomas, mostly of the papillary type. Survival rates after second thyroid cancer were 100% in all 11/18 studies reporting this outcome. Radiotherapy had been used as part of ALL treatments in 17/18 studies. The use of radiotherapy, female sex, and younger age at the diagnosis of primary ALL emerged as important risk factors for thyroid cancer. Conclusions: Thyroid carcinomas account for ~15% of secondary malignancies after childhood leukemia, with radiation remaining a significant risk factor despite its overall reduced use for the treatment of ALL in the last few decades. Importantly, survival rates remain high. Further research is warranted to determine the incidence and outcomes of thyroid cancer in childhood ALL survivors Full article

This review looks at the causes of the association between thyroid dysfunction (hyperthyroidism and hypothyroidism) and ovarian cancer (OC) risk. Epidemiological data have revealed that thyroid dysfunction, particularly hyperthyroidism, is associated with increased risk, progression, and mortality in patients with OC. In addition, research studies and databases have demonstrated that both the expression and localization of thyroid hormone receptors alpha (TRα) and beta (TRβ) and membrane thyroid hormone receptor integrin alpha V beta 3 (αvβ3) affect OC progression and survival in OC patients. Furthermore, this review described the levels of the thyroid hormones (THs) thyroxine (T₄) and 3,5,3′-triiodo-L-thyronine (T₃) in the blood of OC patients and their role in OC progression. Moreover, we present studies that reported the relationship between hyperthyroidism and hypothyroidism and the levels of metabolic hormones in the blood and the possible effects on metabolic reprogramming in OC cells. We also report data indicating the relationship between the treatment of thyroid dysfunction and OC progression. Finally, the cited case studies described the essential case of struma ovarii, which is OC, including thyroid tissue. This review describes the link between thyroid dysfunction and OC risk and progression, which may be important in treating OC patients with thyroid dysfunction. Full article