Emerging evidence suggests that the essence of life is the ecological balance of the neural, endocrine, metabolic, microbial, and immune systems. Gut microbiota have been implicated as an important factor affecting thyroid homeostasis. This study aims to explore the relationship between gut microbiota and the development of thyroid carcinoma. Stool samples were collected from 90 thyroid carcinoma patients (TCs) and 90 healthy controls (HCs). Microbiota were analyzed using 16S ribosomal RNA gene sequencing. A cross-sectional study of an exploratory cohort of 60 TCs and 60 HCs was conducted. The gut microbiota signature of TCs was established by LEfSe, stepwise logistic regression, lasso regression, and random forest model analysis. An independent cohort of 30 TCs and 30 HCs was used to validate the findings. Functional prediction was achieved using Tax4Fun and PICRUSt2. TC patients were subsequently divided into subgroups to analyze the relationship between microbiota and metastatic lymphadenopathy. In the exploratory cohorts, TCs had reduced richness and diversity of gut microbiota compared to HCs. No significant difference was found between TCs and HCs on the phylum level, though 70% of TCs had increased levels of Proteobacteria-types based on dominant microbiota typing. A prediction model of 10 genera generated with LEfSe analysis and lasso regression distinguished TCs from HCs with areas under the curves of 0.809 and 0.746 in the exploration and validation cohorts respectively. Functional prediction suggested that the microbial changes observed in TCs resulted in a decline in aminoacyl-tRNA biosynthesis, homologous recombination, mismatch repair, DNA replication, and nucleotide excision repair. A four-genus microbial signature was able to distinguish TC patients with metastatic lymphadenopathy from those without metastatic lymphadenopathy. Our study shows that thyroid carcinoma patients demonstrate significant changes in gut microbiota, which will help delineate the relationship between gut microbiota and TC pathogenesis.

Emerging studies have provided a preliminary understanding of the thyroid-gut axis, indicating that intestinal microbiota and its metabolites may act directly or indirectly on the thyroid by influencing intestinal microelements uptake, iodothyronine conversion and storage, and immune regulation, providing new insights into the pathogenesis of thyroid disorders and clinical management strategies. However, the research on gut microbiota and thyroid has only presented the tip of the iceberg. More robust clinical data and basic experiments are still required to elucidate the specific relationships and mechanisms in the future. Here we will characterize the associations between the microbiota and thyroid diseases to evaluate their potential implications in the pathophysiology and open up scientific avenues for future precision studies of the thyroid-gut axis.

Background: Gut microbiota are considered to be intrinsic regulators of thyroid autoimmunity. We designed a cross-sectional study to examine the makeup and metabolic function of microbiota in Graves' disease (GD) patients, with the ultimate aim of offering new perspectives on the diagnosis and treatment of GD. Methods: The 16S ribosomal RNA (rRNA) V3–V4 DNA regions of microbiota were obtained from fecal samples collected from 45 GD patients and 59 controls. Microbial differences between the two groups were subsequently analyzed based on high-throughput sequencing. Results: Compared with controls, GD patients had reduced alpha diversity ( p < 0.05). At the phylum level, GD patients had a significantly lower proportion of Firmicutes ( p = 0.008) and a significantly higher proportion of Bacteroidetes ( p = 0.002) compared with the controls. At the genus level, GD patients had greater numbers of Bacteroides and Lactobacillus , although fewer Blautia , [Eubacterium]_hallii_group , Anaerostipes , Collinsella , Dorea , unclassified_f_Peptostreptococcaceae , and [Ruminococcus]_torques_group than controls (all p < 0.05). Subgroup analysis of GD patients revealed that Lactobacillus may play a key role in the pathogenesis of autoimmune thyroid diseases. Nine distinct genera showed significant correlations with certain thyroid function tests. Functional prediction revealed that Blautia may be an important microbe in certain metabolic pathways that occur in the hyperthyroid state. In addition, linear discriminant analysis (LDA) and effect size (LEfSe) analysis showed that there were significant differences in the levels of 18 genera between GD patients and controls (LDA >3.0, all p < 0.05). A diagnostic model using the top nine genera had an area under the curve of 0.8109 [confidence interval: 0.7274–0.8945]. Conclusions: Intestinal microbiota are different in GD patients. The microbiota we identified offer an alternative noninvasive diagnostic methodology for GD. Microbiota may also play a role in thyroid autoimmunity, and future research is needed to further elucidate the role.

Objective: To develop an approach for automatically analyzing bone metastases (BMs) on bone scintigrams based on deep learning technology. Methods: This research included a bone scan classification model, a regional segmentation model, an assessment model for tumor burden and a diagnostic report generation model. Two hundred eighty patients with BMs and 341 patients with non-BMs were involved. Eighty percent of cases were randomly extracted from two groups as training set. Remaining cases were as testing set. A deep residual convolutional neural network with different structures was used to determine whether metastatic bone lesions existed, regions of lesions were automatically segmented. Bone scan tumor burden index (BSTBI) was calculated; finally, diagnostic report could be automatically generated. The sensitivity, specificity and accuracy of classification model were compared with three physicians with different clinical experience. The Dice coefficient evaluated the effect of segmentation model and compared to the result of nnU-Net model. The correlation between BSTBI and blood alkaline phosphatase (ALP) level was analyzed to verify the efficiency of BSTBI. The performance of report generation model was evaluated by the accuracy of interpretation of report. Results: In testing set, the sensitivity, specificity and accuracy of classification model were 92.59%, 85.51% and 88.62%, respectively. The accuracy showed no statistical difference with moderately and experienced physicians and obviously outperformed the inexperienced. The Dice coefficient of BMs area was 0.7387 in segmentation stage. Based on the whole model frame, our segmentation model outperformed the nnU-Net. BSTBI value changed as the BMs changed. There was a positive correlation between BSTBI and ALP level. The accuracy of report generation model was 78.05%. Conclusion: Deep learning based on automatic analysis frameworks for BMs can accurately identify BMs, preliminarily realize a fully automatic analysis process from raw data to report generation. BSTBI can be used as a quantitative evaluation indicator to assess the effect of therapy on BMs in different patients or in the same patient before and after treatment.

The central role of the microbiota as a pivotal factor regulating anti-tumor immune responses has recently been appreciated. Increasing evidence has put a spotlight on the connection of microbiota to T cells, by showing impaired effector and/or memory responses in germ-free (GF) mice or in the presence of dysbiotic communities, and association with tumor growth and overall survival (OS). These observations also have significant implications for anti-tumor therapy and vaccination, suggesting that the communication between T cells and the microbiota involves soluble mediators (microbiota-derived metabolites) that influence various functions of T cells. In addition, there is growing appreciation of the role of bacterial translocation into the peritumoral milieu from the intestinal tract, as well as of locally developed tumor microbial communities, spatially separated from the gut microbiota, in shaping the tumor microbiome. Collectively, these findings have added new support to the idea that tonic inputs mirroring the existence of tumor microbiome could regulate the function of tumor-infiltrating T cells and tissue-resident memory T (TRM) cells. In this review, we focus on recent advances and aspects of these active areas of investigation and provide a comprehensive overview of the unique mechanisms that play a pivotal role in the regulation of anti-tumor immunity by the microbiota, some of which could be of particular relevance for addressing problems caused by tumor heterogeneity. It is our hope that this review will provide a theoretical foundation for future investigations in this area.

Background: Currently, the high morbidity of individuals with thyroid cancer (TC) is an increasing health care burden worldwide. The aim of our study was to investigate the relationship among the gut microbiota community, metabolites, and the development of differentiated thyroid cancer.Methods: 16S rRNA gene sequencing and an integrated LC–MS-based metabolomics approach were performed to obtain the components and characteristics of fecal microbiota and metabolites from 50 patients with TC and 58 healthy controls (HCs).Results: The diversity and richness of the gut microbiota in the TC patients were markedly decreased. The composition of the gut microbiota was significantly altered, and the Bacteroides enterotype was the dominant enterotype in TC patients. Additionally, the diagnostic validity of the combined model (three genera and eight metabolites) and the metabolite model (six metabolites) were markedly higher than that of the microbial model (seven genera) for distinguishing TC patients from HCs. LEfSe analysis demonstrated that genera (g_Christensenellaceae_R-7_group, g_Eubacterium_coprostanoligenes_group) and metabolites [27-hydroxycholesterol (27HC), cholesterol] closely related to lipid metabolism were greatly reduced in the TC group. In addition, a clinical serum indicator (total cholesterol) and metabolites (27HC and cholesterol) had the strongest influence on the sample distribution. Furthermore, functional pathways related to steroid biosynthesis and lipid digestion were inhibited in the TC group. In the microbiota-metabolite network, 27HC was significantly related to metabolism-related microorganisms (g_Christensenellaceae_R-7_group).Conclusions: Our research explored the characteristics of the gut microecology of patients with TC. The findings of this study will help to discover risk factors that affect the occurrence and development of TC in the intestinal microecology.