Dataset Information

Alterations of thyroid microbiota across different thyroid microhabitats in patients with thyroid carcinoma.

ABSTRACT:

Background

In recent years, the incidence rate of Thyroid carcinoma (TC) has been increasing worldwide. Thus, research on factors of TC carcinogenesis may promote TC prevention and decrease the incidence rate. There are several studies targeting the correlation between gut microbiota and thyroid disease. Carcinogenesis of several malignancies is influenced by microbiota. However, thyroid microbiome of TC has not been revealed. This study investigated thyroid microbiota in different TC microhabitats.

Methods

We performed 16s rRNA gene sequencing using tumor tissues and matched peritumor tissues from 30 patients with TC to characterize thyroid microbiota.

Results

The richness and diversity of thyroid microbiota were lower in TC tumor samples than in matched peritumor tissues. At the genus level, the core microbiota of thyroid included Sphingomonas, Comamonas, Acinetobacter, Pseudomonas, Microvirgula, and Soonwooa. The abundance of Sphingomonas and Aeromonas was significantly increased in tumor tissues, while the abundance of Comamonas, Acinetobacter, and Peptostreptococcus was significantly enhanced in peritumor tissues. The combination of Comamonas and Sphingomonas could discriminate tumor samples from peritumor samples with an area under the curve (AUC) of 0.981 (95% confidence interval [CI] 0.949-1.000). The abundance of Sphingomonas was significantly higher in N1 stage than in N0 stage. Sphingomonas could distinguish between N0 and N1 stage with an AUC of 0.964 (95% CI 0.907-1.000).

Conclusions

The microbial diversity and composition were significantly different between peritumor and tumor microhabitats from patients with TC, which may eventually affect TC carcinogenesis and progression. The combination of Comamonas and Sphingomonas could serve as a powerful biomarker for discrimination between tumor and peritumor tissues. Furthermore, the higher abundance of Sphingomonas was correlated with lymph node metastasis, indicating that the abundance of Sphingomonas may indicate a poor prognosis for TC patients, and Sphingomonas may play a role in promoting TC progression.

SUBMITTER: Dai D

PROVIDER: S-EPMC8638380 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundRadiomics strategies exhibit great promise in the context of thyroid nodule diagnosis. This study aimed to compare radiomics features of different sizes of medullary thyroid carcinoma (MTC) and papillary thyroid carcinoma (PTC) tumors and to compare the efficiency of radiomics approaches as a means of differentiating between these tumor types.MethodsIn total, 86 MTC and 330 PTC nodules were divided into the macronodular (>10 mm) and micronodular (⩽10 mm) categories. The radiomics features of these nodules were analyzed to identify independent prognosis factors and evaluate the efficacy of individual and combined indicators as predictors of tumor type.ResultsIn total, 12 radiomics features were found to differ significantly between MTC and PTC macronodules, while 6 differed significantly between MTC and PTC micronodules. Shape 2D_Sphericity, firstorder_Skewness, glrlm_RunLengthNonUniformity, glszm_GrayLevelNonUniformity, and glszm_SizeZoneNonUniformity were features that were independently associated with the differential diagnoses of MTC and PTC macronodules. Receiver operating characteristic (ROC) curve analyses of the efficacy of these 5 single indicators and a combined indicator composed thereof yielded area under the curve (AUC) values of 0.621, 0.678, 0.704, 0.762, 0.747, and 0.824, respectively, with respective sensitivities of 55.3%, 43.0%, 53.1%, 56.3%, 46.9%, and 65.6%, and respective specificity values of 65.6%, 89.1%, 81.6%, 88.8%, 95.0%, and 91.1%. The glrlm_RunEntropy and glszm_SizeZoneNonUniformity features were identified as independent factors associated with the differential diagnoses of MTC and PTC micronodules. Receiver operating characteristic curve analyses of the efficacy of these 2 single indicators and a combined indicator composed thereof yielded respective AUC values of 0.678, 0.678, and 0.771; Sensitivities of 57.0%, 72.7%, and 72.7%; and specificities of 77.3%, 64.2%, and 77.5%.ConclusionsA range of different radiomics features can enable effective differentiation between MTC and PTC nodules of different sizes. Moreover, analyses of combinations of radiomics features yielded diagnostic efficiency values higher than those associated with single radiomics features, highlighting a more reliable approach to diagnosing MTC and PTC tumors.

Project description: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 93 serum samples were collected, including 28 healthy controls (HCs), 28 PTC0 patients and 37 PTC1 patients. Serum samples were analysed by high-performance liquid chromatography-triple stage quadrupole-mass spectrometry (HPLC-TSQ-MS), and 21 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.